JP2008249733A - Apparatus for predicting deterioration in concrete due to frost damage and program therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To make it possible to create a deterioration prediction curve by quantifying the influence of deterioration due to frost damage on a factor-by-factor basis and predict a deterioration prediction curve at a point of reference in consideration of cracks and the effectiveness of repair. <P>SOLUTION: An apparatus for predicting a deterioration due to frost damage predicts, with reference to a curve of the deterioration due to frost damage based an exposure test at a point of reference in a natural environment, a curve of the deterioration due to frost damage at a point of prediction that reflects characteristic values based on structural data on concrete. Further, the apparatus quantitatively calculates, as characteristic values, the relationships of the number of cycles of freezing thawing fracture with respect to aggregate, the quality of a bonding material, the influence of an AE agent, the water-cement ratio, and cracks, and reflects these characteristic values as partial coefficients in calculation of a deterioration prediction curve. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a concrete frost damage deterioration prediction apparatus and its program, which are preferable for maintaining and managing a concrete structure that is subjected to a freezing and thawing action in a natural environment.

  Since the period of high economic growth, a huge amount of concrete structures have been built in Japan on roads, railways, dams, and harbors. Currently, these structures are maintained and managed daily as important social capital, but they are used in various environmental conditions, so they will require more maintenance such as repair and reinforcement in the future. Is expected to increase.

Under these circumstances, in recent years, many systems for supporting the maintenance and management of concrete structures have been developed. As this type of device, for example, a deterioration index corresponding to each deterioration factor is derived by using regional characteristic data and concrete structure design data regarding weather data, terrain data, concrete composition, and the like. There is an apparatus that ranks and evaluates the deterioration state of a concrete structure based on it (for example, see Patent Document 1).
JP 2002-131216 A

Maintenance of a concrete structure is to ensure the performance required for the structure over the service period of the structure. If the performance cannot be ensured during the service period, measures such as repair will be implemented, but deterioration prediction technology is indispensable in order to set an appropriate repair time.
Regarding frost damage deterioration, it has been qualitatively understood that the water cement ratio and the number of freeze-thaw cycles affect the progress of deterioration, but quantitative evaluation has been difficult. A standardized freeze-thaw cycle method has been devised as a method for quantitatively evaluating these effects. This method utilizes the knowledge of the results of exposure tests based on the calculation of the number of times of freezing and thawing at the lowest temperature for each predicted point and the composition of the concrete used in the dam, so it can be applied to general concrete structures at any point. There was a problem that it took a lot of labor and time to apply.

The present invention has been made in view of the above circumstances, and the influence of the frost damage deterioration factor is divided into an internal factor of the concrete itself and an external factor depending on the location of the facility, the surrounding environment, etc. It is an object of the present invention to provide a concrete frost damage deterioration prediction device and its program capable of calculating a deterioration prediction curve by quantifying each influence so that different effects can be examined.
In addition, after calculating the above-mentioned deterioration prediction curve, it is possible to estimate the deterioration prediction curve considering cracks and repair effects, so that repair of concrete with advanced deterioration can be repaired at an appropriate time, and the accuracy of maintenance management is improved. It is another object of the present invention to provide a concrete frost damage deterioration prediction apparatus and its program.
Furthermore, by calculating the above-described deterioration prediction curve not only in the surface layer portion of the concrete but also in the depth direction, the depth direction deterioration will occur when setting the chip thickness at the time of repair and evaluating the member load bearing capacity. It is another object of the present invention to provide a concrete frost damage deterioration prediction apparatus and program capable of grasping the degree thereof and further improving the accuracy of maintenance management.

  In order to solve the above-described problems, the present invention is a frost damage deterioration prediction device for concrete that is constructed on a computer and is subjected to freeze-thaw action in a natural environment, and includes basic data including environmental data and data of a prediction target concrete A basic data input capturing unit that captures the property, a characteristic value calculating unit that calculates a characteristic value related to frost damage deterioration of the concrete to be predicted using the basic data, and a concrete based on an exposure test at a reference point in a natural environment And a deterioration prediction calculation unit that calculates a frost damage deterioration curve at a predicted point reflecting the characteristic value calculated by the characteristic value calculation unit with reference to the frost damage deterioration curve.

  In the present invention, the characteristic value calculation unit may include an aggregate, a binder, an air entrainment agent (hereinafter referred to as an AE agent), a degree of water saturation, water of the prediction target concrete that is captured by the basic data input capturing unit. The influence on the frost damage deterioration due to at least one of the cement ratios is converted into partial coefficients obtained by executing predetermined arithmetic expressions defined in advance, and supplied to the deterioration prediction calculation unit.

  In the present invention, the deterioration prediction calculation unit obtains a reference frost damage deterioration curve at the prediction point by multiplying the number of freeze-thaw cycles in the exposure test at the reference point by the partial coefficient output by the characteristic value calculation unit. The standard frost damage curve shows the number of times of freezing and thawing according to the minimum temperature obtained by measuring the number of times that the daily minimum temperature and maximum temperature were exceeded and the temperature exceeding the freezing point was exceeded. And a deterioration prediction curve using the destruction function indicated by the minimum temperature.

  Further, in the present invention, the deterioration prediction calculation unit multiplies a partial coefficient representing the influence of cracks by the ratio of the number of fracture cycles when there is a crack in the prediction target concrete defined by the fracture function and when there is no crack, It is characterized by correcting the reference deterioration curve.

  Further, in the present invention, the deterioration prediction calculation unit multiplies a partial coefficient indicating a repair effect obtained from the number of fracture cycles when the repair target concrete is repaired and the number of fracture cycles when the concrete is not repaired, It is characterized by correcting the reference deterioration curve.

  In order to solve the above-described problems, the present invention is a program used for a frost damage deterioration prediction apparatus for concrete subjected to freeze-thaw action in a natural environment, and captures basic data including environmental data and prediction target concrete data. Processing, calculating the characteristic value related to the frost damage deterioration of the predicted concrete using the basic data, and the characteristic value based on the frost damage deterioration curve of the concrete based on the water exposure test in a natural environment. And a process of calculating a frost damage deterioration curve at a predicted point reflecting the characteristic value calculated by the calculation unit.

  In order to solve the above-described problems, the present invention is a frost damage deterioration prediction apparatus for concrete that is constructed on a computer and that is subjected to a freeze-thaw action in a natural environment, the analysis depth of the concrete input by a user, A depth direction analysis condition input capturing unit that captures a heat conduction boundary condition, a heat conduction analysis processing unit that performs unsteady heat conduction analysis of the concrete by a finite element method based on the heat conduction boundary condition, and the heat conduction analysis And a deterioration prediction calculation unit that calculates a deterioration curve inside the concrete based on the number of times of freezing and thawing calculated according to the internal depth.

