CN111563289A - Slip crack surface position prediction method, system and medium based on slope internal vibration - Google Patents
Slip crack surface position prediction method, system and medium based on slope internal vibration Download PDFInfo
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Abstract
The invention relates to the technical field of slope engineering and discloses a slip surface position prediction method, a system and a medium based on slope internal vibration. Therefore, the method for predicting the position of the slip fracture surface can avoid damage to the slope structure, and improves the prediction efficiency and accuracy because no operation such as construction or reconnaissance and complex calculation are needed.
Description
Technical Field
The invention relates to the technical field of slope engineering, in particular to a method, a system and a medium for predicting a position of a slip crack surface based on internal vibration of a slope.
Background
With the continuous development of economy in China, the scale of projects such as infrastructure construction, energy development and the like is continuously enlarged, a large number of filling and excavating side slopes are generated, and a large number of slope stability problems are caused by the natural side slopes and the artificial side slopes. Among the slope stability problems, the dynamic stability problem caused by earthquake is characterized by wide distribution, large quantity and great harm, which often threatens the life and property safety of people and brings great economic loss.
The stability analysis of the slope is carried out on the basis of the determined slip surface, so that the determination of the nature and the position of the slip surface of the earthquake slope under the action of power is very important. For the prediction of the position of the slip surface, an engineer usually assumes a series of slip surfaces according to engineering experience after an earthquake, and then utilizes limit balance analysis to search and obtain the slope slip surface, but the prediction result has great randomness and instability by adopting the mode and depending on the engineering experience of the engineer; in addition, after an earthquake, the position of the slip crack surface is judged by drilling holes, digging exploration grooves and the like, but the exploration means has great structural damage to the side slope, and the drilling holes are drilled on the slope body which is damaged to a certain extent, so that great potential safety hazards exist.
Therefore, there is a need for a slip surface position prediction method that does not rely on the experience of engineers and does not require post-earthquake drilling trenching for exploration.
Disclosure of Invention
The invention aims to: the method for predicting the corresponding slip crack surface in the slope at the moment of the dynamic damage of the slope earthquake can avoid damage to the slope structure and greatly improve the prediction efficiency and accuracy.
In order to achieve the above purpose, the technical solution adopted by the invention to solve the technical problem is as follows: a slip crack surface position prediction method based on slope internal vibration comprises the following steps:
s1: performing EMD on time-frequency data acquired by accelerometers embedded at different positions in the slope to obtain IMF components of each order of each time-frequency data;
s2: selecting IMF components with higher amplitude and richer frequency information in each order of IMF components of each time-frequency data, and performing Hillbert transformation on the IMF components to obtain corresponding Hillbert marginal spectrums;
s3: performing marginal spectrum comparison analysis based on Hillbert marginal spectrum peak curves corresponding to the time-frequency data of the two accelerometers at adjacent positions, and marking the position of a damage point;
s4: and predicting curved surfaces passing through all damage points in the side slope geological model space coordinate system according to the coordinates of each accelerometer in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
According to a specific embodiment, step S3 of the method for predicting a position of a slip crack surface based on internal vibration of a slope of the present invention specifically includes:
dividing a plurality of data acquisition surfaces from the slope surface of the side slope to the direction of filling soil in the side slope according to the embedded position of each accelerometer in the side slope, and dividing a plurality of data acquisition sequences from the slope toe to the direction of the slope top on each data acquisition surface;
and sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence, and marking the position of the damage point corresponding to each data acquisition sequence.
According to a specific embodiment, step S2 of the method for predicting a position of a slip crack surface based on internal vibration of a slope according to the present invention is specifically: and separating a plurality of orders of IMF components with frequencies distributed from high to low, and selecting IMF components with higher amplitude and richer frequency information from the first 4 orders of IMF components of each time-frequency data.
