JP6367507B1 - Grout filling status discrimination system and grout filling status discrimination program - Google Patents

Abstract

By analyzing nondestructive inspection data by artificial intelligence, it is possible to determine the filling state of the grout with high accuracy.
In determining the state of grout filling of a cable sheath 71 of a PC structure by nondestructive inspection, past waveform numerical data obtained by nondestructive inspection of the cable sheath and the past waveform image are obtained. Waveform numerical data obtained by performing a non-destructive inspection on the cable sheath that newly determines the grout filling status is acquired and acquired in advance by acquiring three or more levels of association with the result of the grout filling status discrimination. With reference to the degree of association, the grout filling state in the cable sheath is determined based on the input waveform numerical data.
[Selection] Figure 3

Description

  The present invention relates to a grout filling state determination system and a grout filling state determination program for determining a grout filling state in a cable sheath of a PC structure by nondestructive inspection.

  Conventionally, when grout is injected into a cable sheath of a PC structure such as a post-tension type bridge, viaduct or building, a pump is connected to the grout injection side of the cable sheath and a vacuum pump is connected to the discharge side. . After the pressure inside the cable sheath is reduced by the vacuum pump, the pressure pump is operated while the vacuum pump is operated to fill the cable sheath with grout.

  By the way, in recent years, corrosion and breakage accidents of tendons due to poor filling of the grout into the cable sheath in the PC structure have come to be seen. For this reason, conventionally, the grout filling state in the cable sheath is determined by nondestructive inspection. As this nondestructive inspection method, for example, an X-ray transmission method, a shock elastic wave method, an impact echo method, a broadband ultrasonic method, or the like is used.

JP 2000-206098 A

  However, even if inspection data can be obtained by these nondestructive inspection methods, it is not easy to accurately determine the grout filling state in the cable sheath based on the inspection data. In fact, it takes considerable skill to predict the grout filling status from the inspection data only to a certain degree of accuracy. There was also.

  For this reason, there has been a demand for a technique that can automatically perform accurate determination without accumulating skill in determining the grout filling state. A technical idea for improving accuracy by automatically determining the grout filling state by artificial intelligence is also conceivable.

  In particular, Patent Document 1 receives an ultrasonic reflection echo from a building structure, acquires shape information of a flaw in a pillar, etc., and information on the shape of the flaw and an inspection reference database obtained by artificial intelligence or the like. A technique for performing comparison and evaluating the degree of maintenance of a building is disclosed.

  However, the technique disclosed in Patent Document 1 does not specifically disclose a technique for discriminating the state of grout filling in the cable sheath of the PC structure by analyzing nondestructive inspection data using artificial intelligence.

  Therefore, the present invention has been devised in view of the above-mentioned problems, and the object of the present invention is to grout for determining the grout filling state in the cable sheath of the PC structure by nondestructive inspection. In the filling status discrimination system and the grout filling status discrimination program, the grout filling status discrimination system and the grout filling that can determine the filling status of the grout with high accuracy by analyzing nondestructive inspection data by artificial intelligence. To provide a status determination program.

The grout filling status discrimination system according to the present invention is a grout filling status discrimination system for discriminating the grout filling status of a PC structure in a cable sheath by nondestructive inspection. And a database in which three or more levels of correlation between the waveform numerical data of the past and the past waveform numerical data are determined, and the cable sheath that newly determines the grout filling status is not stored. The input means for inputting the waveform numerical data obtained by performing the destructive inspection, and the relevance stored in the database, and based on the waveform numerical data input through the input means, the cable and a discriminating means for discriminating the grout filling status to the sheath, said database is further Three or more levels of association between a combination of past incidental information on the cable sheath and the past numerical waveform data and a result of determining the grout filling status for the combination are stored in advance, and the input means Additional information related to the cable sheath for determining the filling status is further input. Contains one or more of material information, grout information on grout, concrete information placed in PC structure, test condition information for nondestructive inspection, surface condition information of PC structure, and configuration information in PC structure It is characterized by that.

The grout filling status determination program according to the present invention is a grout filling status determination program for determining the grout filling status of a PC structure in a cable sheath by nondestructive inspection. An association degree acquisition step for obtaining in advance three or more levels of correlation between the waveform numerical data of the waveform and the grout filling status determination result for the past waveform numerical data, and a cable sheath for newly determining the grout filling status An input step in which waveform numerical data obtained by performing nondestructive inspection is input, and the association degree acquired in the association degree acquisition step is referred to, and based on the waveform numerical data input through the input step And a determination step for determining the grout filling state in the cable sheath. Ta to be executed, in the association degree obtaining step, further the combination of the past supplementary information and the historical waveform values related cable sheath, three or more stages of association degree between discrimination result of the filling status of the grout for the combination In the input step, additional information related to the cable sheath for newly determining the grout filling status is further input, and the past additional information and the additional information include the configuration information of the cable sheath, the arrangement information of the cable sheath. , Information on tendons related to tendons provided in the PC structure, grout information on the grout, concrete information placed in the PC structure, non-destructive test condition information, PC structure surface condition information, PC It includes any one or more pieces of configuration information in the structure .

  According to the present invention having the above-described configuration, it is possible to easily grasp the grout filling state without requiring special skill. According to the present invention, it is possible to grasp the grout filling state with higher accuracy. Furthermore, by configuring the above-mentioned association degree with artificial intelligence, it is possible to further improve the discrimination accuracy by learning this.

It is a block diagram which shows the whole structure of the grout filling condition discrimination system to which this invention is applied. It is a figure which shows the specific structural example of a discrimination device. It is a figure for demonstrating the search concept of the output solution by the grout filling condition discrimination system to which this invention is applied. It is a flowchart which shows the processing operation of a search program. It is a figure which shows the example of the relevance in the case of using the impact elastic wave method as a nondestructive inspection method. It is a figure which shows the 1st example of the relevance of the combination in the case of using the impact elastic wave method as a nondestructive inspection method. It is a figure which shows the 2nd example of the relevance of the combination in the case of using the impact elastic wave method as a nondestructive inspection method. It is a figure which shows the 3rd example of the combination relevance in the case of using the impact elastic wave method as a nondestructive inspection method. It is a figure which shows the 4th example of the relevance of the combination in the case of using the impact elastic wave method as a nondestructive inspection method. It is a figure which shows the example of an association degree in the case of using an impact echo method as a nondestructive inspection method. It is a figure which shows the 1st example of the relevance of the combination in the case of using an impact echo method as a nondestructive inspection method. It is a figure which shows the 2nd example of the relevance of the combination in the case of using an impact echo method as a nondestructive inspection method. It is a figure which shows the example of the association degree in the case of utilizing an ultrasonic method as a nondestructive inspection method. It is a figure which shows the 1st example of the relevance of the combination in the case of utilizing an ultrasonic method as a nondestructive inspection method. It is a figure which shows the 2nd example of the relevance of the combination in the case of utilizing an ultrasonic method as a nondestructive inspection method. It is a figure which shows the 1st example of the relevance of the combination in the case of utilizing the impact elastic wave method and the impact echo method as a nondestructive inspection method. It is a figure which shows the 2nd example of the relevance of the combination in the case of using the impact elastic wave method and the impact echo method as a nondestructive inspection method. It is a figure which shows the 3rd example of the relevance of the combination in the case of using the impact elastic wave method and the impact echo method as a nondestructive inspection method. It is a figure which shows the 4th example of the relevance of the combination in the case of using the impact elastic wave method and the impact echo method as a nondestructive inspection method.

  Hereinafter, a grout filling situation determination system to which the present invention is applied will be described in detail with reference to the drawings.

  FIG. 1 is a block diagram showing the overall configuration of a grout filling status determination system 1 to which the present invention is applied. The grout filling status discrimination system 1 discriminates the grout filling status of the PC structure 7 into the cable sheath 71 by nondestructive inspection. The grout filling status determination system 1 includes a non-destructive inspection unit 8, an evaluation device 9 connected to the non-destructive inspection unit 8, a determination device 2 connected to the evaluation device 9, and a database 3 connected to the determination device 2. And.

  The PC structure 7 is a bridge, a viaduct, a building or the like in which a PC steel material and a cable sheath 71 are disposed.

  The cable sheath 71 is arranged such that a PC steel rod (not shown) such as a PC steel rod or a number of PC steel wires is in a tension state and is separated from the inner wall surface of the cable sheath 71. Incidentally, since the present embodiment is intended for the post-tension type PC structure 7, in this case, the cable sheath 71 is placed in the PC structure 7 and then concrete is filled and cured, and thereafter A PC steel material (not shown) is inserted into the cable sheath 71 to apply a tensile stress. Thereafter, the cable sheath 71 is filled with grout and cured.

  The nondestructive inspection unit 8 detects inspection data for determining the grout filling state in the cable sheath 71 by a nondestructive inspection method such as a shock elastic wave method, an impact echo method, an ultrasonic method, or an X-ray transmission method. To do. The nondestructive inspection unit 8 transmits the detected inspection data to the evaluation device 9.

  The evaluation device 9 is composed of electronic devices such as a PC (personal computer), a smartphone, a tablet terminal, and a wearable terminal. In the evaluation device 9, the inspection data acquired by the nondestructive inspection unit 8 includes at least waveform numerical data, and transmits the acquired inspection data to the determination device 2.