  In the present invention, the heat conduction analysis processing unit calculates the number of freeze / thaw cycles per year based on the outside air temperature according to the basic data acquired from the outside, and the outside air temperature in the year with the highest number of freeze / thaw times calculated. In addition, heat conduction analysis inside the concrete is performed using a heat transfer boundary modeled in advance for each heat transfer direction on the concrete surface in contact with the outside air.

  Further, in the present invention, the deterioration prediction calculation unit is based on a deterioration characteristic curve of the concrete based on an exposure test at a reference point in a natural environment, and the aggregate, the binder, the air entraining agent, the saturated water of the concrete. The effect on the frost damage deterioration characteristics due to at least one of the degrees is converted into a partial coefficient obtained by executing a predetermined arithmetic expression defined in advance, and the number of freeze-thaw cycles in the exposure test at the reference point Multiply the partial coefficient to obtain a standard deterioration curve of the predicted point, and calculate the reference deterioration curve based on the concrete internal temperature obtained from the heat conduction analysis result and the number of times that the freezing point is exceeded from the maximum temperature and the maximum temperature of the day. The measured number of freeze-thaw cycles by minimum temperature and the number of fracture cycles are calculated using the fracture function indicated by the water cement ratio and the minimum temperature. And converting the Predictive curve.

  In order to solve the above-described problems, the present invention is a program used for a frost damage deterioration prediction apparatus for concrete subjected to freeze-thaw action in a natural environment, the analysis depth of the concrete input by a user, and heat Based on the step of capturing the conduction boundary condition, the step of performing unsteady heat conduction analysis of the concrete by a finite element method based on the heat conduction boundary condition, and the freeze-thawing frequency calculated by the internal depth by the heat conduction analysis And a step of calculating a deterioration curve inside the concrete.

  According to the present invention, a frost damage inferior curve at a predicted point reflecting a characteristic value calculated by a characteristic value calculation unit is calculated based on a frost damage deterioration curve of concrete based on an exposure test at a reference point in a natural environment. By doing so, it is possible to construct a concrete deterioration prediction system that undergoes freeze-thaw action, and to accurately calculate a concrete deterioration prediction curve under any concrete composition and natural environment.

Further, according to the present invention, the quality of the aggregate and the binder, the influence of the AE agent, the water cement ratio, and the crack can be quantitatively determined by using the ratio of the number of fracture cycles as a characteristic value. By reflecting the calculation of the deterioration prediction curve as a partial coefficient, it is possible to appropriately perform the deterioration diagnosis of the concrete subjected to the freezing and thawing action.
In addition, after calculating the deterioration curve of the concrete to be predicted, it is possible to easily determine the repair time according to the concrete performance by estimating the deterioration prediction curve considering cracks and repair effects. The accuracy of maintenance can be improved by repairing to.

Furthermore, according to the present invention, in order to obtain the degree of deterioration in the depth direction as well as the surface layer portion of the concrete, the temperature distribution inside the concrete is obtained by unsteady heat conduction analysis, and the number of times of freezing and thawing is calculated according to the depth. Similarly, by estimating the degradation depth, it is possible to grasp the degree of degradation in the depth direction when setting the chip thickness during actual repairs and evaluating the load bearing capacity of the member. Can be further enhanced.
In addition, when conducting heat conduction analysis by the finite element method, the concrete to be analyzed is roughly divided into several types and modeled in advance so that users can predict deterioration without being particularly conscious of model creation. Can be reduced. Furthermore, when conducting heat conduction analysis, the number of freeze / thaw cycles per year based on the outside air temperature over the entire period to be predicted is obtained, and the year with the highest number of freeze / thaw cycles is selected and implemented as the representative year. The analysis result can be acquired at a relatively high speed. In addition to limiting the period to be analyzed, by selecting the year with the largest number of freeze / thaw cycles as the representative year, it is possible to predict on the safe side when evaluating deterioration.

FIG. 1 is a schematic configuration diagram showing an embodiment of a concrete frost damage deterioration prediction apparatus according to the present invention.
As shown in FIG. 1, the frost damage deterioration prediction apparatus 10 of the present invention is composed of a CPU 11, a RAM 12, a storage device 13, an input device 14, a display device 15, a printing device 16, a communication device (NW) 17, and the like. Commonly connected via a system bus 18.
The CPU 11 takes in various data obtained via the input device 14 or the communication device 17 in accordance with the deterioration prediction program of the present invention stored in the storage area of the storage device 13, and a series of processes described later using the RAM 12 as a work area. The result is temporarily stored in the RAM 12 again and displayed or printed out via the display device 15 or the printing device 16.

The CPU 11 functions as a basic data input capturing unit 111, a characteristic value calculation unit 112, a deterioration prediction calculation unit 113, and a deterioration prediction curve output unit 114 according to the present invention by the deterioration prediction program of the present invention. FIG. 2 shows a block configuration developed for each function described above.
That is, the frost damage deterioration prediction apparatus 10 according to an embodiment of the present invention functionally includes a basic data input capture unit 111, a characteristic value calculation unit 112, a deterioration prediction calculation unit 113, a deterioration prediction curve output unit 114, A database (DB) 115 is used.

The basic data input capturing unit 111 has a function of capturing basic data including environmental data and structural data of the prediction target concrete and storing the basic data in the DB 115, and the characteristic value calculating unit 112 uses the basic data stored in the DB 115. And it has a function to calculate the characteristic value related to frost damage deterioration of the concrete to be predicted.
The characteristic value calculation unit 112 also affects the frost damage deterioration characteristics of at least one of the aggregate, the binder, the AE agent, the water saturation, and the water cement ratio of the prediction target concrete captured by the basic data input capturing unit 111. Is also converted into partial coefficients obtained by executing predetermined arithmetic expressions defined in advance, and supplied to the degradation prediction arithmetic unit 113. In addition, the deterioration prediction calculation unit 113 is a prediction point that reflects the characteristic value calculated by the characteristic value calculation unit 112 based on the frost damage deterioration characteristic curve of concrete based on the exposure test at the reference point in the natural environment. It has a function to predict the frost damage degradation curve.

The deterioration prediction calculation unit 113 multiplies the number of freeze / thaw cycles in the exposure test at the reference point by the partial coefficient output by the characteristic value calculation unit 112 to obtain a reference frost damage deterioration curve at the prediction point, and the reference frost damage deterioration The curve shows the number of times of freezing and thawing according to the minimum temperature obtained by obtaining the minimum and maximum temperatures of the day and measuring the number of times the temperature was below the freezing point and exceeding the freezing point in time series, and the number of destruction cycles by the water cement ratio and the minimum temperature. It has a function to convert to a deterioration prediction curve using a destructive function.
Furthermore, when the prediction target concrete has cracks and when there is no crack, the ratio of failure cycles is multiplied as a partial coefficient representing the effect of cracks, the reference deterioration characteristic curve is corrected, and the prediction target concrete is repaired. It also has the function of correcting the reference deterioration curve by multiplying the partial coefficient indicating the repair effect obtained from the number of destruction cycles and the number of destruction cycles when not applied.