In one aspect of the present invention, the present invention further provides a slip crack surface position prediction system, which includes a plurality of accelerometers buried at different positions inside a slope, and a data processing device; the data processing device is used for acquiring time-frequency data acquired by each accelerometer buried in a side slope;
further, the data processing apparatus further includes:
the EMD decomposition module is used for performing EMD decomposition on the time-frequency data acquired by the accelerometers embedded at different positions in the slope to obtain each-order IMF component of each time-frequency data;
the Hillbert transformation module is used for selecting IMF components with higher amplitude and richer frequency information from all orders of IMF components of each time-frequency data, and performing Hillbert transformation on the IMF components to obtain corresponding Hillbert marginal spectrums;
the damage point marking module is used for comparing and analyzing the marginal spectrum according to Hillbert marginal spectrum peak curves corresponding to the time frequency data of the two accelerometers at adjacent positions and marking the position of a damage point;
and the prediction module is used for predicting curved surfaces passing through all damage points in the side slope geological model space coordinate system according to the coordinates of the accelerometers in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
According to a specific embodiment, in the sliding fracture surface position prediction system of the present invention, the damage point marking module further includes:
the dividing submodule is used for dividing a plurality of data acquisition surfaces from the slope surface of the side slope to the direction in the side slope according to the embedded position of each accelerometer in the side slope, and dividing a plurality of data acquisition sequences from the slope foot to the direction of the slope top on each data acquisition surface;
and the data acquisition sequence damage point marking submodule is used for sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence and marking the position of the damage point corresponding to each data acquisition sequence.
According to a specific embodiment, in the slip crack surface position prediction system of the present invention, the accelerometers of the data acquisition sequence inside the slope fill are all located on the same vertical line.
According to a specific embodiment, in the system for predicting the position of the slip fracture surface, the accelerometer is installed in a box body, and the total weight of the whole box body is consistent with the weight of the side slope accumulated soil with the same volume.
According to a specific embodiment, in the slip crack surface position prediction system, an accelerometer positioned at the top of a side slope is fixed by epoxy resin.
According to a specific embodiment, in the slip crack surface position prediction system of the present invention, the accelerometer is a three-axis accelerometer.
In a specific implementation aspect, the invention further provides a readable storage medium, on which one or more programs are stored, wherein the one or more programs, when executed by one or more processors, implement the slope internal vibration-based slip crack surface position prediction method of the invention.
In summary, compared with the prior art, the invention has the beneficial effects that:
according to the method for predicting the position of the slip crack surface based on the internal vibration of the side slope, EMD decomposition and Hillbert transformation are sequentially carried out on time frequency data acquired by accelerometers embedded at different positions in the side slope to obtain a Hillbert marginal spectrum corresponding to each time frequency data, marginal spectrum comparison analysis is carried out based on Hillbert marginal spectrum peak curves corresponding to the time frequency data of two accelerometers at adjacent positions to mark the positions of all damage points, and finally, curved surfaces passing through all the damage points in a side slope geological model space coordinate system are predicted according to the coordinates of the accelerometers in the side slope geological model space coordinate system, and the curved surfaces are the slip crack surfaces. Therefore, the method for predicting the position of the slip fracture surface can avoid damage to the slope structure, is short in time consumption due to the fact that operation such as construction or investigation is not needed, improves prediction efficiency, does not need complex calculation, represents earthquake damage characteristics inside the slope in an energy angle, and improves accuracy.
Description of the drawings:
FIG. 1 is a three-dimensional map of an accelerometer inside a slope of the present invention;
FIG. 2 is a schematic view of a slope top embedded accelerometer according to the present invention;
FIG. 3 is a schematic diagram of a slope geological model coordinate system of the present invention;
FIG. 4 is a schematic representation of a predicted three-dimensional slip surface of the present invention;
FIG. 5 is a two-dimensional map of an accelerometer inside a slope of the present invention;
FIG. 6 is a schematic representation of a predicted two-dimensional slip surface of the present invention;
FIG. 7 is a graph showing a distribution of accelerometers inside a slope according to an example of the present invention;
FIGS. 8-11 are graphs of the IMF component of the first 4 th order and its instantaneous frequency, respectively, in an example of the present invention;
FIG. 12 is a graph of a change in the marginal spectrum peak curve of each accelerometer according to an example of the present invention;
FIG. 13 is a graph II of the variation of the marginal spectrum peak curve of each accelerometer in the experimental example of the present invention;
FIG. 14 is a schematic view of a predicted slip surface in an experimental example of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
The method for predicting the position of the slip crack surface based on the internal vibration of the side slope is implemented on the basis of a data acquisition network of an accelerometer embedded in the side slope. Usually, the data acquisition network is built by embedding accelerometers in the horizontal and vertical directions on the slope surface of the side slope, filling soil in the side slope, the slope bottom of the side slope and the slope top of the side slope respectively based on the actual engineering side slope size. According to literature research, under the action of an earthquake, the position of earthquake damage in the slope is about 0.3-0.6 of relative elevation, the data can be more accurate only by closely arranging accelerometers, and if the slope height of the slope is higher, the accelerometers can be arranged according to the actual size.