  The evaluation device 9 may transmit the acquired inspection data to the determination device 2 without special analysis. For example, when the waveform numerical data indicating the time and the fluctuation width is acquired as the inspection data, the evaluation device 9 may transmit the waveform numerical data indicating the time and the fluctuation width as it is to the determination device 2 without performing a special analysis. Good.

  Further, the evaluation device 9 may analyze the acquired inspection data and transmit it to the determination device 2. For example, when the numerical waveform data indicating the relationship between time and amplitude is acquired as the inspection data, the evaluation device 9 analyzes the waveform data and converts it into waveform numerical data indicating the frequency, and transmits it to the determination device 2. Also good. The evaluation device 9 analyzes the inspection data including the waveform numerical data, and appropriately processes the data into an optimum one for evaluating the degree of filling of the grout in the cable sheath 71.

  For example, the evaluation device 9 converts the obtained waveform numerical data indicating the relationship between the time and the amplitude to the waveform numerical data indicating the relationship between the frequency and the amplitude by performing FFT (Fast Fourier Transform) transformation. May be. Further, the evaluation device 9 performs a Fourier transform on the acquired waveform numerical data indicating the relationship between time and amplitude, and converts it into waveform numerical data indicating the relationship between time and frequency. May be converted into waveform numerical data indicating the relationship between the time remaining in the specific frequency region and the fluctuation width by performing inverse Fourier transform. Further, the evaluation device 9 further performs Fourier transform on the waveform numerical data indicating the relationship between the time and the amplitude that leaves the specific frequency region, thereby obtaining the frequency and amplitude that leaves the specific frequency region. You may convert into the waveform numerical data which show a relationship. Further, the evaluation device 9 may convert the numerical waveform data indicating the relationship between the acquired time and the amplitude to the numerical waveform data indicating the relationship between the spectrum such as the power spectrum and the frequency.

  The evaluation device 9 may perform wavelet transform on the acquired waveform numerical data as the inspection data to convert it into waveform numerical data indicating the relationship between time and frequency. By applying the wavelet transform, it is possible to leave the time characteristics that are lost in the Fourier transform. For this reason, by performing wavelet transform, it is converted into waveform numerical data indicating the relationship between time and frequency.

  The evaluation device 9 takes the logarithm of the value of the power spectrum obtained by Fourier transform on the acquired waveform numerical data as the inspection data, and further converts it into waveform numerical data indicating a cepstrum obtained by inverse Fourier transform. May be. Further, the evaluation device 9 may further extract waveform numerical data indicating a spectral envelope that is a low-order cepstrum or a spectral fine structure that is a high-order cepstrum from the numerical waveform data indicating a cepstrum. For example, the waveform numerical data indicating the spectral envelope may be extracted by determining the cepstrum order. The cepstrum order can take an arbitrary value such as 20, 100, for example. Further, in the numerical waveform data indicating the spectral envelope, coefficients of each cepstrum order may be extracted. Further, the evaluation device 9 may convert the fluctuation width cut out from a certain time domain into waveform numerical data indicating a formant that is a peak when converted into the frequency domain in the acquired waveform image. When the first formant, the second formant,... Are selected from the lowest peak frequency band, the waveform numerical data indicates, for example, the relationship between the first formant and the second formant. May be shown. The evaluation device 9 may perform AFTE (Auditory filterbank temporal envelope) conversion on the waveform numerical data as the acquired inspection data.

  Thus, the evaluation apparatus 9 may convert the acquired inspection data into two-dimensional waveform numerical data. The two-dimensional numerical waveform data may indicate two relationships among, for example, time, amplitude, frequency, spectrum, cepstrum, formant, and the like. Moreover, you may take these reciprocals.

  Further, the evaluation device 9 may convert the acquired inspection data into three-dimensional waveform numerical data by performing a spectrogram or the like, for example. For example, the three-dimensional waveform numerical data may indicate three relationships among time, amplitude, frequency, spectrum, cepstrum, formant, and the like.

  Incidentally, the evaluation device 9 can display each inspection data via a display unit including a display (not shown), for example. Further, the evaluation device 9 can record each data in the storage, display the data on the display unit based on a command from the user, or write the data to the portable memory. The user can remove the portable memory from the evaluation device 9 and carry it freely. Furthermore, the evaluation device 9 can transfer these data to other electronic devices via the public communication network.

  In the present invention, the configuration of the evaluation device 9 is not essential and may be omitted. In such a case, the inspection data output from the nondestructive inspection unit 8 is directly transmitted to the determination device 2.

  The database 3 is constructed with respect to the condition for determining the grout filling status to be provided. The database 3 stores information sent via a public communication network or information input by a user of this system. Further, the database 3 transmits the accumulated information to the determination device 2 based on a request from the determination device 2.

  The discriminating device 2 is composed of electronic devices such as a personal computer (PC), for example. In addition to the PC, the discriminating device 2 can be embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, and a wearable terminal. It may be made. The user can determine whether or not the grout filling in the cable sheath 71 is sufficient by obtaining the determination result of the grout filling state as a search solution by the determination device 2. When the grout is not sufficiently filled in the cable sheath 71, an operation of filling the grout into the cable sheath 71 by operating a pressure feed pump (not shown) is performed.

  FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the discrimination device 2 as a whole, and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, and the like. A communication unit 26 for searching, a search unit 27 for searching for an optimum design condition, and a storage unit 28 for storing a program for performing a search to be executed, represented by a hard disk or the like, are connected to the internal bus 21, respectively. Has been. Further, the internal bus 21 is connected to a display unit 23 as a monitor for actually displaying information.

  The control unit 24 is a so-called central control unit for controlling each component mounted in the determination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in accordance with an operation via the operation unit 25.

  The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input from the user, the operation unit 25 notifies the control unit 24 of this. Upon receiving this notification, the control unit 24 executes a desired processing operation in cooperation with each component including the search unit 27.

  The search unit 27 searches for the determination result of the grout filling status. The search unit 27 reads various information stored in the storage unit 28 as necessary information and various information stored in the database 3 when executing the search operation. The search unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

  The display unit 23 includes a graphic controller that creates a display image based on control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD).

  When the storage unit 28 is composed of a hard disk, based on the control by the control unit 24, predetermined information is written to each address and is read out as necessary. The storage unit 28 stores a program for executing the present invention. This program is read by the control unit 24 and executed.

  The operation in the grout filling situation determination system 1 having the above-described configuration will be described.

  In the grout filling status discrimination system 1, for example, as shown in FIG. 3, the grout filling in the cable sheath 71 is performed based on input parameters consisting of inspection data A, B,. The output solution related to the situation discrimination result is searched. The determination result of the grout filling state may be shown as information determined as a filling rate such as a filling rate of 0%, 50%, 100%, etc. It may be indicated by a determination result such as a filling state P indicating what distribution the grout is filled with.

  The input parameters are inspection data detected by the nondestructive inspection unit 8, processed by the evaluation device 9 as necessary, and analyzed. Inspection data A, B,... For different nondestructive inspections are input as input parameters.

  Thus, after the inspection data is detected by the nondestructive inspection unit 8, the processing operation by the search program is actually executed. The processing operation flow of this search program is shown in FIG.

  The evaluation device 9 performs various analyzes on the inspection data detected by the nondestructive inspection unit 8 in step S11 as necessary, and converts the data into various data as necessary in order to facilitate the search by the subsequent search device. Processing is performed (step S12). Incidentally, this step S12 can be omitted when various analyzes and processing are not performed.

  Next, the process proceeds to step S13, and an output solution of inspection data and association degree is searched. Prior to performing this search, the database 3 obtains in advance three or more levels of relevance between a reference input parameter (hereinafter referred to as a reference input parameter) and a grout filling status determination result as an output solution. Keep it.

  FIG. 5 shows an example of the association degree acquired in advance in the database 3. In the example of FIG. 5, a shock elastic wave method is used as a nondestructive inspection method in the nondestructive inspection unit 8. The inspection data detected by the nondestructive inspection unit 8 based on the shock elastic wave method is constituted by, for example, waveform numerical data detected when an impact is applied. For this reason, in the reference input parameter, waveform numerical data detected when an impact is applied based on the shock elastic wave method is learned in advance as past waveform numerical data.

  In the example shown in FIG. 5, the waveform numerical data is configured by numerical data indicating the relationship between time and fluctuation width.

  The database 3 stores in advance three or more levels of relevance between the past numerical waveform data as the reference input parameter and the determination result of the grout filling state in the cable sheath 71 as the output solution. . According to the example of FIG. 5, when the reference input parameter is the past waveform numerical data h1, “filling rate 50%” is set with an association degree of 80%, and “filling rate 0%” is set with an association degree of 60%. ing. When the reference input parameter is the past waveform numerical data h2, “filling rate 100%” is set with an association degree of 90%, and “filling state P” is set with an association degree of 40%.