Here, the description returns to FIG. The RAM 12 includes a work area for temporarily storing various processing programs including the above-described frost damage deterioration prediction program executed by the CPU 11 and data related to the processing.
The storage device 13 includes a storage medium 13a in which the above-described program, data, and the like are stored. The storage medium 13a is configured by a magnetic or optical recording medium or a semiconductor memory. The recording medium 13a is fixedly attached to the storage device 13 or is detachably mounted. The recording medium 13a is a storage area for storing a program executed by the CPU 11, control data, and the like, and a concrete environment to be predicted. A storage area for storing basic data related to data and structure data as a DB (115 in FIG. 2) is provided. When the CPU 11 executes the frost damage deterioration prediction program stored in the recording medium 13a, a process for predicting a frost damage deterioration prediction curve, which will be described later, is executed.

The input device 14 is configured by a keyboard, a pointing device, or the like, and outputs an input instruction signal to the CP 11.
The display device 15 is composed of a CRT, an LCD (Liquid Crystal Device), or the like, and displays various screen data in addition to a frost damage deterioration prediction curve described later based on display data generated and output by the CPU 11. The printing device 16 is configured by a printer or the like, and prints and outputs print data including various documents generated and output by the CPU 11.
The NW device 17 is connected to an external computer network via an IP (Internet / Protocol) network or the like, and is used as a tool for taking in the basic data described above, as well as an external device permitted to use the system. Data relating to a frost damage deterioration prediction curve generated and output by the CPU 11 is supplied to a connected terminal device (not shown).

  Next, the deterioration prediction process executed by the frost damage deterioration prediction apparatus 10 having the above-described configuration will be described. As shown in FIG. 3, the deterioration prediction process is performed from the steps of the basic data input process (S301), the characteristic value calculation process (S302), the deterioration prediction process (S303), and the deterioration prediction curve display process (S304). These series of processes are sequentially executed in accordance with the frost damage deterioration prediction processing program stored in the storage medium 13a of the storage device 13.

In detail, as shown in FIG. 3, first, in step S301, basic data is input from the input device 14 or via the NW device 17, and processing for capturing the basic data is performed. The basic data is fetched by the input data fetching unit 111.
Here, as basic data, environmental data such as external factors such as temperature, solar radiation in winter, heading, and snow cover are prepared, and a file is input by acquiring them from AMeDAS or the like. In addition, “position data” consisting of “structure name”, “construction year”, “evaluation year to be predicted”, latitude, longitude, altitude, etc. of the predicted point, solid or low quality, or water absorption rate (%) and stable "Aggregate data" consisting of data such as weight loss (%), cement type, "binder data" indicating the type of admixture (fly ash, blast furnace slag, not used), and to improve frost damage resistance Structure data consisting of various data which are internal factors such as “AE agent data”, “saturation degree”, “water cement ratio” indicating whether or not the surfactant (AE agent) is used, and width 0. “Crack data” indicating the presence / absence of cracks of 3 mm or more, “repair effect data” indicating the repair implementation year and an effect factor described later, and the like are input to the frost damage deterioration prediction apparatus 10 of the present invention.

Next, a characteristic value calculation process is performed in step S302. The characteristic value calculation process is performed by the characteristic value calculation unit 112.
Here, it is first executed from the freeze / thaw number calculation process. Although details will be described later, the nearest AMeDAS point is found from the position data of the concrete structure, and the temperature is corrected by the altitude, and the number of times of freezing and thawing by the minimum temperature is calculated. Then, the characteristic value is calculated in consideration of the influence of irradiation, direction, snow cover, and snow cover period. An example of a graph display of the number of freeze / thaw cycles by minimum temperature is shown in FIG. 12 together with a table.
Subsequently, a characteristic value calculation process is performed for each of the influences on the aggregate, the binder, the AE agent, the degree of saturation, and the water cement ratio, and the partial coefficients ψ _ag , ψ _bi , ψ _AE , ψ _as , ψ _This is supplied to the deterioration prediction calculation unit 113 as _{w / c} .

Specifically, in the case of aggregate, the partial coefficient (ψ _ag ) is expressed by the respective functions when the hardness and hardness are 1.0, the low quality is 0.38, and the water absorption rate and the stability loss weight are known. A partial coefficient is calculated. In addition, as for the influence of the binder, the partial coefficient is calculated from the combination of cement and admixture. Further, as for the influence of the AE agent, a partial coefficient is calculated from the presence or absence of the AE agent, and when there is no AE agent, the partial coefficient expressed as a function of the water cement ratio is calculated.
As for the influence of the degree of water saturation, the partial coefficient is 1.0 for wet A (when always in contact with water) and 2 for wet B (when there is rain but there is no continuous water supply during freezing). .4. Further, the partial coefficient is calculated by executing a predetermined arithmetic expression to be described later, where θ is the average daily minimum temperature of the water cement ratio and c / w is the cement water ratio. Details of both characteristic values will be described later.

Next, a deterioration prediction process is executed in S303. The deterioration prediction process is executed by the deterioration prediction processing unit 113. Here, first, a reference deterioration prediction curve is calculated. Specifically, the number of freeze-thaw cycles in the exposure test at the reference point is multiplied by each characteristic value obtained by the characteristic value calculation process described above to calculate the reference deterioration curve at the predicted point. Here, the horizontal axis of the prediction curve is the number of freeze-thaw cycles.
Subsequently, a process for calculating a deterioration prediction curve is executed. Here, the reference deterioration curve is converted into a deterioration prediction curve having the horizontal axis as the number of years elapsed using the minimum number of freeze-thaw cycles at each temperature and the destruction function. Furthermore, a deterioration prediction curve in the case where there is a crack is obtained by multiplying the number of cycles of the reference deterioration curve by a partial coefficient representing the influence of the crack. In addition, a deterioration pre-curve calculation process considering the effect of repair is also performed. That is, here, a partial coefficient representing the effect of repair is multiplied by the cycle number of the reference deterioration curve to obtain a deterioration prediction curve when the repair is performed. Details of both will be described later.

  Finally, a deterioration prediction curve display process is executed in S304. The deterioration prediction curve display process is executed by the deterioration prediction curve output unit 114. An example of the frost damage deterioration prediction curve screen display is shown in FIG. 13, and an example of the frost damage deterioration prediction curve screen considering the effect of cracks is shown in FIG. An example is shown in FIG.