For example: when the slope height of the side slope is less than 5m, the accelerometers are respectively arranged at 0, 0.33H, 0.66H and H away from the slope height; and when the slope height of the side slope is more than 5m, the accelerometers are respectively arranged at 0, 0.25H, 0.5H, 0.75H and H away from the slope height.
In fact, in order to ensure the accuracy of the predicted slip surface, a large number of accelerometers may be embedded, that is, more data acquisition surfaces are established, and the accelerometers in each data acquisition surface are more dense, so as to improve the accuracy of the predicted slip surface.
As shown in fig. 1, accelerometers a 1-a 16, accelerometers B1-B16 and accelerometers C1-C16 are arranged in the slope, and respectively form a data acquisition plane a, a data acquisition plane B and a data acquisition plane C.
And regarding the data acquisition surface A, accelerometers A1-A4 are sequentially arranged at a position 10cm away from the slope surface of the side slope from the slope toe to the top of the slope, and the accelerometers are transversely arranged. Then, the rest accelerometers are horizontally arranged along the accelerometers A1-A4 on the slope surface, namely the accelerometers A1, A5, A9 and A13 are on the same horizontal line, the accelerometers A2, A6, A10 and A14 are on the same horizontal line, the accelerometers A3, A7, A11 and A15 are on the same horizontal line, and the accelerometers A4, A8, A12 and A16 are on the same horizontal line. And the accelerometers A5-A8 in the slope filling are located on the same vertical line, the accelerometers A9-A12 are located on the same vertical line, and the accelerometers A13-A16 are located on the same vertical line. The distance between the mounting position of the accelerometer A8 at the top of the filled soil and the accelerometer A4 at the top of the previous slope is 50 cm.
In this way, a first data acquisition sequence (a1, a2, A3, a4), a second data acquisition sequence (a5, A6, a7, A8), a third data acquisition sequence (a9, a10, a11, a12), and a fourth data acquisition sequence (a13, a14, a15, a16) constitute the data acquisition plane a.
Similarly, the accelerometers B1-B16 are arranged in the above manner, and a first data acquisition sequence (B1, B2, B3, B4), a second data acquisition sequence (B5, B6, B7, B8), a third data acquisition sequence (B9, B10, B11, B12) and a fourth data acquisition sequence (B13, B14, B15, B16) of the data acquisition plane B are formed. The accelerometers C1-C16 are arranged in the mode, and a first data acquisition sequence (C1, C2, C3 and C4), a second data acquisition sequence (C5, C6, C7 and C8), a third data acquisition sequence (C9, C10, C11 and C12) and a fourth data acquisition sequence (C13, C14, C15 and C16) of the data acquisition plane C are formed.
And the data acquisition surface B is parallel to the data acquisition surface A, the distance between the surfaces is 50-80 cm, and the data acquisition surface C is parallel to the data acquisition surface B, and the distance between the surfaces is 50-80 cm.
It should be noted that, before the accelerometer is buried, in order to avoid the influence of loop noise on the measurement, on one hand, the accelerometer is a three-axis accelerometer, which has the characteristics of high sampling frequency, fast response, vector acquired data and the like, is particularly suitable for the vibration condition, and is durable when buried in the rock-soil body; on the other hand, the accelerometer needs to be fixed in an insulated square box, the weight of the accelerometer and the box is ensured to be consistent with the weight of the soil with the same volume, the accelerometer and the box are tightly attached, no gap is left, the cooperative motion of the accelerometer and the peripheral rock-soil body is determined, and the measured data is accurate. And then sealing the fixed accelerometer insulation square box, horizontally placing the fixed accelerometer insulation square box, and ensuring that the bottom surface of the square box is completely contacted with the filler. As shown in fig. 2, accelerometers a4, A8, a12 and the like on the top of the slope are all fixed by epoxy resin to improve the test accuracy.