  These association degrees are stored in the database 3 in advance in the database 3 as past waveform numerical data obtained when the nondestructive inspection was previously performed based on the shock elastic wave method, and as a result of the determination. These may be set based on them. This association degree may be configured by a so-called neural network. This degree of association indicates accuracy in determining the grout filling state in the actual cable sheath 71 based on the past waveform numerical data when the nondestructive inspection is performed based on the shock elastic wave method. Is. For example, for past waveform numerical data h3 based on the shock elastic wave method, the “filling status Q” with an association degree of 70% is close to the most accurate determination, and the “filling rate of 50%” with an association degree of 50% is close to this. It will be an accurate judgment that follows. Similarly, for past waveform numerical data h2 based on the shock elastic wave method, “filling rate 100%” with an association degree of 90% is close to the most accurate judgment, and “filling status P” with an association degree of 40% is this. It will be an accurate judgment that follows.

  After shifting to step S13, the search program performs an operation of determining the grout filling state in the cable sheath 71 based on the input parameter detected in step S11 or analyzed in step S12. Reference is made to the association shown in FIG. 5 acquired in advance to determine the grout filling status. For example, when the waveform numerical data of the input parameter is the past waveform numerical data h1 as the input parameter for reference or approximates it, when the above-mentioned association degree is referred to, the highest association degree is obtained. “Filling rate 50%” is selected as the optimal solution. However, it is not essential to select a solution with the highest degree of association as the optimum solution, and “filling rate 0%” that allows the association itself with a low degree of association may be selected as the optimum solution. Of course, an output solution that is not connected with an arrow may be selected. That is, the selection of the grout filling status in the cable sheath 71 is not limited to the case where the degree of association is selected in order from the highest degree of association, but the order of selection from the order of the lowest association degree according to the case. Or any other priority order.

  Further, the waveform numerical data of the input parameter is partially similar to the past waveform numerical data h2 as the reference input parameter, but is partially similar to the past waveform numerical data h3 and may be assigned to any of them. In the case where it is not known, for example, it may be determined which reference input parameter is assigned based on the feature amount on the numerical data. At this time, it may be determined which reference input parameter is to be assigned based on the feature value on the numerical data through deep learning. For example, a threshold value may be set in advance in a predetermined time zone, frequency band, and amplitude range, and the feature amount may be a magnitude relationship of time, frequency, and amplitude with respect to the threshold value.

  For example, the relationship between time and amplitude, frequency, the relationship between frequency and amplitude, the relationship between time and amplitude leaving a specific frequency region, the relationship between frequency and amplitude leaving a specific frequency region, time and In the numerical data indicating any of the relations with the frequency, a predetermined time, fluctuation width, frequency, magnitude relation with respect to these threshold values, or the like may be used as the feature amount in the numerical data. In addition, for example, cepstrum and formants may be used as the feature values on the numerical data.

  Thus, after assigning the waveform numerical data as the input parameter to the past waveform numerical data as the reference input parameter, the grout filling situation as the output solution based on the association degree set in the reference input parameter. Will be selected.

  When the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between time and fluctuation width, the analysis of the acquired waveform numerical data can be omitted. For this reason, it is possible to transmit the acquired waveform numerical data as it is from the evaluation device 9 to the discrimination device 2. As a result, the processing time until the output solution is output can be shortened.

  When the waveform numerical data and the past waveform numerical data are numerical data indicating the frequency, the frequency that cannot be obtained by the numerical data indicating the relationship between the time and the fluctuation width, which is a complicated relationship, may be used as the feature amount. it can. Thereby, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the frequency. For this reason, since it can discriminate | determine based on the magnitude relationship with respect to a predetermined frequency and the threshold value of a frequency, for example, it becomes possible to further improve the discrimination | determination precision of a grout filling condition.

  When the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the frequency and the amplitude, the frequency that cannot be acquired by the numerical data indicating the relationship between the time and the amplitude that is a complicated relationship. The relationship with the runout width can be used as a feature amount. Thereby, when the waveform numerical data as the input parameter is assigned to the past waveform numerical data as the reference input parameter, the assignment can be performed based on the relationship between the frequency and the amplitude. For this reason, for example, it is possible to make a determination based on a predetermined frequency, a fluctuation width, or a magnitude relationship with respect to these threshold values, so that it is possible to further improve the accuracy of determining the grout filling status.

  If the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the time remaining in the specific frequency region and the amplitude, the relationship between the time remaining in the specific frequency region and the amplitude It can be an amount. As a result, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the relationship between the time remaining in the specific frequency region and the amplitude. it can. For this reason, for example, it is possible to determine based on a predetermined time, a fluctuation width, and a magnitude relationship with respect to these threshold values, and based on a relation between a time and a fluctuation width in which noises unrelated to the grout filling state are removed. Since it can be determined, it is possible to further improve the accuracy of determining the grout filling status.

  If the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the frequency and the amplitude remaining in the specific frequency region, the relationship between the frequency and the amplitude remaining in the specific frequency region is characterized. It can be an amount. Thereby, when assigning the waveform numerical data as the input parameter to the past waveform numerical data as the reference input parameter, the assignment can be performed based on the relationship between the frequency and the amplitude leaving the specific frequency region. it can. For this reason, for example, it is possible to discriminate based on a predetermined frequency, an amplitude, and a magnitude relationship with respect to these threshold values, and on the basis of the relationship between the frequency and the amplitude, from which noise and the like that are not related to the grout filling state are removed Since it can be determined, it is possible to further improve the accuracy of determining the grout filling status.

  When the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between time and frequency, the relationship between time and frequency can be used as a feature amount. Thereby, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the relationship between time and frequency. For this reason, since it can discriminate | determine based on the frequency which considered the time characteristic, for example, it becomes possible to further improve the discrimination | determination precision of a grout filling condition.

  Note that the method of selecting an output solution for the input parameter is not limited to the above-described method, and may be executed based on any method as long as it refers to the association degree. Further, the search operation in step S13 may be performed using artificial intelligence.

  Next, the process proceeds to step S14, and the determination result of the grout filling state in the cable sheath 71 as the selected optimum solution is displayed via the display unit 23. Thus, the user can immediately grasp the determination result of the grout filling state in the cable sheath 71 by visually recognizing the display unit 23.

  In the example of FIG. 6, a combination of past numerical waveform data detected based on the shock elastic wave method, past input / output ratio detected based on the shock elastic wave method, data such as propagation velocity, and the like, An example is shown in which three or more levels of association with the determination result of the grout filling status for the combination are set.

In such a case, as shown in FIG. 6, the relevance is a combination of past waveform numerical data h1, h2,... As non-destructive inspection data and data such as past input / output ratio and propagation speed. Are expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data h1 has an association degree of 40%, “input / output ratio: 0.001 to 0.1 (× 10 ⁻³ ), propagation speed: 4.5 to 5.0 ( m / s) ”is associated with an association degree of 50%. The node 61d has a past waveform numerical value data h2 of 40% association, “input / output ratio: 0.001 to 0.1 (× 10 ⁻³ ), propagation speed: 3.0 to 4.0 (m / s) "is associated with an association degree of 30%.

Similarly, when such association is set, when the process proceeds to step S13, the waveform numerical data as input parameters detected in step S11 or extracted in step S12, the input / output ratio, and the propagation Based on data such as speed, an output solution corresponding to the degree of association is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimum solution, the association degree shown in FIG. 6 acquired in advance is referred to. For example, in the input parameter, it is the waveform numerical data h1, and “input / output ratio: 0.001 to 0.1 (× 10 ⁻³ ), propagation speed: 4.5 to 5.0 (m / s)”. In some cases, the node 61b is associated with the degree of association, and the node 61b is associated with the degree of filling of 60% and the degree of filling Q of 40%.

  In this way, waveform numerical data as input parameters and data such as input / output ratio and propagation speed are converted into past waveform numerical data as reference input parameters and data such as past input / output ratio and propagation speed. After the assignment, the grout filling situation as an output solution is selected based on the association degree set in the reference input parameter.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the example of FIG. 7, there are three or more levels of a combination of past waveform numerical data detected based on the shock elastic wave method, incidental information on the past cable sheath 71, and a grout filling state determination result for the combination. In this example, the degree of association is set.

  The incidental information includes the configuration information of the cable sheath 71, the arrangement information of the cable sheath 71, the tension material information regarding the tension material (PC steel material) provided in the PC structure 7 in which the cable sheath 71 is provided, and the cable sheath 71 is filled. Information regarding grout to be performed, concrete information to be placed in the PC structure provided with the cable sheath 71, test condition information of the non-destructive inspection for the cable sheath 71, and the surface state of the PC structure 7 provided with the cable sheath 71 Any one or more of the information and configuration information in the PC structure 7 provided with the cable sheath 71 are included. The past incidental information is such past information.