Hereinafter, the details of the frost damage deterioration prediction apparatus of the present invention will be described. FIG. 4 conceptually shows the relationship between the internal structure of concrete and the water-cement ratio. The frost damage of concrete, which is positioned as one of the porous materials, is thought to depend on the rate of icing between the gaps.
Through experiments, it has been clarified that the freeze-thaw resistance of concrete increases as the water-cement ratio (the amount of space in which moisture exists) is lower and as the age of the material elapses. The reason is considered to be that the strength of the concrete is increased, and there is also an idea that promotes freeze-thaw resistance as a function of strength. However, a high water cement ratio means that the amount of freezing water in the system increases at the same time as shown in the graph of the relationship between the water cement ratio and the freezing amount in the pores in FIG. In evaluating the resistance to freezing and thawing, it is considered that the amount of water that can be frozen, not the strength, should be evaluated.

  The amount of frozen water in concrete depends on the amount of space in which moisture exists and the size of the space. When the age is constant, the degree of damage due to freezing and thawing increases as the water cement ratio increases. Moreover, the pseudo-fixed point temperature of water depends on the space size, and the smaller the size, the lower the pseudo-fixed point temperature. Therefore, all the amount of water does not freeze at 0 ° C. In other words, as the temperature decreases, water up to a small space size freezes and damage increases.

FIG. 6 is an example of three graphs showing the results of freeze-thawing tests of concrete not using the AE agent (hereinafter referred to as nonAE concrete) when the minimum temperature is changed. %), And the horizontal axis shows the number of freeze-thaw cycles (number).
As is clear from this graph, it can be seen that the higher the water-cement ratio and the lower the minimum temperature, the greater the damage. This indicates that the higher the freezing potential of moisture in the concrete, the more likely it is to deteriorate, and the relationship between the number of fracture cycles and the frozen pore volume ratio is graphically displayed in FIG. The higher the is, the smaller the number of destruction cycles. The frozen pore volume ratio is the ratio of the frozen volume in the frozen pore volume, and the number of fracture cycles is the number of cycles in which the relative dynamic elastic modulus is less than 60%. Further, according to FIG. 7, the influence of the water cement ratio and the minimum temperature can be uniformly expressed by an index “freeze pore volume ratio”.

By the way, it is not realistic to measure with concrete for the above-mentioned “freezing pore quantification”, and it is practical to express the number of fracture cycles in terms of water cement ratio and minimum temperature.
Assuming that the number of fracture cycles can be defined by the fracture function shown in FIG. 8, the test results obtained by changing the minimum temperature and water-cement ratio described in FIG. 6 are the graphs shown in FIG. ).
Here, the horizontal axis shows a standardized state with a certain water cement ratio and the minimum temperature (in this case, w / c = 50%, minimum temperature−5 ° C.). According to FIG. 9, even a test result in which the water cement ratio and the minimum temperature are changed by using the fracture function can be expressed as one deterioration curve.

Incidentally, FIG. 10A shows the result of the exposure test at the reference point conducted by the applicant. The result shown here is based on AE concrete mixed with fly ash, and excludes the increase in strength during exposure. The exposure test environment is as shown in FIG.
In addition, AE concrete is the concrete which uses AE agent, and is described as AE concrete below.

The standardized freeze-thaw cycle method evaluates the amount of damage as a function of water-cement ratio and minimum temperature history, and by changing the degree of damage, apparently the number of freeze-thaw cycles, at any water-cement ratio, and any It is a method to replace concrete deterioration under a single minimum temperature.
The correction of the water cement ratio and the minimum temperature is realized by calculating a “weight coefficient” from the fracture function shown in FIG. 8 and apparently changing the number of cycles. “Weighting factor” is defined as the ratio of the number of failure cycles to a certain water cement ratio and minimum temperature. A specific calculation method is obtained by executing the following arithmetic expression (1).

  Using the standardized freeze-thaw cycle method, if the exposure test result of the specimen at the reference point is replaced with a water cement ratio of 49% and a minimum temperature of −6 ° C., FIG. 11 (degradation in the natural environment by the standardized freeze-thaw cycle method) Curve), and a plurality of deterioration curves are replaced with deterioration curves under an arbitrary water cement ratio and minimum temperature.

  From FIG. 11, the deterioration curve of the concrete subjected to the freezing and thawing action in the natural environment is expressed as the following arithmetic expression (2).

However, this deterioration curve is a deterioration curve of AE concrete mixed with fly ash using moderately hot Portland cement exposed to the reference point, and the standardized freeze-thaw cycle even if the water cement ratio and weather (minimum temperature) change Although it can be corrected by the method, if the type of cement, the quality of the aggregate, and the presence or absence of the use of the AE agent are different, this arithmetic expression (2) cannot be used directly.
Therefore, the conditions different from the exposure tests at these reference points are corrected using the partial coefficients as follows. In the present invention, the partial coefficients shown in Table 1 below are introduced, and the following calculation formula (3 ) To generalize the deterioration curve.

Below, the characteristic value items will be described. First, external factors will be explained. When using the standardized freeze-thaw cycle method, the influence of external factors must be equivalently converted to daily maximum and daily minimum temperatures. Here, we consider the effects of solar radiation and snow as factors other than the outside temperature.
As information about the outside air temperature, the daily maximum and minimum temperatures are required in calculating the number of times of freezing and thawing. This must be a direct input item. However, since there is a difference in altitude between the reference point of the meteorological data recording and the predicted point, it is necessary to correct the altitude. The correction is based on the following commonly used calculation formula (4).

  The effect of solar radiation on concrete temperature is to replace the amount of heat from solar radiation with the outside air temperature equivalently. The conversion to the equivalent outside air temperature follows the following equation (5).

Furthermore, the influence of the direction is added to the above-described arithmetic expression (5). The influence of the direction will be evaluated by the weight for each direction from the amount of solar radiation for each direction measured in the winter season at the exposure test site at the reference point. As the weight, when the south surface is 1, the upper surface is 0.84, the west surface is 0.58, the east surface is 0.25, and the north surface is 0.23. These are all values obtained from experiments.
Therefore, the equivalent outside air temperature in consideration of sunlight is changed as shown in the following arithmetic expression (6).

但し、通常気象観測所で計測される日射量は、上面のそれであるため、演算式（６）の方位による重み係数は、上面１．００、南面１．１９、西面０．６９、東面０．３０、北面０．２７である。

However, since the amount of solar radiation measured at a normal weather station is that of the top surface, the weighting factors according to the direction of the calculation formula (6) are the top surface 1.00, the south surface 1.19, the west surface 0.69, and the east surface. 0.30, north surface 0.27.

On the other hand, if the above equation (6) is used, it is possible to estimate the amount of melted snow by referring to the wind speed and temperature of the AMeDAS data, and calculate using the subsequent data after the target structure is constructed. Just do it. However, verification regarding the accuracy is a problem.
Therefore, in this case, it is assumed that direct information regarding the amount of snow is taken in. In this case, since there is no snow cover data in AMeDAS, we will refer to the data observed at the regional meteorological observatory. In addition, it shall not melt while there is snow.