When the side slope encounters a destructive earthquake, data are collected through the data collection network, and time-frequency data monitored by each accelerometer are analyzed and processed through the data processing device, so that the method for predicting the position of the slip crack surface based on the internal vibration of the side slope can be realized.
The invention relates to a slip crack surface position prediction method based on slope internal vibration, which comprises the following steps:
s1: and performing EMD on the time-frequency data acquired by the accelerometers embedded at different positions in the slope to obtain each-order IMF component of each time-frequency data. Specifically, the EMD decomposition is carried out by using MATLAB software programming, and the superposed waves are removed through the EMD decomposition on one hand, and the waveform is more symmetrical on the other hand. After EMD decomposition, IMF components of each order of each time frequency data and an instantaneous frequency chart thereof can be obtained.
S2: and selecting IMF components with higher amplitude and richer frequency information from the IMF components of each order of each time frequency data, and performing Hillbert transformation on the IMF components to obtain a corresponding Hillbert marginal spectrum. Specifically, after separating a plurality of order IMF components whose frequencies are distributed from high to low, an IMF component with a higher amplitude and richer frequency information is generally selected from the first 4 order IMF components of each time-frequency data.
Further, it is known from past experience that IMF2 having high recognition resolution is generally selected. And then Hillbert transformation is carried out on the selected IMF component by using a derivation formula program of MATLAB software to obtain Hillbert marginal spectrums corresponding to the accelerometers, and corresponding marginal spectrum peaks are screened out.
The specific Hillbert transformation operation process is as follows:
and (3) performing Hillbert transformation on the IMF component c (t):
where PV represents the cauchy principle component value, the analytic signal z (t) is constructed accordingly:
z(t)＝c(t)+jH[c(t)]＝a(t)ejθ(T)(2)
wherein:
for instantaneous amplitude, θ (t) ═ arctan [ H [ c (t)]/c(t)]Is the instantaneous phase.
The instantaneous frequency is defined on the basis of equation (2):
further extrapolation to the expression for the Hillbert spectrum is:
wherein a in the formula (4)i(t) is the i-th order IMF at time t and frequency ωiThe corresponding instantaneous amplitude, if H (ω, t) is integrated over time, the Hillbert marginal spectrum can be obtained:
s3: and performing marginal spectrum comparison analysis based on Hillbert marginal spectrum peak curves corresponding to the time-frequency data of the two accelerometers at adjacent positions, and marking the position of the damage point.
Specifically, according to the embedding position of each accelerometer in the side slope, a plurality of data acquisition surfaces are divided from the slope surface of the side slope to the direction in the side slope, and a plurality of data acquisition sequences are divided from the slope toe to the direction of the slope top on each data acquisition surface; and sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence, and marking the position of the damage point corresponding to each data acquisition sequence.
In fact, the principle of the marginal spectrum peak analysis is as follows: if the marginal spectrum peak value curve of each accelerometer in a data acquisition sequence from the slope toe to the slope top basically meets the linear growth rule and the amplitude change is small, the situation that the interior of the side slope is not damaged by earthquake damage in the process is shown. And if the marginal spectrum peak curve has a sudden change (the change amplitude of the marginal spectrum peak value of a certain accelerometer is large), the sudden change indicates that the earthquake damage occurs at a certain part in the side slope. Therefore, by the marginal spectrum peak comparison analysis, it can be determined between which two adjacent accelerometers the earthquake damage is located.
For example, in practice, the edge spectrum comparison analysis of the first data acquisition sequence (a1, a2, A3, a4), the second data acquisition sequence (a5, A6, a7, A8), the third data acquisition sequence (a9, a10, a11, a12), and the fourth data acquisition sequence (a13, a14, a15, a16) shown in fig. 1 is performed sequentially.