  The form information of the cable sheath 71 includes the length of the cable sheath 71, the diameter of the cable sheath 71, and the like. The arrangement information of the cable sheath 71 includes all information related to the arrangement form of the cable sheath 71 in the PC structure 7. Examples of the arrangement information of the cable sheath 71 include, for example, information on whether or not the cable sheaths are arranged in parallel when viewed from the front, and the interval between the cable sheaths 71. The tension material information includes the length of the PC steel material, the type of the PC steel material, and the tension force. The grout information is the Young's modulus of the grout. The concrete information includes the Young's modulus of concrete placed on the PC structure 7 and the presence / absence of cover concrete. In the case of the impact elastic wave method, the test condition information for the nondestructive inspection includes information on the impact force of the steel ball that strikes the inspection target. In the case of the impact echo method, the test condition information for the non-destructive inspection includes information on the diameter of the hitting steel ball for hitting the inspection target. In the case of the ultrasonic method, the test condition information for the nondestructive inspection includes information on the frequency of the ultrasonic wave applied toward the inspection target. In the case of the X-ray transmission method, the test condition information for the nondestructive inspection includes information on the amount of X-ray radiation irradiated toward the inspection object. The surface state information of the PC structure 7 includes information such as the presence / absence of unevenness, cracking conditions, and wet / dry conditions. The configuration information in the PC structure 7 is an arrangement interval of the reinforcing bars.

  In the example shown in FIG. 7, the incidental information is configured by the form information of the cable sheath 71.

  In such a case, as shown in FIG. 7, the association degree is a set of combinations of past waveform numerical data h1, h2,... As non-destructive inspection data and form information of the past cable sheath 71. It is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data h1 is associated with an association degree of 40%, and “former diameter: 50 mm to 75 mm” as the form information of the past cable sheath 71 is associated with an association degree of 50%. The node 61d is associated with a past waveform numerical value data h2 having an association degree of 40% and a past sheath information of “sheath diameter: 26 mm to 50 mm” as a form information of the cable sheath 71 with an association degree of 30%.

  Similarly, when such an association degree is set, when the process proceeds to step S13, waveform numerical data of nondestructive inspection as an input parameter detected in step S11 or extracted in step S12, and An output solution corresponding to the association degree of the form information of the cable sheath 71 is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting the optimum solution, the association degree shown in FIG. 7 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data h1 and “sheath diameter: 50 mm to 75 mm” as the form information of the cable sheath 71, the node 61b is associated through the association degree. Are associated with a relevance of 60% and a “filling status Q” of 40%.

  In this way, after assigning the waveform numerical data and the incidental information as the input parameters to the past waveform numerical data and the past incidental information as the reference input parameters, the correlation set in the reference input parameter is set. The grout filling situation as an output solution is selected based on the degree.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the example of FIG. 8, the combination of past waveform numerical data detected based on the shock elastic wave method and the past X-ray image detected based on the X-ray transmission method, and the grout filling state for the combination. An example in which three or more levels of association with the determination result are set is shown.

  In such a case, as shown in FIG. 8, the relevance is determined by the past waveform numerical data h1, h2,... As the inspection data of the nondestructive inspection and the past X-ray images r1, r2,. A set of combinations is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data h1 is associated with an association degree of 40%, and the past X-ray image r2 is associated with an association degree of 50%. The node 61d is associated with the past waveform numerical data h2 with an association degree of 40% and the past X-ray image r1 with an association degree of 30%.

  Similarly, when such association is set, when the process proceeds to step S13, the waveform numerical data and the X-ray image as input parameters detected in step S11 or extracted in step S12 are used. Based on this, an output solution corresponding to the degree of association is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimum solution, the association degree shown in FIG. 8 acquired in advance is referred to. For example, when the input parameter is the waveform numerical value data h1 and the X-ray image r2, the node 61b is associated through the association degree, and the node 61b has an association degree of 60 with a “filling rate of 50%”. %, “Filling status Q” is associated with an association degree of 40%.

  Thus, after assigning the waveform numerical data and the X-ray image as the input parameters to the past waveform numerical data and the past X-ray image as the reference input parameters, they are set as the reference input parameters. The grout filling situation as an output solution is selected based on the degree of association.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the example of FIG. 9, the past waveform numerical data detected based on the shock elastic wave method, the incidental information related to the past cable sheath 71, and the past X-ray image detected based on the X-ray transmission method. An example is shown in which three or more levels of association between the combination and the determination result of the grout filling status for the combination are set.

  In the example shown in FIG. 9, the incidental information is composed of grout information.

  In such a case, as shown in FIG. 9, the relevance is determined by past waveform numerical data h1, h2,... As past inspection data of nondestructive inspection, past grout information, and past X-ray images r1, r2. ,... Are expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data h1 has an association degree of 40%, “Young coefficient: 30 to 35 GPa” as past grout information has an association degree of 50%, and the past X-ray image r1 has an association degree of 50%. % Are related. In the node 61d, the past waveform numerical data h2 has an association degree of 40%, “Young coefficient: 26-30 GPa” as past grout information has an association degree of 30%, and the past X-ray image r2 has an association degree of 30%. Are linked.

  Similarly, when such association is set, when the process proceeds to step S13, the numerical waveform data, the grout information, and the X-rays as input parameters detected in step S11 or extracted in step S12. Based on the image, an output solution corresponding to the association degree is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimum solution, the association degree shown in FIG. 9 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data h1, the “Young coefficient: 30 to 35 GPa” as the grout information, and the X-ray image r1, the node 61b is associated via the association degree. The node 61b is associated with a “filling rate of 50%” with an association degree of 60% and a “filling state Q” with an association degree of 40%.

  Thus, after assigning the waveform numerical data, the grout information, and the X-ray image as the input parameters to the past waveform numerical data, the past grout information, and the past X-ray image as the reference input parameters, The grout filling situation as an output solution is selected based on the relevance set in the reference input parameter.

  The search program similarly outputs a solution in step S14 based on the search result.

  FIG. 10 shows an example of the association degree acquired in advance in the database 3. In the example of FIG. 10, the impact echo method is used as a nondestructive inspection method in the nondestructive inspection unit 8. The inspection data detected by the nondestructive inspection unit 8 based on the impact echo method is composed of waveform numerical data detected when an impact is applied, for example. For this reason, in the reference input parameter, the waveform numerical data detected when an impact is applied based on the impact echo method is learned in advance as past waveform numerical data.

  In the example of FIG. 10, the waveform numerical data is composed of numerical data indicating the relationship between the frequency and the amplitude.

  The database 3 stores in advance three or more levels of relevance between the past numerical waveform data as the reference input parameter and the determination result of the grout filling state in the cable sheath 71 as the output solution. . According to the example of FIG. 10, when the reference input parameter is the past waveform numerical data k1, “filling rate 50%” is set with an association degree of 80% and “filling rate 0%” with an association degree of 60%. ing. Further, when the reference input parameter is the past waveform numerical data k2, “filling rate 100%” is set at 90% relevance and “filling status P” is set at 40% relevance.

  These association degrees are stored in the database 3 in advance in the database 3 in the past waveform numerical data when the non-destructive inspection was performed based on the impact echo method, and the determination result. You may make it set based on them. This association degree may be configured by a so-called neural network. This degree of association indicates accuracy in determining the grout filling state in the actual cable sheath 71 based on the past waveform numerical data when the nondestructive inspection is performed based on the impact echo method. It is. For example, for past waveform numerical data k3 based on the impact echo method, the “filling status Q” with an association degree of 70% is closest to the most accurate determination, followed by the “filling rate of 50%” with an association degree of 50%. It will be an accurate judgment. Similarly, for past waveform numerical data k2 based on the impact echo method, “filling rate of 100%” with an association degree of 90% is close to the most accurate judgment, and “filling condition P” with an association degree of 40% is the most appropriate judgment. It will be an accurate judgment that follows.

  After shifting to step S13, the search program performs an operation of determining the grout filling state in the cable sheath 71 based on the input parameter detected in step S11 or analyzed in step S12. Reference is made to the association shown in FIG. 10 acquired in advance to determine the grout filling status. For example, when the waveform numerical data of the input parameter is the past waveform numerical data k1 as the reference input parameter, or approximates it, when the above-mentioned association degree is referred to, the highest association degree is obtained. “Filling rate 50%” is selected as the optimal solution. However, it is not essential to select a solution with the highest degree of association as the optimum solution, and “filling rate 0%” that allows the association itself with a low degree of association may be selected as the optimum solution. Of course, an output solution that is not connected with an arrow may be selected. That is, the selection of the grout filling status in the cable sheath 71 is not limited to the case where the degree of association is selected in order from the highest degree of association, but the order of selection from the order of the lowest association degree according to the case. Or any other priority order.

  Further, the waveform numerical data of the input parameter is partially similar to the past waveform numerical data k2 as the reference input parameter, but is also partially similar to the past waveform numerical data k3 and may be assigned to any of them. In the case where it is not known, for example, it may be determined which reference input parameter is assigned based on the feature amount on the numerical data. At this time, it may be determined which reference input parameter is to be assigned based on the feature value on the numerical data through deep learning.

  Thus, after assigning the waveform numerical data as the input parameter to the past waveform numerical data as the reference input parameter, the grout filling as the output solution is performed based on the association degree set in the reference input parameter. The situation will be selected.

  The search program similarly outputs a solution in step S14 based on the search result.

  Next, the process proceeds to step S14, and the determination result of the grout filling state in the cable sheath 71 as the selected optimum solution is displayed via the display unit 23. Thus, the user can immediately grasp the determination result of the grout filling state in the cable sheath 71 by visually recognizing the display unit 23.