Next, the influence by structure data is demonstrated. First, the partial coefficient representing the influence of aggregate quality will be described.
As an index indicating the quality of the aggregate, water absorption and stability loss weight are taken up. Member coefficient [psi _ag relating to the quality of the aggregate used in scaling the freeze-thaw cycle method is represented by the following arithmetic expression (7) (8). However, this is an empirical formula.
(When water absorption rate and stability loss weight are known)

(When water absorption rate and stability loss weight are unknown)
When using hard aggregate, ψ _η = 1.0, when using low quality aggregate, ψ _η = 0.38, ψ _η = 0.58, and adopting the smaller member coefficient And ψ _η = 0.38.

Next, partial coefficients representing the influence of the type of binder will be described. The partial coefficients ψ _bi of the binder are summarized as follows. Here, it should be noted that the reference is the exposed specimen at the reference point, the medium heat is used as the cement, and fly ash is used as the admixture. Therefore, if the partial coefficient is set based on this, it is as shown in Table 2 (partial coefficient of binder (AE concrete)) and Table 3 (partial coefficient of binder (non-AE concrete)) shown below.

Next, the partial coefficient indicating whether or not the AE agent is used will be described. Based on this test result, the cycle in which the durability index ratio and relative dynamic elastic modulus of AE concrete and nonAE concrete are 60% (ratio of the number of fracture cycles: partial coefficient), that is, the partial coefficient representing the influence of the AE agent. ψ _AE is obtained by executing the following arithmetic expression (9).

If an AE agent is used, ψ _AE = 1.0.

  In the arithmetic expression (9), DF refers to a durability factor (Durability Factor) and is calculated by P · N / M. Here, P is the relative dynamic elastic modulus (%) in the N cycles of the freeze-thaw test, N is the number of cycles of the freeze-thaw test when P reaches 60%, or P is the test end cycle (generally 300, 200, 100 cycles), and the number of cycles when the relative dynamic elastic modulus is less than 60%, M is a predetermined number of freeze-thaw cycles (generally 300, 200, 100 cycles). Here, Nagakura's research refers to Nagakura Tadashi, research on freezing and thawing resistance of concrete, Technical Research Report of Electric Power Research Laboratory (Civil Engineering · 61612), 1961.9.

Next, the partial coefficient representing the influence of water saturation will be described. As described above, the partial coefficient ψ _sa representing the influence of water saturation is calculated by executing the following arithmetic expression (10).

When it is always in contact with water, ψ _sa = 1.0, there is rain, but when there is no continuous water supply during freezing, ψ _sa = 2.4.

The calculation method of the reference result curve in consideration of the influence of internal factors will be described below.
Here, the reference deterioration curve at the predicted point is obtained based on the deterioration curve in the natural environment in the exposure test at the reference point.
The deterioration curve at the reference point is represented by the following calculation formula (11).
REd ^m = 100 exp (−6.02 × 10 ⁻³ × N ^0.42 ) (11)
Here, REd ^m = relative kinematic modulus (%), N: number of freeze-thaw cycles (times), and N multiplied by a partial coefficient is used as a reference deterioration curve at a predicted point.
REd = 100exp (−6.02 × 10 ⁻³ × (N · ψ _mod ) ^0.42 ) (12)
Ψ _mod = ψ _ag · ψ _bin · ψ _AE · ψ _sa · ψ _{w / c} (13)
Ψ _ag : partial coefficient representing the influence of aggregate, ψ _bin : partial coefficient representing the influence of binder, ψ _AE : partial coefficient representing the influence of AE agent, ψ _sa : partial coefficient representing the influence of water saturation , Ψ _{w / c} : partial coefficient representing the influence of water cement, ψ _mod : partial coefficient.

By the way, the freeze-thaw the number of prediction point, when N ^p, standard deterioration curve,
^{REd = 100exp {-6.02 × 10 -3} × (N p) O. ⁴² }… (14)
However, N ^p = N · ψ _mod , and N ^p is the number of freeze-thaw cycles when the reference temperature θ _st of the prediction point and w / c (water cementation) of the prediction target concrete are fixed.

In the natural environment, the minimum temperature always fluctuates, so the number of freeze / thaw cycles for each minimum temperature, N _ij (i: year, j: minimum temperature), the weight coefficient ψ _θj obtained from the above equation (15) is Multiply and calculate the number of freeze-thaws in each year according to the following equation (16) (standardized freeze-thaw cycle method).

Thus, it is possible to obtain the freeze-thaw number N _i, can be a life display by N _i from the number of cycles displayed by N ^P. This makes it possible to predict deterioration on the time axis. FIG. 13 shows an example of the screen configuration.

In addition, the deterioration prediction curve considering the effect of cracks can be predicted by correcting N ^p = N · ψ _mod × ψ _{cr in} the above equation (14). However, (psi) _cr is a partial coefficient showing the influence of a crack. Thereby, it is possible to predict the deterioration curve in consideration of the influence of cracks with respect to the reference deterioration curve.
The partial coefficient representing the effect of cracking is ψ _cc = 1.0 when there is no crack, and ψ _cr = 0.19 when there is a crack, based on the results of laboratory tests and the like. Both are numerical values obtained from experiments.

  In the frost damage deterioration prediction apparatus according to the present invention, calculation and display of a deterioration curve considering cracks is performed in the following procedure. It is a precondition for the calculation of the reference deterioration curve that the calculation of the reference deterioration curve has been completed. As an example of the screen configuration is shown in FIG. 14, first, by pressing a crack influence button in the deterioration prediction curve window, the above-described processing in consideration of the influence of the crack is executed, and the deterioration in the facility is performed. A prediction curve and a degradation curve graph of the cracked part are automatically displayed.

Further, the deterioration prediction curve considering the repair effect can be predicted by correcting N ^p = N · ψ _mod × ψ _{re in} the above-described arithmetic expression (14). However, [psi _re a partial coefficient repair effect. As a result, a deterioration curve can be predicted in consideration of the effect of the repair effect on the reference deterioration curve.
The partial coefficient representing the effect of repair is calculated by the following arithmetic expression (17) when based on the repair test result, and by the following arithmetic expressions (18) and (19) when based on the test of the repair material alone. Is done. The latter is an arithmetic expression obtained by experiment.

  In the frost damage deterioration prediction apparatus of the present invention, in order to calculate and display a deterioration curve considering the repair effect, an operation is performed in the following procedure. The calculation of the standard deterioration curve considering the repair effect is based on the precondition that the calculation of the standard deterioration curve has been completed. As shown in FIG. 15 as an example of the screen configuration, the start year and the effect coefficient in the deterioration prediction curve window are designated, and then the repair effect button is pressed. As a result, processing considering the influence of repair is executed, and the deterioration prediction curve of the facility and the deterioration curve graph after repair are automatically displayed.