First, performing marginal spectrum comparison analysis corresponding to two accelerometers at adjacent positions in the first data acquisition sequence (a1, a2, A3, a4), namely performing marginal spectrum comparison analysis of the accelerometers a1 and a2 first, if no damage occurs between the accelerometer a1 and the accelerometer a2, continuing to perform marginal spectrum comparison analysis of the accelerometers a2 and A3, and so on until the position of a damage point in the first data acquisition sequence (a1, a2, A3, a4) is found. If a lesion occurs between accelerometer a1 and accelerometer a2, the analysis of marginal spectrum comparisons between subsequent adjacent accelerometers of the first data acquisition sequence (a1, a2, A3, a4) is not continued. That is, the marginal spectrum comparison analysis corresponding to two accelerometers at adjacent positions in the second data acquisition sequence (a5, a6, a7, A8) is started. And analogizing in turn until the positions of the damage points corresponding to the four data acquisition sequences in the data acquisition surface A in FIG. 1 are determined.
Similarly, the positions of the damage points corresponding to the four data acquisition sequences in the data acquisition plane a, the data acquisition plane B and the data acquisition plane C in fig. 1 are determined in sequence by the above method.
S4: and predicting the curved surfaces passing through all the damage points in the side slope geological model space coordinate system according to the coordinates of each accelerometer in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
The side slope geological model space coordinate system is shown in fig. 3, and can be directly established according to the measured dimension of the side slope and the installation distance of the accelerometer. By using the time-frequency data acquired by the data acquisition network shown in fig. 1, the three-dimensional slip crack surface shown in fig. 4 can be predicted by the slip crack surface position prediction method based on the internal vibration of the slope.
In addition, on the premise that the precision of the slip surface can meet the requirements of general slope engineering, in order to reduce cost investment and improve prediction speed, the influence on the y direction of the slope geological model space coordinate system can be ignored, that is, a data acquisition network as shown in fig. 5 is constructed, the data acquisition network only comprises one data acquisition surface, and the data acquisition surface comprises a first data acquisition sequence (a1, a2, A3, a4), a second data acquisition sequence (a5, A6, a7, A8), a third data acquisition sequence (a9, a10, a11, a12) and a fourth data acquisition sequence (a13, a14, a15, a 16). Then, by using the time-frequency data collected by the data collection network shown in fig. 5, the method for predicting the position of the slip crack surface based on the internal vibration of the slope can predict the two-dimensional slip crack surface shown in fig. 6.
Therefore, the method for predicting the position of the slip crack surface based on the internal vibration of the side slope can avoid damage to the side slope structure, is short in time consumption due to the fact that operation such as construction or investigation is not needed, improves prediction efficiency, does not need complex calculation, represents the internal vibration damage characteristics of the side slope in terms of energy, and improves accuracy.
In order to further explain the method for predicting the position of the slip crack surface based on the internal vibration of the side slope, the method is described in detail by combining the experimental example:
as shown in fig. 6, the accuracy of predicting the internal slip surface of the slope according to the present invention is verified by using a vibration table model test, taking a gravity retaining wall to reinforce a roadbed slope as an example. In order to keep consistent with data measured by a vibration table test, accelerometers A1-A6 are distributed on the vibration table test, and a geological model of the side slope is manufactured according to the actual engineering side slope size and the similarity ratio of 1: 6. The accelerometers A1-A3 are respectively placed at a position 20cm away from the back of the retaining wall, the accelerometers A1-A3 are located on the same vertical line, and a first data acquisition sequence (A1, A2 and A3) is formed; the accelerometers A4-A6 are respectively placed at a position 70cm away from the back of the retaining wall, the accelerometers A4-A6 are positioned on the same vertical line, and a second data acquisition sequence (A4, A5 and A6) is formed; further, the accelerometers a1 and a4, a2 and a5, and A3 and a6 are located on the same horizontal line, respectively.
In the experiment, Wenchuan clinostat strong seismic records with similar conditions to the place where the model is located are selected, compression treatment is carried out according to the similar law, the time compression ratio is 1:2.45, and the duration of the treated seismic waves is 65 s. Consider seismic acceleration input from the X direction (horizontal vertical line) and the Z direction (vertical line).
EMD decomposition is performed on seismic waves recorded by an input wenchuan clinolong station, the decomposition results are shown in fig. 7-10, and the first four-order Intrinsic Mode Function (IMF) almost includes most amplitude components of the original signals.