  In the example of FIG. 11, there are three or more levels of combinations of past waveform numerical data detected based on the impact echo method, past incidental information on the cable sheath 71, and the grout filling status determination result for the combination. An example in which the association degree is set is shown.

  In the example shown in FIG. 11, the incidental information is configured by test condition information.

  In this case, as shown in FIG. 11, the relevance is a set of combinations of past waveform numerical data k1, k2,... As non-destructive inspection data and past test condition information. Nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data k1 is associated with an association degree of 40%, and “steel ball diameter: 100 mm to 200 mm” as past test condition information is associated with an association degree of 50%. The node 61d is associated with the past waveform numerical data k2 having an association degree of 40% and “steel ball diameter: 30 mm to 100 mm” as test condition information with an association degree of 30%.

  Similarly, when such an association degree is set, when the process proceeds to step S13, waveform numerical data of nondestructive inspection as an input parameter detected in step S11 or extracted in step S12, and Search for an output solution according to the relevance of the test condition information. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 11 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data k1 and “steel ball diameter: 100 mm to 200 mm” as the test condition information, the node 61b is associated via the association degree. “Filling rate 50%” is associated with an association degree of 60%, and “filling state Q” is associated with an association degree of 40%.

  In this way, after assigning the waveform numerical data and the test condition information as the input parameters to the past waveform numerical data and the past test condition information as the reference input parameters, they are set to the reference input parameters. The grout filling situation as an output solution is selected based on the degree of association.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the example of FIG. 12, the combination of past waveform numerical data detected based on the impact echo method and the past X-ray image detected based on the X-ray transmission method, and determination of the grout filling state for the combination. An example is shown in which three or more levels of association with results are set.

  In such a case, as shown in FIG. 12, the relevance is obtained by past waveform numerical data k1, k2,... As non-destructive inspection data and past X-ray images r1, r2,. A set of combinations is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data k1 is associated with an association degree of 40%, and the past X-ray image r2 is associated with an association degree of 50%. The node 61d is associated with the past waveform numerical data k2 with an association degree of 40% and the past X-ray image r1 with an association degree of 30%.

  Similarly, when such association is set, when the process proceeds to step S13, the waveform numerical data and the X-ray image as input parameters detected in step S11 or extracted in step S12 are used. Based on this, an output solution corresponding to the degree of association is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 12 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data k1 and the X-ray image r2, the node 61b is associated through the association degree, and the node 61b has an association degree of 60 with a “filling rate of 50%”. %, “Filling status Q” is associated with an association degree of 40%.

  Thus, after assigning the waveform numerical data and the X-ray image as the input parameters to the past waveform numerical data and the past X-ray image as the reference input parameters, they are set as the reference input parameters. The grout filling situation as an output solution is selected based on the degree of association.

  The search program similarly outputs a solution in step S14 based on the search result.

  In addition, although illustration is abbreviate | omitted, in the database 3, the past waveform numerical data detected based on the impact echo method, the incidental information regarding the past cable sheath 71, and the past detected based on the X-ray transmission method are stored. Three or more levels of association may be set between the combination of the X-ray images and the determination result of the grout filling status for the combination.

  Even at this time, similarly, input parameters (waveform numerical data, incidental information, and X-ray image) are used as reference input parameters (past waveform numerical data, past incidental information, and past X-ray images). Then, the grout filling state as an output solution is selected based on the association degree set in the reference input parameter.

  FIG. 13 shows an example of the association degree acquired in advance in the database 3. In the example of FIG. 13, an ultrasonic method is used as a nondestructive inspection method in the nondestructive inspection unit 8. The inspection data detected by the nondestructive inspection unit 8 based on this ultrasonic method is composed of waveform numerical data relating to ultrasonic waves. For this reason, in the reference input parameter, waveform numerical data detected based on the ultrasonic method is learned in advance as past waveform numerical data. As the ultrasonic method, for example, a broadband ultrasonic method or the like is used.

  In the example of FIG. 13, the waveform numerical data is constituted by numerical data indicating the relationship between the frequency leaving the specific frequency region and the fluctuation width.

  The database 3 stores in advance three or more levels of relevance between the past numerical waveform data as the reference input parameter and the determination result of the grout filling state in the cable sheath 71 as the output solution. . According to the example of FIG. 13, when the reference input parameter is the past waveform numerical data m1, “filling rate 50%” is set with an association degree of 80% and “filling rate 0%” with an association degree of 60%. ing. Further, when the reference input parameter is the past waveform numerical data m2, the “filling rate 100%” is set to 90% and the “filling status P” is set to 40%.

  These association degrees are stored in the database 3 in advance in the database 3 in the past waveform numerical data when the non-destructive inspection was previously performed based on the ultrasonic method, and the determination result and the filling rate and filling state, You may make it set based on them. This association degree may be configured by a so-called neural network. This degree of association indicates the accuracy with which the grout filling state in the actual cable sheath 71 is determined based on the past waveform numerical data when the nondestructive inspection is performed based on the ultrasonic method. It is. For example, with respect to past waveform numerical data m3 based on the ultrasonic method, “filling status Q” with an association degree of 70% is closest to the most accurate determination, followed by “filling rate 50%” with an association degree of 50%. It will be an accurate judgment. Similarly, for the past waveform numerical data m2 based on the ultrasonic method, an association degree of 90% “filling rate of 100%” is close to the most accurate determination, followed by “filling state P” of an association degree of 40%. It will be an accurate judgment.

  After shifting to step S13, the search program performs an operation of determining the grout filling state in the cable sheath 71 based on the input parameter detected in step S11 or analyzed in step S12. Reference is made to the association shown in FIG. 13 acquired in advance to determine the grout filling status. For example, when the waveform numerical data of the input parameter is the past waveform numerical data m1 as the input parameter for reference or approximates it, when the above-mentioned association degree is referred to, the highest association degree is obtained. “Filling rate 50%” is selected as the optimal solution. However, it is not essential to select a solution with the highest degree of association as the optimum solution, and “filling rate 0%” that allows the association itself with a low degree of association may be selected as the optimum solution. Of course, an output solution that is not connected with an arrow may be selected. That is, the selection of the grout filling status in the cable sheath 71 is not limited to the case where the degree of association is selected in order from the highest degree of association, but the order of selection from the order of the lowest association degree according to the case. Or any other priority order.

  Further, the waveform numerical data of the input parameter is partially similar to the past waveform numerical data m2 as the reference input parameter, but is also partially similar to the past waveform numerical data m3 and may be assigned to any of them. In the case where it is not known, for example, it may be determined which reference input parameter is assigned based on the feature amount on the numerical data. At this time, it may be determined which reference input parameter is to be assigned based on the feature value on the numerical data through deep learning.

  Thus, after assigning the waveform numerical data as the input parameter to the past waveform numerical data as the reference input parameter, the grout filling as the output solution is performed based on the association degree set in the reference input parameter. The situation will be selected.

  Next, the process proceeds to step S14, and the determination result of the grout filling state in the cable sheath 71 as the selected optimum solution is displayed via the display unit 23. Thus, the user can immediately grasp the determination result of the grout filling state in the cable sheath 71 by visually recognizing the display unit 23.

  In the example of FIG. 14, there are three or more stages of a combination of past waveform numerical data detected based on the ultrasonic method, incidental information on the past cable sheath 71, and a grout filling status determination result for the combination. An example in which the association degree is set is shown.

  In addition, in the example shown in FIG. 14, incidental information is comprised by concrete information.

  In such a case, as shown in FIG. 14, the relevance is a set of combinations of past waveform numerical data m1, m2,... As past non-destructive inspection data and past concrete information. It will be expressed as nodes 61a-61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data m1 is associated with an association degree of 40%, and “Young's modulus: 30 to 35 GPa” as past concrete information is associated with an association degree of 50%. The node 61d is associated with the past waveform numerical data m2 having an association degree of 40% and the past concrete information “Young's modulus: 26 to 30 GPa” with an association degree of 30%.

  Similarly, when such an association degree is set, when the process proceeds to step S13, waveform numerical data of nondestructive inspection as an input parameter detected in step S11 or extracted in step S12, and Search the output solution according to the relevance of concrete information. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimum solution, the association degree shown in FIG. 14 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data m1 and the “Young's modulus: 30 to 35 GPa” as the concrete information, the node 61b is associated with the degree of association. "Rate 50%" is associated with an association degree of 60%, and "Filling state Q" is associated with an association degree of 40%.

  In this way, after assigning the waveform numerical data and concrete information as input parameters to the past waveform numerical data and past concrete information as reference input parameters, the correlation set in the reference input parameter is set. The grout filling situation as an output solution is selected based on the degree.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the example of FIG. 15, the combination of past waveform numerical data detected based on the ultrasonic method and the past X-ray image detected based on the X-ray transmission method, and determination of the grout filling status for the combination An example is shown in which three or more levels of association with results are set.

  In such a case, as shown in FIG. 15, the relevance is obtained by past waveform numerical data m1, m2,... As non-destructive inspection data and past X-ray images r1, r2,. A set of combinations is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data m1 is associated with an association degree of 40%, and the past X-ray image r2 is associated with an association degree of 50%. The node 61d is associated with the past waveform numerical data m2 having an association degree of 40% and the past X-ray image r1 with an association degree of 30%.