As described above, the embodiment of the present invention described above is based on the frost damage deterioration curve of concrete based on the exposure test of the reference point in the natural environment, and the frost damage at the predicted point reflecting the characteristic value based on the concrete structure data. By predicting the deterioration curve, it is possible to construct a deterioration prediction system for concrete subjected to freezing and thawing action, and to accurately calculate the deterioration curve of concrete in any concrete mix and natural environment.
In addition, regarding the quality of aggregates and binders, the effect of AE agent, water cement ratio and cracks, the relationship with the number of freeze-thaw failure cycles of concrete is quantitatively obtained as characteristic values, and deterioration prediction is performed using these characteristic values as partial coefficients. By reflecting it in the calculation of the curve, it is possible to appropriately perform deterioration diagnosis of the concrete subjected to the freezing and thawing action. Furthermore, after calculating a standard deterioration curve, a deterioration prediction curve considering cracks and repair effects is estimated, so that a repair time according to the performance of the concrete is easily formulated.

  By the way, the deterioration prediction according to the embodiment of the present invention described above is intended for the surface layer portion of concrete, but when setting the thickness of the chisel at the time of actual repair or evaluating the load bearing capacity of the member. It is necessary to grasp the degree of degradation in the depth direction. Therefore, in another embodiment of the present invention, the temperature distribution inside the concrete is obtained by unsteady heat conduction analysis, the number of freeze-thaws is calculated for each depth, and the deterioration depth is estimated by the same procedure as described above. did.

If the temperature data is read from the outside air, the temperature inside the member can be estimated, and there is no particular restriction on the analysis target period. However, when this function is implemented in the deterioration prediction without providing a restriction on the analysis target period, it adversely affects the processing time of the computer.
That is, when a non-stationary analysis, particularly a two-dimensional model is assumed, a long time is required for the analysis. For example, when the member size is 30 cm × 30 cm and the analysis target period is 1 hour in increments of 1 hour, the calculation time is about 2 minutes. Therefore, when it is going to perform a heat conduction analysis over 40 years after completion, about 80 minutes are required for calculation time, and it is unrealistic. Moreover, when it is going to carry out heat conduction analysis with the above model in the future, for example, when trying to predict the state after 100 years, the period from completion to the present (temporarily 40 years) and the future period If it is going to calculate over 4 hours, it will require 4 hours or more. For this reason, it is unrealistic to carry out heat conduction analysis over the entire period of the target year of deterioration prediction, and some kind of response is required.

For this reason, in the present invention, the representative year is selected and the heat conduction analysis is performed, and the year when the number of times of freezing and thawing based on the outside temperature is large is selected.
This is because, when simply focusing on the temperature, the influence of the daily temperature difference at the freezing point cannot be taken into account, and the temperature distribution inside the concrete and the number of times of freezing and thawing are not linear.

FIG. 16 and FIG. 17 are graphs showing the temperature distribution inside the concrete and the number of annual freeze-thaw cycles when the outside air records the lowest temperature. According to these, the temperature distribution shows a smooth curve distribution from the surface to the inside, but the number of freeze / thaw changes linearly at a certain depth, but the number of changes due to the depth is less at deeper depths. It can be seen that the distribution and the number of freeze-thaws are not similar. Therefore, it is not reasonable to set the representative year based on the temperature, and selection based on the number of times of freezing and thawing is considered necessary.
Therefore, the number of freeze / thaw cycles for each year is determined from the past outside air temperature at the prediction target point, the year with the highest number of times is selected as the representative year, and this representative year is repeated in the prediction of deterioration. This is because it was considered to make a prediction on the safe side when evaluating the deterioration. FIG. 18 is a functional block diagram according to another embodiment of the present invention, and FIGS. 19 and 20 are flowcharts showing the procedure.

That is, the frost damage deterioration prediction apparatus 10 according to another embodiment of the present invention is configured by adding a depth direction analysis condition input capturing unit 116 and a heat conduction analysis processing unit 117 to the basic configuration shown in FIG. Others are the same as that of one Embodiment shown in FIG.
The depth direction analysis condition input capturing unit 116 has a function of capturing the analysis depth of the concrete and the heat conduction boundary condition input by the user. The parameters input and captured here are the heat conduction analysis processing unit. 117. Other basic data such as environment and structures are input from the DB 115 to the heat conduction analysis processing unit 117. Unsteady heat conduction of concrete using the finite element method based on the heat conduction boundary conditions previously taken in. Perform analysis. The heat conduction analysis processing unit 116 calculates the number of times of freezing and thawing per year based on the outside air temperature according to the basic data, and calculates the outside air temperature in the year with the highest number of times of freezing and thawing as well as the heat on the concrete surface in contact with the outside air. Conduct heat analysis inside concrete using heat transfer boundary modeled in advance for each transfer direction.

Further, the deterioration prediction calculation unit 113 calculates a deterioration curve inside the concrete based on the number of times of freezing and thawing calculated according to the internal temperature by the heat conduction analysis.
Specifically, frost damage deterioration characteristics due to at least one of aggregate, binder, air entrainment agent, and water saturation, based on the deterioration characteristic curve of concrete based on exposure tests at reference points in the natural environment Are converted into partial coefficients obtained by executing predetermined arithmetic expressions defined in advance. Then, the number of freeze-thaw cycles in the exposure test at the reference point is multiplied by the partial coefficient to obtain the reference deterioration curve at the predicted point. The reference deterioration curve is calculated based on the concrete internal temperature obtained from the previous heat conduction analysis result. Predict the deterioration curve inside the concrete using the minimum temperature of the day and the number of freeze-thaw cycles by minimum temperature, which is the number of times the freezing point is exceeded from the maximum temperature, and the failure function indicated by the water-cement ratio and the minimum temperature.

The processing procedure of another embodiment of the present invention shown in FIG. 18 will be described in detail below with reference to the flowcharts shown in FIGS.
In the figure, the processes shown in S171 to A179 are deterioration predictions for the surface layer portion of concrete, and are the same as those in the embodiment shown in FIG. 2, so the description thereof is omitted to avoid duplication. The concrete frost damage deterioration prediction apparatus 10 of the present invention first determines whether or not to calculate the deterioration depth in S180. Here, when it is designated by the user that calculation of the degradation depth is required, the depth direction analysis condition input fetching unit 116 receives designation from the user regarding the heat transfer boundary and depth, and takes this into the heat conduction analysis. The processing unit 117 is activated.
The heat conduction analysis processing unit 117 performs unsteady heat conduction analysis of the concrete using the finite element method based on the heat conduction boundary condition specified by the user, and passes the analysis result to the deterioration prediction calculation unit 113 (S182). ). The deterioration prediction calculation unit 113 identifies a deterioration curve for each depth based on the freeze / thaw frequency calculated based on the internal depth based on the heat conduction analysis (S183 to S186), and activates the deterioration prediction curve output unit 114 to be described later. A contour line such as degradation or a degradation curve at an arbitrary depth is displayed (S187, S188).