By comparing the decomposition results of the first four-order empirical modes, the IMF2 component is found to have a high amplitude and rich frequency information, and therefore, the IMF2 component is selected for marginal spectrum analysis.
Performing Hillbert transformation on the IMF components to obtain Hillbert marginal spectrums corresponding to the accelerometers A1-A6, screening out marginal spectrum peak values, sorting the Hillbert marginal spectrum peak values of each point on a monitoring sequence, and drawing a graph as shown in FIGS. 11 and 12.
From the marginal spectrum theorem, the first data acquisition sequence (a1, a2, A3) was chosen for analysis, and it can be seen that the marginal spectral peak of accelerometer a1 increases substantially linearly with increasing PGA, reaching a maximum at 0.9g, indicating no model collapse during this process, whereas accelerometer a2 experiences significant amplitude fluctuations at 0.4g, and the peak at this measurement point is lower than the peak of accelerometer a1 indicating that a seismic injury has occurred under seismic excitation.
When the second data acquisition sequence (a4, a5, a6) is selected for analysis, it can be seen that the change amplitude of the accelerometer a5 is not large, and basically meets the rule of linear growth, indicating that the model is not cracked in the process, and the peak value of the accelerometer a6 after the PGA is 0.4g is lower than that of the accelerometer a5, which indicates that the soil in the area is damaged.
Thus, it can be concluded that the sudden change in the marginal spectrum peak is manifested as a failure of the slope soil near the back wall between accelerometers a1 and a2 and a breakdown of the soil mass above the slope between accelerometers a5 and a6, and the moment of damage should be between 0.4g and 0.7 g. In addition, soil beneath accelerometer A3 is damaged, resulting in incomplete transmission of seismic energy to accelerometer A3.
Then, it is not difficult to find that the position of the slip plane is between the accelerometers a1 and a2 in the first data acquisition sequence (a1, a2, A3), and between the accelerometers a5 and a6 in the second data acquisition sequence (a4, a5, a6), and the predicted damage location points are connected into a curve, i.e., the slip plane of the seismoelectric slope, as shown in fig. 13.
Finally, according to the test result of the vibrating table, the fracture angle of the post-earthquake slip crack surface of the roadbed slope is 48 degrees, and the fracture angle which can be further obtained by combining the two-dimensional slip crack surface obtained in the test example is 44 degrees, is closer to the result of the vibrating table, has the error within 10 percent, and can meet the precision requirement of engineering. It should be noted that in this experimental example, since the arrangement of the accelerometers of the vibration table only involves one data acquisition plane, including two data acquisition sequences, the arrangement is sparse, which results in a rough result, and if the arrangement of the accelerometers is denser, the results are more accurate.
In one aspect of the present invention, the present invention further provides a slip crack surface position prediction system, which includes a plurality of accelerometers buried at different positions inside a slope, and a data processing device; the data processing device is used for acquiring time-frequency data acquired by each accelerometer buried in the side slope.
Further, the data processing apparatus further includes:
the EMD decomposition module is used for performing EMD decomposition on the time-frequency data acquired by the accelerometers embedded at different positions in the slope to obtain each-order IMF component of each time-frequency data;
the Hillbert transformation module is used for selecting IMF components with higher amplitude and richer frequency information from all orders of IMF components of each time-frequency data, and performing Hillbert transformation on the IMF components to obtain corresponding Hillbert marginal spectrums;
the damage point marking module is used for comparing and analyzing the marginal spectrum according to Hillbert marginal spectrum peak curves corresponding to the time frequency data of the two accelerometers at adjacent positions and marking the position of a damage point;
and the prediction module is used for predicting curved surfaces passing through all damage points in the side slope geological model space coordinate system according to the coordinates of the accelerometers in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
In an implementation manner, in the sliding fracture surface position prediction system of the present invention, the damage point marking module further includes:
the division submodule is used for dividing a plurality of data acquisition surfaces from the slope surface of the side slope to the direction in the side slope according to the embedded position of each accelerometer in the side slope, and dividing a plurality of data acquisition sequences from the slope foot to the direction of the slope top on each data acquisition surface;
and the data acquisition sequence damage point marking submodule is used for sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence and marking the position of the damage point corresponding to each data acquisition sequence.