  Similarly, when such association is set, when the process proceeds to step S13, the waveform numerical data and the X-ray image as input parameters detected in step S11 or extracted in step S12 are used. Based on this, an output solution corresponding to the degree of association is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 15 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data m1 and the X-ray image r2, the node 61b is associated through the association degree, and the node 61b has an association degree of 60 with a “filling rate of 50%”. %, “Filling status Q” is associated with an association degree of 40%.

  Thus, after assigning the waveform numerical data and the X-ray image as the input parameters to the past waveform numerical data and the past X-ray image as the reference input parameters, they are set as the reference input parameters. The grout filling situation as an output solution is selected based on the degree of association.

  The search program similarly outputs a solution in step S14 based on the search result.

  In addition, although illustration is abbreviate | omitted, in the database 3, the past waveform numerical data detected based on the ultrasonic method, the incidental information regarding the past cable sheath 71, and the past detected based on the X-ray transmission method are stored. Three or more levels of association may be set between the combination of the X-ray images and the determination result of the grout filling status for the combination.

  Even at this time, similarly, input parameters (waveform numerical data, incidental information, and X-ray image) are used as reference input parameters (past waveform numerical data, past incidental information, and past X-ray images). Then, the grout filling state as an output solution is selected based on the association degree set in the reference input parameter.

  In the example of FIG. 16, a combination of past waveform numerical data detected based on the impact elastic wave and past waveform numerical data detected based on the impact echo method, and a grout filling state determination result for the combination This shows an example in which three or more levels of association are set.

  In this case, as shown in FIG. 16, the relevance is the past waveform numerical data h1, h2,... By the impact elastic wave method as the inspection data of the nondestructive inspection, and the past waveform numerical data by the impact echo method. A set of combinations of k1, k2,... is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61b, the past waveform numerical data h1 is associated with an association degree of 40%, and the past waveform numerical data k2 is associated with an association degree of 50%. Further, the node 61d is associated with the past waveform numerical data h2 with an association degree of 40% and the past waveform numerical data k1 with an association degree of 30%.

  Similarly, when such association is set, when the process proceeds to step S13, the numerical value data of the shock elastic wave method as the input parameter detected in step S11 or extracted in step S12 The output solution corresponding to the degree of association of the numerical waveform data of the impact echo method is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 16 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data h1 and the waveform numerical data k2, the node 61b is associated through the association degree, and the node 61b has an association degree of 60 with a “filling rate of 50%”. %, “Filling status Q” is associated with an association degree of 40%.

  In this way, the numerical value data of the shock elastic wave method and the numerical value data of the impact echo method are used as input parameters. The numerical value data of the past shock elastic wave method and the past impact echo method are used as the input parameters for reference. Then, the grout filling state as the output solution is selected based on the association degree set in the reference input parameter.

  The search program similarly outputs a solution in step S14 based on the search result.

  Although illustration is omitted, for example, the database 3 includes a combination of past waveform numerical data detected based on the impact elastic wave and waveform numerical data detected based on the ultrasonic method, and the combination. Three or more levels of association with the determination result of the grout filling status for may be set. Although not shown, the database 3 includes a combination of past waveform numerical data detected based on the impact echo method and waveform numerical data detected based on the ultrasonic method, and a grout for the combination. Three or more levels of association with the determination result of the filling status may be set. Thus, in the present invention, the database 3 includes a combination of past waveform numerical data obtained by nondestructive inspection of any two or more of the impact elastic wave method, impact echo method, and ultrasonic method, and the combination Three or more levels of association with the determination result of the grout filling status may be set.

  In the example of FIG. 17, a combination of past waveform numerical data detected based on the impact elastic wave, past waveform numerical data detected based on the impact echo method, and incidental information related to the past cable sheath 71. In this example, three or more levels of association with the result of determining the grout filling status for the combination are set.

  In the example shown in FIG. 17, the incidental information includes surface state information of the PC structure 7 and arrangement information of the cable sheath 71.

  In this case, as shown in FIG. 17, the relevance is the past waveform numerical data h1, h2,... By the impact elastic wave method as non-destructive inspection data, and the past waveform numerical data by the impact echo method. A set of combinations of k1, k2,..., past surface state information of the PC structure 7 and arrangement information of the cable sheath 71 is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61c, the past waveform numerical data h2 has an association degree of 60%, the past waveform numerical data k2 has an association degree of 60%, and “flat” as the surface state information of the past PC structure 7 is associated. The non-parallel arrangement as the arrangement information of the past cable sheath 71 is associated with the association degree of 50%. In the node 61d, the past waveform numerical data h2 has an association degree of 30%, the past waveform numerical data k2 has an association degree of 70%, and “non-parallel arrangement” as past arrangement information of the cable sheath 71 has an association degree of 90%. % Are related.

  Similarly, when such association is set, when the process proceeds to step S13, the numerical value data of the shock elastic wave method as the input parameter detected in step S11 or extracted in step S12 The output solution corresponding to the degree of association between the numerical value data of the impact echo method, the surface state information of the PC structure 7 and the arrangement information of the cable sheath 71 is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimum solution, the association degree shown in FIG. 17 acquired in advance is referred to. For example, in the input parameters, the waveform numerical data h2 and the waveform numerical data k2 are “flat” as the surface state information of the PC structure 7 and “non-parallel arrangement” as the arrangement information of the cable sheath 71. In this case, the node 61c is associated through the association degree, and the node 61c is associated with the “filling rate of 0%” at the association degree of 70%.

  In this way, the input parameters (waveform numerical data of the shock elastic wave method, waveform numerical data of the impact echo method, and incidental information) are input to the reference input parameters (waveform numerical data of the past shock elastic wave method and the past. In this case, the grout filling state as an output solution is selected based on the relevance set in the reference input parameter.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the present invention, the database 3 includes a combination of past waveform numerical data obtained by nondestructive inspection of any two or more of the impact elastic wave method, impact echo method, and ultrasonic method and past incidental information. Further, three or more levels of association may be set between the determination result of the grout filling status for the combination.

  Even at this time, input parameters (waveform numerical data obtained by nondestructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method and incidental information) are also used for reference. After assigning to input parameters (past waveform numerical data obtained by nondestructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method, and past incidental information), the reference input The grout filling situation as an output solution is selected based on the association degree set in the parameter.

  In the example of FIG. 18, the past waveform numerical data detected based on the impact elastic wave, the past waveform numerical data detected based on the impact echo method, and the past detected based on the X-ray transmission method. An example is shown in which three or more levels of association between the combination with the X-ray image and the determination result of the grout filling status for the combination are set.

  In such a case, as shown in FIG. 18, the relevance is the past waveform numerical data h1, h2,... By the impact elastic wave method as the inspection data of the nondestructive inspection, and the past waveform numerical data by the impact echo method. A set of combinations of k1, k2,... and past X-ray images r1, r2,... is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61a, the past waveform numerical data h1 is associated with an association degree of 80%, the past waveform numerical data k1 is associated with an association degree of 30%, and the past X-ray image r1 is associated with 80%. The node 61d is associated with the past waveform numerical data h2 having an association degree of 30%, the past waveform numerical data k1 having an association degree of 70%, and the past X-ray image r2 having an association degree of 90%.

  Similarly, when such association is set, when the process proceeds to step S13, the numerical value data of the impact elastic wave as the input parameter detected in step S11 or extracted in step S12, and An output solution corresponding to the degree of association between the numerical value data of the impact echo waveform and the X-ray image of the X-ray transmission method is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 18 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data h1, the waveform numerical data k1, and the X-ray image r1, the node 61a is associated through the association degree, "Rate 100%" is associated with an association degree of 70%, and "Filling rate 0%" is associated with an association degree of 30%.

  In this way, the input parameters (waveform numerical data of the shock elastic wave method, waveform numerical data of the impact echo method, and X-ray image) are converted into the reference input parameters (waveform numerical data of the past shock elastic wave method, After assigning to the past numerical value data of the impact echo method and the past X-ray image), the grout filling state as an output solution is selected based on the association degree set in the reference input parameter.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the present invention, the database 3 includes the past numerical waveform data obtained by nondestructive inspection of any two or more of the shock elastic wave method, the impact echo method, and the ultrasonic method, and the past X-ray image. Three or more levels of association between the combination and the determination result of the grout filling status for the combination may be set.

  Even at this time, refer to the input parameters (waveform numerical data obtained by nondestructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method and X-ray image). Assigned to input parameters (past waveform numerical data obtained by nondestructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method and past X-ray image) The grout filling situation as an output solution is selected based on the association degree set as the input parameter.

  In the example of FIG. 19, past waveform numerical data detected based on the impact elastic wave, past waveform numerical data detected based on the impact echo method, incidental information regarding the past cable sheath 71, and X-rays An example is shown in which three or more levels of association are set between a combination of a past X-ray image detected based on the transmission method and a grout filling state determination result for the combination.

  In the example illustrated in FIG. 19, the incidental information is configured by arrangement information of the cable sheath 71.