Details will be described below. In executing the heat conduction analysis, the user can input arbitrary values for the thermal conductivity, heat transfer coefficient, specific heat, and unit volume weight of the concrete.
The thermal conductivity is a coefficient representing the ease of heat transfer inside the concrete, and the heat transfer coefficient is a coefficient representing the ease of heat in and out of the concrete surface in contact with the outside air. As shown in FIG. 21, there are three types of boundary conditions: a model (a) with a heat transfer boundary as one direction, a model (b) with two directions, and a model (c) with four directions. did.
The model shown in FIG. 21A is a case where the influence of outside air from one direction in the Y direction is superior (exposed) compared to the X direction, and is applied to structures such as dams and retaining walls. Further, the model shown in FIG. 21B is a case where the influence of outside air from two directions in the Y direction is superior (exposed) compared to the X direction, and a member such as a beam or a slab, for example, Applies to structures such as floor boards, girders and walls. Further, the model shown in FIG. 21C is a case where the model is affected by outside air from both XY directions, and is applied to a pillar member such as a peer.

In the above-described embodiment of the present invention, as the degradation factor of the concrete, it is possible to examine the influence by factor by dividing into an internal factor due to the property of the concrete itself and an external factor due to the location of the facility, the surrounding environment, etc. In this way, a system was constructed to calculate the impact for each external and internal factor. The influence of the external factors examined here is limited to the concrete surface, and it is considered that the temperature distribution and the frequency of freezing and thawing are different inside the concrete. Therefore, heat conduction analysis inside the concrete is performed based on external factors and internal factors depending on the properties of the concrete itself, and the deterioration status inside the concrete is predicted from the result.
FIG. 22 shows a processing procedure for deterioration prediction, and FIG. 23 shows an example of a deterioration depth prediction screen. The deterioration depth prediction screen shown in FIG. 23 is displayed when the apparatus of the present invention is activated, and is divided into a basic setting window and a deterioration prediction calculation condition window.

The frost damage deterioration prediction apparatus 10 of the present invention first reads environmental data such as outside air temperature and solar radiation as external factors (S201), and also takes basic data set by the user (S202) to determine the representative year. After selection (S203), input data for heat conduction analysis is created.
The input data corresponding to the weather such as the outside temperature is AMeDAS data and JMA annual report (irradiation amount). AMeDAS data and JMA annual report shall be in CSV format. Since freeze-thaw occurs mainly in the winter period, year data or year data is converted from July of the current year to the weather year data format of June of the following year. For the missing period, values that are linearly interpolated with the previous and subsequent values shall be used.
The data specified by the user when calculating the impact due to external factors includes facility name, target year (completion year, evaluation year), regional designation (northern latitude, east longitude, altitude), sunshine in winter, impact of snow (snow cover period), etc. is there. The facility location is specified by latitude, longitude, and altitude, and the nearest observation point data among the input data is used. Internal factors are used to calculate the standard deterioration curve.

  In addition, when conducting heat conduction analysis inside concrete, the outside air temperature value of the corresponding period is used. Ambient temperature data such as AMeDAS data have been developed in order since 1976, and the temperature time value data may not be obtained depending on the completion time of the facility and the area. For this reason, in the period from the completion date of the facility to the evaluation year, one year in which data is prepared to some extent is set as the representative year, and the deterioration prediction is performed assuming that the temperature in the representative year is repeated every year. In addition, as described above, the year in which the number of times of freezing and thawing is the largest among the years in which the outside air temperature value is obtained within the period from the facility completion year to the evaluation year is as described above.

The data specified by the user at the time of executing the heat conduction analysis (S204) is the analysis depth, thermal conductivity, specific heat, unit volume weight, and heat transfer coefficient. Also, the outside air temperature time value and the solar radiation time value in the representative year are automatically created from the facility location information input at the time of selecting the representative year, and are used for execution of heat conduction analysis.
In preparing the outside air temperature time value, the altitude difference correction and the snow cover correction are performed in consideration of the difference in weather conditions between the observation point and the facility and the effect of snow cover. In the former, the temperature reduction rate due to the altitude is −0.6 ° C./100 m, and in the latter, the member covered with snow is not melted.
Then, using 0 ° C. as the reference for freezing and thawing, the number of times of freezing and thawing by minimum temperature is calculated for each year from the corrected temperature. The number of freeze / thaw cycles by minimum temperature for each year is displayed in a table and graph.

  In addition, the heat conduction analysis performs unsteady calculations in 1 hour steps. The calculation result is output as a file in a predetermined format and used for internal deterioration prediction. Depending on the depth of analysis, it may take a lot of time to calculate the heat conduction analysis, so the file structure is such that the heat conduction analysis results can be saved and read.

Since the number of times of freezing and thawing varies depending on the facility surface and depth, when performing internal deterioration prediction, first, a reference deterioration curve of the target facility concrete is calculated according to the value of the internal factor specified in the basic setting (S205).
Next, a deterioration curve inside the concrete is predicted based on the number of times of freezing and thawing calculated from the internal temperature of the heat conduction analysis result (S206). The reference deterioration curve of the facility can be determined by multiplying the number of cycles in the deterioration curve of the water reservoir adjustment pond exposure test shown by the following equation (1) by the ratio of the number of destruction cycles inside the facility.
REd = 100exp (−6.02 × 10 ⁻³ × cycle ^0.42 )

The contents output by the internal deterioration prediction are, for example, a deterioration distribution diagram shown in FIG. 24, a deterioration aging diagram shown in FIG. 27, and a deterioration cross-sectional view shown in FIG. In each of FIGS. 24 to 26, the respective designation screens can be displayed by clicking the degradation distribution button, the degradation aging diagram, and the degradation sectional view button corresponding to each diagram in the degradation depth prediction screen shown in FIG.
In the degradation depth prediction screen, the initial state is a state in which the degradation distribution map button is designated. In the initial state, in order to calculate and display the degradation distribution diagram, the coefficient to be displayed is selected and the execution button is clicked. As the coefficients to be displayed, four items can be selected: a relative dynamic elastic modulus (%), a relative static elastic modulus (%), a relative compressive strength (%), and a relative tensile strength (%).

In order to calculate and display the deterioration aging diagram, the deterioration aging map button is clicked on the deterioration depth prediction screen to switch to the screen for designating the deterioration aging diagram.
On the degradation depth prediction screen shown in FIG. 23, click a point to be evaluated, and add a check mark to □ (5 points MAX). At this time, the coordinates of the evaluation store position displayed on the right side can be confirmed, and if it is correct, the execution button can be clicked to display the deterioration aging diagram screen shown in FIG. Here, by selecting a coefficient to be displayed and clicking a drawing button, a deterioration aging diagram (relative kinematic elasticity coefficient) that matches the condition is displayed.