It should be noted that the data processing device in the slip fracture surface position prediction system of the present invention may be a geological monitoring management platform, or may be a computer located at a monitoring station.
It should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described apparatus is merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the communication connection between the modules may be an indirect coupling or communication connection through some interfaces, devices or units, and may be electrical or in other forms.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A slip crack surface position prediction method based on slope internal vibration is characterized by comprising the following steps:
s1: performing EMD on time-frequency data acquired by accelerometers embedded at different positions in the slope to obtain IMF components of each order of each time-frequency data;
s2: selecting IMF components with higher amplitude and richer frequency information in each order of IMF components of each time-frequency data, and performing Hillbert transformation on the IMF components to obtain corresponding Hillbert marginal spectrums;
s3: performing marginal spectrum comparison analysis based on Hillbert marginal spectrum peak curves corresponding to the time-frequency data of the two accelerometers at adjacent positions, and marking the position of a damage point;
s4: and predicting curved surfaces passing through all damage points in the side slope geological model space coordinate system according to the coordinates of each accelerometer in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
2. The method for predicting the position of a slip crack surface based on internal vibration of a slope according to claim 1, wherein the step S3 specifically comprises:
dividing a plurality of data acquisition surfaces from the slope surface of the side slope to the direction of filling soil in the side slope according to the embedded position of each accelerometer in the side slope, and dividing a plurality of data acquisition sequences from the slope toe to the direction of the slope top on each data acquisition surface;
and sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence, and marking the position of the damage point corresponding to each data acquisition sequence.
3. The method for predicting the position of a slip crack surface based on internal vibration of a slope according to claim 2, wherein the step S2 is specifically as follows: and separating a plurality of orders of IMF components with frequencies distributed from high to low, and selecting IMF components with higher amplitude and richer frequency information from the first 4 orders of IMF components of each time-frequency data.
4. A slip crack surface position prediction system is characterized by comprising a plurality of accelerometers embedded in different positions in a side slope and a data processing device; the data processing device is used for acquiring time-frequency data acquired by each accelerometer buried in a side slope;
further, the data processing apparatus further includes:
the EMD decomposition module is used for performing EMD decomposition on the time-frequency data acquired by the accelerometers embedded at different positions in the slope to obtain each-order IMF component of each time-frequency data;
the Hillbert transformation module is used for selecting IMF components with higher amplitude and richer frequency information from all orders of IMF components of each time-frequency data, and performing Hillbert transformation on the IMF components to obtain corresponding Hillbert marginal spectrums;
the damage point marking module is used for comparing and analyzing the marginal spectrum according to Hillbert marginal spectrum peak curves corresponding to the time frequency data of the two accelerometers at adjacent positions and marking the position of a damage point;
and the prediction module is used for predicting curved surfaces passing through all damage points in the side slope geological model space coordinate system according to the coordinates of the accelerometers in the side slope geological model space coordinate system, wherein the curved surfaces are slip surfaces.
5. The slip surface location prediction system of claim 4, wherein the damage point labeling module further comprises:
the dividing submodule is used for dividing a plurality of data acquisition surfaces from the slope surface of the side slope to the direction in the side slope according to the embedded position of each accelerometer in the side slope, and dividing a plurality of data acquisition sequences from the slope foot to the direction of the slope top on each data acquisition surface;
and the data acquisition sequence damage point marking submodule is used for sequentially carrying out comparison and analysis on the marginal spectrums corresponding to the two accelerometers at the adjacent positions in each data acquisition sequence and marking the position of the damage point corresponding to each data acquisition sequence.
6. The slip fracture surface location prediction system of claim 5, wherein the data acquisition sequences inside slope fill have accelerometers all located on the same vertical line.
7. A slip surface position prediction system according to any of claims 4 to 6 wherein the accelerometer is mounted within a casing and the total weight of the entire casing is the same as the weight of the same volume of side slope mound.
8. The slip face position prediction system of claim 7, wherein the accelerometer at the top of the side slope is secured with epoxy.