  In this case, as shown in FIG. 19, the relevance is determined by past waveform numerical data h1, h2,..., Past waveform numerical data k1, k2,. A set of combinations of the arrangement information of the cable sheath 71 and the past X-ray images r1, r2,... Is expressed as so-called hidden layer nodes 61a to 61e. In each of the nodes 61a to 61e, a weight for the reference input parameter and a weight for the output solution are set. This weighting is an association degree of three or more levels. For example, in the node 61c, the past waveform numerical data h2 has an association degree of 60%, the past waveform numerical data k2 has an association degree of 60%, and “non-parallel arrangement” as arrangement information of the past cable sheath 71 is associated. The past X-ray image r2 is linked at 50% and 50%.

  Similarly, when such association is set, when the process proceeds to step S13, the numerical value data of the shock elastic wave method as the input parameter detected in step S11 or extracted in step S12 Then, an output solution corresponding to the degree of association between the numerical value data of the impact echo method, the arrangement information of the cable sheath 71, and the X-ray image of the X-ray transmission method is searched. The search program performs an operation of selecting one or more optimum solutions from the output solutions based on the input parameters. In selecting this optimal solution, the association degree shown in FIG. 19 acquired in advance is referred to. For example, when the input parameter is the waveform numerical data h2, the waveform numerical data k2, the “non-parallel arrangement” as the arrangement information of the cable sheath 71, and the X-ray image r2, the relevance is obtained. The node 61c is associated with the node 61c, and the filling rate of 0% is associated with the association degree of 70%.

  In this way, input parameters (waveform numerical data of the impact elastic wave method, waveform numerical data of the impact echo method, arrangement information of the cable sheath 71, and X-ray image) are input to the reference input parameters (past impact elasticity of the past. Wave method waveform numerical data, past impact echo method waveform numerical data, past cable sheath 71 arrangement information, and past X-ray image), and the correlation set in the reference input parameter. The grout filling situation as an output solution is selected based on the degree.

  The search program similarly outputs a solution in step S14 based on the search result.

  In the present invention, in the present invention, the past waveform numerical data obtained by the nondestructive inspection of any two or more of the shock elastic wave method, the impact echo method, and the ultrasonic method are stored in the database 3 and the past cable. Three or more levels of relevance are set for the combination of the incidental information regarding the sheath 71 and the past X-ray image detected based on the X-ray transmission method, and the result of determining the grout filling status for the combination. Also good.

  Even at this time, similarly, input parameters (waveform numerical data obtained by non-destructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method, incidental information, X-ray image) ) Are input parameters for reference (past waveform numerical data obtained by non-destructive inspection of any two or more of shock elastic wave method, impact echo method, ultrasonic method, past incidental information, past X After the assignment to the line image), the grout filling state as the output solution is selected based on the association degree set in the reference input parameter.

  In the above-described embodiments, the shock elastic wave method, the impact echo method, and the ultrasonic method have been described as examples of the nondestructive inspection method capable of acquiring the waveform numerical data. However, any other numerical waveform data can be acquired. Of course, it may be replaced with a nondestructive inspection method. In addition, inspection data acquired by any other nondestructive inspection method may be combined with a nondestructive inspection method in which waveform numerical data is acquired.

  In any nondestructive inspection method, the user can grasp the grout filling state in the cable sheath based on the output discrimination result. Based on the grasped state of grout filling, it is possible to inject grout further in the cable sheath 71 if necessary.

  In particular, according to the present invention, it is possible to easily grasp the grout filling state without requiring special skill. According to the present invention, it is possible to grasp the grout filling state with higher accuracy. Furthermore, by configuring the above-mentioned association degree with artificial intelligence, it is possible to further improve the discrimination accuracy by learning this.

  In particular, it is possible to improve the search accuracy by searching for an optimal solution based on past waveform numerical data obtained by two or more types of nondestructive inspection.

  In particular, by searching for an optimal solution based on past waveform numerical data and past incidental information, it is possible to improve the search accuracy.

  In particular, by searching for an optimal solution based on past waveform numerical data and past X-ray images, the search accuracy can be improved.

  In particular, it is possible to improve the search accuracy by searching for an optimal solution based on past waveform numerical data, past incidental information, and past X-ray images.

  In addition, the present invention is characterized in that an optimum grout filling state determination result is searched for through the association degree set in three or more stages. The association degree can be described by a numerical value of 0 to 100%, for example, but is not limited to this, and may be configured at any stage as long as it can be described by three or more numerical values.

  By searching based on the degree of association represented by numerical values of three or more levels, search and display in descending order of the degree of association in a situation where the determination result of the filling status of a plurality of grouts is selected. Is also possible. In this way, if the user can display to the user in the descending order of the association degree, it is possible to prompt the user to preferentially select the determination result of the more likely grout filling status. On the other hand, even if the result of determining the filling state of the grout with a low association degree can be displayed in the sense of a second opinion, it can be useful when the analysis cannot be performed well with the first opinion.

  In addition to this, according to the present invention, it is possible to make a determination without overlooking the determination result of the grout filling state having an extremely low relevance such as 1%. Even the result of determining the filling status of grout with a very low degree of association is connected as a slight sign and may be useful as a result of determining the filling status of grout once every tens or hundreds of times. This can be alerted to the user.

  Furthermore, according to the present invention, there is an advantage that a search policy can be determined by a method of setting a threshold value by performing a search based on such three or more levels of association. If the threshold value is lowered, it is possible to pick up without omission even if the above-mentioned relevance is 1%, but it is unlikely that the discrimination result of the grout filling state can be detected suitably, and a lot of noise is picked up. In some cases. On the other hand, if the threshold value is increased, there is a high possibility that the determination result of the optimum grout filling status can be detected with high probability, but usually the degree of association is low and it is slewed, but it is tens of times and once every hundred times. In some cases, the preferred solution that comes out may be overlooked. It is possible to decide which to place importance on the basis of the idea on the user side and the system side, but it is possible to increase the degree of freedom in selecting points to place such emphasis.

  In particular, according to the present invention, the waveform image processing step can be omitted by acquiring waveform numerical data by nondestructive inspection. For this reason, it is possible to shorten the processing time until the output solution is output. In addition, according to the present invention, by acquiring waveform numerical data by nondestructive inspection, it is not necessary to have a capacity for storing an imaged waveform, so that the capacity stored in the database 3 can be reduced. Become. In addition, even when the database 3 is updated, the update operation can be easily performed.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between time and amplitude, analysis of the acquired waveform numerical data can be omitted. For this reason, it is possible to transmit the acquired waveform numerical data as it is from the evaluation device 9 to the discrimination device 2. As a result, the processing time until the output solution is output can be shortened.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the frequency, the frequency that cannot be obtained by the numerical data indicating the relationship between time and fluctuation width, which is a complicated relationship, is obtained. It can be a feature amount. Thereby, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the frequency. For this reason, since it can discriminate | determine based on the magnitude relationship with respect to a predetermined frequency and the threshold value of a frequency, for example, it becomes possible to further improve the discrimination | determination precision of a grout filling condition.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the frequency and the amplitude, the numerical value indicating the relationship between the time and the amplitude that is a complicated relationship. The relationship between the frequency and the amplitude that cannot be obtained with data can be used as a feature amount. Thereby, when the waveform numerical data as the input parameter is assigned to the past waveform numerical data as the reference input parameter, the assignment can be performed based on the relationship between the frequency and the amplitude. For this reason, for example, it is possible to make a determination based on a predetermined frequency, a fluctuation width, or a magnitude relationship with respect to these threshold values, so that it is possible to further improve the accuracy of determining the grout filling status.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the time in which the specific frequency region is left and the fluctuation width, the time and fluctuation in which the specific frequency region is left. The relationship with the width can be used as a feature amount. As a result, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the relationship between the time remaining in the specific frequency region and the amplitude. it can. For this reason, for example, it is possible to determine based on a predetermined time, a fluctuation width, and a magnitude relationship with respect to these threshold values, and based on a relation between a time and a fluctuation width in which noises unrelated to the grout filling state are removed. Since it can be determined, it is possible to further improve the accuracy of determining the grout filling status.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between the frequency leaving the specific frequency region and the fluctuation width, the frequency and fluctuation remaining in the specific frequency region are used. The relationship with the width can be used as a feature amount. Thereby, when assigning the waveform numerical data as the input parameter to the past waveform numerical data as the reference input parameter, the assignment can be performed based on the relationship between the frequency and the amplitude leaving the specific frequency region. it can. For this reason, for example, it is possible to discriminate based on a predetermined frequency, an amplitude, and a magnitude relationship with respect to these threshold values, and on the basis of the relationship between the frequency and the amplitude, from which noise and the like that are not related to the grout filling state are removed Since it can be determined, it is possible to further improve the accuracy of determining the grout filling status.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are numerical data indicating the relationship between time and frequency, the relationship between time and frequency can be used as a feature amount. Thereby, when assigning waveform numerical data as an input parameter to past waveform numerical data as a reference input parameter, the assignment can be performed based on the relationship between time and frequency. For this reason, since it can discriminate | determine based on the frequency which considered the time characteristic, for example, it becomes possible to further improve the discrimination | determination precision of a grout filling condition.

  According to the present invention, when the waveform numerical data and the past waveform numerical data are three-dimensional numerical data, it is possible to further improve the search accuracy of the optimum solution.

  Furthermore, in the present invention, the association degree described above may be updated. This update may reflect information provided via a public communication network such as the Internet. If new knowledge about the relationship between the input parameters and the output solution (the result of determining the grout filling status) is discovered through site information or writing that can be obtained from the public communication network, depending on the knowledge Increase or decrease relevance.

  In addition to information based on information that can be obtained from the public communication network, this relevance update is artificially performed by the system side or user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. Alternatively, it may be updated automatically. Artificial intelligence may be used in these update processes.

DESCRIPTION OF SYMBOLS 1 Grout filling condition discrimination system 2 Discriminator 3 Database 7 Structure 8 Nondestructive inspection part 9 Evaluation apparatus 21 Internal bus 23 Display part 24 Control part 25 Operation part 26 Communication part 27 Search part 28 Storage part 61 Node 71 Cable sheath

Claims (10)

In the grout filling status discrimination system for discriminating the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A database in which the past waveform numerical data obtained by the non-destructive inspection for the cable sheath and the correlation degree of the three or more stages of the determination result of the grout filling state with respect to the past waveform numerical data are stored in advance;
An input means for inputting waveform numerical data obtained by performing a non-destructive inspection on a cable sheath that newly determines the grout filling status;
With reference to the degree of association stored in the database, and based on the waveform numerical data input via the input means, and a determination means for determining the grout filling status in the cable sheath ,
The database further stores in advance three or more levels of association between past incidental information about the cable sheath and the past waveform numerical data, and the determination result of the grout filling status for the combination,
The input means is further inputted with incidental information on the cable sheath for newly determining the grout filling status,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. A grout filling status determination system comprising: one or more of concrete information to be performed, test condition information for nondestructive inspection, surface state information of a PC structure, and configuration information in the PC structure . In the grout filling status discrimination system for discriminating the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A database in which the past waveform numerical data obtained by the non-destructive inspection for the cable sheath and the correlation degree of the three or more stages of the determination result of the grout filling state with respect to the past waveform numerical data are stored in advance;
An input means for inputting waveform numerical data obtained by performing a non-destructive inspection on a cable sheath that newly determines the grout filling status;
With reference to the degree of association stored in the database, and based on the waveform numerical data input via the input means, and a determination means for determining the grout filling status in the cable sheath,
The database further stores in advance three or more levels of association between past incidental information about the cable sheath and the past waveform numerical data, and the determination result of the grout filling status for the combination,
The input means is further inputted with incidental information on the cable sheath for newly determining the grout filling status,
The past waveform numerical data and the waveform numerical data are numerical data obtained by a nondestructive inspection of any one of a shock elastic wave method, an impact echo method, and an ultrasonic method,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. Including one or more of concrete information to be performed, test condition information for nondestructive inspection, surface condition information of PC structure, and configuration information in PC structure
Grout filling status discrimination system . The database includes three or more levels of correlation between the combination of the past numerical waveform data and the past inspection data obtained by the X-ray transmission method with respect to the cable sheath, and the determination result of the grout filling state for the combination. Degree is pre-stored,
The grout filling state according to claim 1 or 2 , wherein the input means receives inspection data obtained by performing an X-ray transmission method on a cable sheath for newly determining the grout filling state. Discriminating system. In the grout filling status discrimination system for discriminating the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A database in which the past waveform numerical data obtained by the non-destructive inspection for the cable sheath and the correlation degree of the three or more stages of the determination result of the grout filling state with respect to the past waveform numerical data are stored in advance;
An input means for inputting waveform numerical data obtained by performing a non-destructive inspection on a cable sheath that newly determines the grout filling status;
With reference to the degree of association stored in the database, and based on the waveform numerical data input via the input means, and a determination means for determining the grout filling status in the cable sheath,
The database includes past incidental information on the cable sheath, a combination of the past waveform numerical data obtained by nondestructive inspection of any two or more of the shock elastic wave method, the impact echo method, and the ultrasonic method, The degree of association of three or more stages with the discrimination result of the grout filling status for the combination is stored in advance,
The input means is the waveform numerical value obtained by performing two or more kinds of non-destructive inspections of the shock elastic wave method, the impact echo method, and the ultrasonic method on the cable sheath for newly determining the grout filling state. Data and incidental information about the cable sheath are entered,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. Including one or more of concrete information to be performed, test condition information for nondestructive inspection, surface condition information of PC structure, and configuration information in PC structure
Grout filling status discrimination system. The database includes a combination of two or more kinds of the past waveform numerical data and past inspection data obtained by the X-ray transmission method with respect to the cable sheath, and a determination result of a grout filling state with respect to the combination. The degree of association over the level is stored in advance,
5. The grout filling state determination according to claim 4, wherein the input means further receives inspection data obtained by performing an X-ray transmission method on a cable sheath for newly determining a grout filling state. system. In the grout filling status determination program for determining the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A degree-of-association acquisition step of acquiring in advance three or more degrees of association between past waveform numerical data obtained by non-destructive inspection of the cable sheath and a grout filling status determination result for the past waveform numerical data;
An input step in which waveform numerical data obtained by performing a nondestructive inspection on a cable sheath that newly determines the grout filling state is input,
With reference to the association degree acquired in the association degree acquisition step, based on the waveform numerical data input through the input step, the computer executes a determination step for determining the grout filling state in the cable sheath ,
In the association degree acquisition step, a combination of the past incidental information on the cable sheath and the past waveform numerical data, and a degree of association of three or more stages of the determination result of the grout filling state for the combination is acquired in advance.
In the above input step, additional information relating to the cable sheath that newly determines the grout filling status is further input,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. A grout filling status determination program including one or more of concrete information to be performed, test condition information for nondestructive inspection, surface state information of a PC structure, and configuration information in the PC structure . In the grout filling status determination program for determining the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A degree-of-association acquisition step of acquiring in advance three or more degrees of association between past waveform numerical data obtained by non-destructive inspection of the cable sheath and a grout filling status determination result for the past waveform numerical data;
An input step in which waveform numerical data obtained by performing a nondestructive inspection on a cable sheath that newly determines the grout filling state is input,
With reference to the association degree acquired in the association degree acquisition step, based on the waveform numerical data input through the input step, the computer executes a determination step for determining the grout filling state in the cable sheath,
In the association degree acquisition step, a combination of the past incidental information on the cable sheath and the past waveform numerical data, and a degree of association of three or more stages of the determination result of the grout filling state for the combination is acquired in advance.
In the above input step, additional information relating to the cable sheath that newly determines the grout filling status is further input,
The past waveform numerical data and the waveform numerical data are numerical data obtained by a nondestructive inspection of any one of a shock elastic wave method, an impact echo method, and an ultrasonic method,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. Including one or more of concrete information to be performed, test condition information for nondestructive inspection, surface condition information of PC structure, and configuration information in PC structure
A grout filling status discriminating program. In the association degree acquisition step, there are three stages: a combination of the past waveform numerical data and past inspection data obtained by the X-ray transmission method with respect to the cable sheath, and a grout filling state determination result for the combination. The above association degree is acquired in advance,
8. The grout filling state according to claim 6 or 7 , wherein in the input step, inspection data obtained by performing an X-ray transmission method on a cable sheath for newly determining the grout filling state is input. Discriminator program. In the grout filling status determination program for determining the grout filling status in the cable sheath of the PC structure by non-destructive inspection,
A degree-of-association acquisition step of acquiring in advance three or more degrees of association between past waveform numerical data obtained by non-destructive inspection of the cable sheath and a grout filling status determination result for the past waveform numerical data;
An input step in which waveform numerical data obtained by performing a nondestructive inspection on a cable sheath that newly determines the grout filling state is input,
With reference to the association degree acquired in the association degree acquisition step, based on the waveform numerical data input through the input step, the computer executes a determination step for determining the grout filling state in the cable sheath,
In the association degree acquisition step, past incidental information on the cable sheath and a combination of the past waveform numerical data obtained by nondestructive inspection of any two or more of the shock elastic wave method, impact echo method, and ultrasonic method And the degree of association of three or more stages with the determination result of the grout filling status for the combination in advance,
In the input step, the waveform numerical value obtained by performing a nondestructive inspection of any two or more of the impact elastic wave method, the impact echo method, and the ultrasonic method on the cable sheath that newly determines the grout filling state Data and incidental information about the cable sheath are entered,
The past incidental information and the incidental information include cable sheath configuration information, cable sheath arrangement information, tension material information regarding the tension material provided in the PC structure, grout information regarding the grout, and placement in the PC structure. Including one or more of concrete information to be performed, test condition information for nondestructive inspection, surface condition information of PC structure, and configuration information in PC structure
A grout filling status discriminating program. In the association degree acquisition step, a combination of two or more types of the past waveform numerical data and past inspection data obtained by the X-ray transmission method for the cable sheath, and a determination result of the grout filling state for the combination And obtain the degree of relevance of three or more stages in advance,
10. The grout filling status determination according to claim 9 , wherein in the input step, inspection data obtained by performing an X-ray transmission method on a cable sheath for newly determining a grout filling status is further input. program.

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