9. The slip plane position prediction system of claim 7, wherein the accelerometer is a three-axis accelerometer.
10. A readable storage medium on which one or more programs are stored, wherein the one or more programs, when executed by one or more processors, implement the method for slope internal shock based slip crack surface location prediction according to any one of claims 1-3.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116046307A (en) * | 2022-12-12 | 2023-05-02 | 中铁西北科学研究院有限公司 | Identification method suitable for earthquake damage mode of vibrating table with tunnel slope |
| CN116905297A (en) * | 2023-09-12 | 2023-10-20 | 大秦铁路股份有限公司大同工务段 | Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020023364A1 (en) * | 2000-07-08 | 2002-02-28 | Robert Arnold | Rubber stamp positioning device |
| CN103135128A (en) * | 2013-01-31 | 2013-06-05 | 重庆大学 | Three-dimensional slope stability prediction method under earthquake load effect |
| CN104182646A (en) * | 2014-09-01 | 2014-12-03 | 重庆大学 | Three dimensional earthquake side slope landslide occurring time forecasting method based on slide displacement analysis |
| CN105677961A (en) * | 2016-01-05 | 2016-06-15 | 江西理工大学 | Method for constructing side-slope three-dimensional slip crack face model |
| CN105787277A (en) * | 2016-03-02 | 2016-07-20 | 江西理工大学 | Three-dimensional critical slip crack surface search method for slope |
| CN106707341A (en) * | 2017-03-16 | 2017-05-24 | 西南石油大学 | High-resolution sequence stratigraphic division method based on EEMD (Ensemble Empirical Mode Decomposition) |
| CN107844650A (en) * | 2017-11-07 | 2018-03-27 | 西安理工大学 | Dam abutment safety coefficient computational methods based on full structural plane surrender method |
| CN108663712A (en) * | 2018-05-08 | 2018-10-16 | 四川大学 | A kind of Thin interbed method of side slope earthquake damage |
-
2020
- 2020-04-29 CN CN202010352769.5A patent/CN111563289A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020023364A1 (en) * | 2000-07-08 | 2002-02-28 | Robert Arnold | Rubber stamp positioning device |
| CN103135128A (en) * | 2013-01-31 | 2013-06-05 | 重庆大学 | Three-dimensional slope stability prediction method under earthquake load effect |
| CN104182646A (en) * | 2014-09-01 | 2014-12-03 | 重庆大学 | Three dimensional earthquake side slope landslide occurring time forecasting method based on slide displacement analysis |
| CN105677961A (en) * | 2016-01-05 | 2016-06-15 | 江西理工大学 | Method for constructing side-slope three-dimensional slip crack face model |
| CN105787277A (en) * | 2016-03-02 | 2016-07-20 | 江西理工大学 | Three-dimensional critical slip crack surface search method for slope |
| CN106707341A (en) * | 2017-03-16 | 2017-05-24 | 西南石油大学 | High-resolution sequence stratigraphic division method based on EEMD (Ensemble Empirical Mode Decomposition) |
| CN107844650A (en) * | 2017-11-07 | 2018-03-27 | 西安理工大学 | Dam abutment safety coefficient computational methods based on full structural plane surrender method |
| CN108663712A (en) * | 2018-05-08 | 2018-10-16 | 四川大学 | A kind of Thin interbed method of side slope earthquake damage |
Non-Patent Citations (3)
| Title |
|---|
| HONGLUE QU等: "Honglue Qu等", 《JOURNAL OF GEOPHYSICS AND ENGINEERING》 * |
| SIH, GC: ""Surface displacement characteristics of line crack trespassing the Rayleigh wave speed barrier as influenced by velocity and applied stress dependent local zone of restrain"", 《THEORETICAL AND APPLIED FRACTURE MECHANICS》 * |
| 陈涛: ""山区机场高填方地基变形及稳定性研究"", 《中国优秀博硕士学位论文全文数据库(博士) 工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116046307A (en) * | 2022-12-12 | 2023-05-02 | 中铁西北科学研究院有限公司 | Identification method suitable for earthquake damage mode of vibrating table with tunnel slope |
| CN116905297A (en) * | 2023-09-12 | 2023-10-20 | 大秦铁路股份有限公司大同工务段 | Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium |
| CN116905297B (en) * | 2023-09-12 | 2023-12-12 | 大秦铁路股份有限公司大同工务段 | Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium |
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