To calculate / display the degradation cross-sectional view, click the degradation cross-sectional view button on the degradation depth prediction screen to switch to the screen for designating the degradation cross-sectional view. The screen for designating a deteriorated sectional view has different analysis model shapes in one dimension and two dimensions.
In the case of a one-dimensional model, the year to be evaluated (Max 3 years) is input on the degradation depth prediction screen. Here, the evaluation year displayed on the right side is confirmed, and if it is correct, the execution button is clicked to display the degradation sectional view window shown in FIG. After that, by selecting a coefficient to be displayed and clicking a drawing button, a degradation cross-sectional view (relative kinematic elastic coefficient one-dimensional) shown in FIG. 28 can be displayed.
In the case of the two-dimensional case as well, the year to be evaluated and the cross-sectional position to be evaluated are input using the degradation depth prediction screen. Then, the evaluation year and the evaluation cross-section position displayed on the right side are confirmed, and if they are correct, the execution button is clicked to display a deteriorated cross-section window. Thereafter, the same procedure as described above can be executed to display a degraded cross-sectional view (two-dimensional relative dynamic elastic modulus).

29 and 30 are diagrams for explaining the verification result of the present invention. The verification result in the surface layer portion according to the embodiment of the present invention and the deterioration depth according to another embodiment of the present invention are shown. Each of the verification results is shown.
As shown in FIG. 29, it is considered that the evaluation results of the structures A, B, and C relatively correspond to the actual measurement values. Further, according to FIG. 30, it is recognized that the measured values are greatly deteriorated in the direction toward the surface portion although the variation is large due to the influence of the coarse aggregate or the like.
It can also be seen that the analytical values also roughly match the actual measurement values.

As described above, according to the present invention, first, a predicted point that reflects the characteristic value calculated by the characteristic value calculation unit based on the frost damage deterioration curve of concrete based on the exposure test at the reference point in the natural environment. By calculating the frost damage inferior curve, the concrete deterioration prediction system subject to freezing and thawing action can be constructed, and the concrete deterioration prediction curve under any concrete mix and natural environment can be accurately calculated.
Further, the present invention can quantitatively determine the quality of aggregates and binders, the influence of AE agent, water cement ratio and cracking as the ratio of the number of fracture cycles as characteristic values. By reflecting this in the calculation of the deterioration prediction curve, it is possible to appropriately perform the deterioration diagnosis of the concrete subjected to the freezing and thawing action. In addition, after calculating the deterioration curve of the concrete to be predicted, it is possible to easily determine the repair time according to the concrete performance by estimating the deterioration prediction curve considering cracks and repair effects. The accuracy of maintenance can be improved by repairing to.

In addition, the present invention secondly calculates the temperature distribution inside the concrete by unsteady heat conduction analysis in order to determine the degree of deterioration in the depth direction as well as the surface layer portion of the concrete, and calculates the number of times of freezing and thawing by depth. By estimating the deterioration depth as described above, it becomes possible to grasp the degree of deterioration in the depth direction when setting the chip thickness during actual repair and evaluating the load bearing capacity of the member. To further improve the accuracy.
Furthermore, when conducting heat conduction analysis by the finite element method, the concrete to be analyzed is roughly divided into several types and modeled in advance so that users can predict deterioration without being particularly conscious of model creation. Can be reduced. In addition, when conducting heat conduction analysis, the number of freeze-thaw cycles per year based on the outside air temperature is calculated over the entire period to be predicted, and the year with the highest number of freeze-thaw cycles is selected and implemented as the representative year. The analysis result can be acquired at a relatively high speed. In addition to limiting the period to be analyzed, by selecting the year with the largest number of freeze / thaw cycles as the representative year, it is possible to predict on the safe side when evaluating deterioration.

  2 and 6, the basic data input acquisition unit 111, the characteristic value calculation unit 112, the deterioration prediction calculation unit 113, the deterioration prediction curve output unit 114, and the depth direction analysis condition input acquisition unit. 116 and the procedure executed by each of the heat conduction analysis processing unit 117 are recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into the computer system and executed. Can be realized. The computer system here includes an OS and hardware such as peripheral devices.

Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.
The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
The program may be for realizing a part of the functions described above.
Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes a design and the like within a scope not departing from the gist of the present invention.

It is a schematic block diagram which shows one Embodiment of the frost damage deterioration prediction apparatus in this invention. It is the block diagram which expanded and showed the function of the internal structure of the frost damage deterioration prediction apparatus concerning one Embodiment of this invention. It is a flowchart which shows the process sequence of the frost damage deterioration prediction program concerning one Embodiment of this invention. It is the conceptual diagram quoted in order to demonstrate the relationship between concrete structure and water cement ratio. It is the figure which showed the relationship between the water cement ratio and the amount of freezing in a pore by the graph display. It is the figure which displayed the freeze-thaw test result which changed minimum temperature in the graph of 3 examples. It is the graph quoted in order to explain the relationship between the number of fracture cycles and the frozen pore volume ratio. It is the figure quoted in order to demonstrate the relationship when the number of destruction cycles is defined by the destruction function. It is the graph quoted in order to demonstrate the normalization process by a destruction function. It is the figure quoted in order to demonstrate the exposure test result of a water dam specimen, and the freezing and thawing frequency according to the minimum temperature of a water pond adjustment pond. It is a figure which shows the deterioration curve in the natural environment by the normalization freeze-thaw cycle method. It is a figure which shows an example when the number of times of freezing and thawing according to the minimum temperature (table and graph) is displayed on the screen. It is a figure which shows an example when the deterioration prediction curve (graph) is displayed on the screen by this invention. It is a figure which shows an example when the deterioration prediction curve (graph) which considered the crack was displayed on the screen by this invention. It is a figure which shows an example when the deterioration prediction curve (graph) which considered repair is displayed on the screen by this invention. It is a figure which shows the temperature distribution inside a member in the time of recording minimum temperature. It is a figure which shows the frequency | count of the freeze-thaw inside a member. It is the block diagram which expanded and showed the internal structure of the frost damage deterioration prediction apparatus concerning other embodiment of this invention. It is a flowchart which shows the process sequence of the frost damage deterioration prediction program of this invention. It is a flowchart which shows the process sequence of the frost damage deterioration prediction program of this invention. It is the figure quoted in order to demonstrate the boundary condition used in other embodiment of this invention. It is a flowchart which shows the process sequence of the frost damage deterioration prediction program concerning other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is a figure which shows an example of the screen structure used in other embodiment of this invention. It is the figure quoted in order to demonstrate the verification result concerning one Embodiment of this invention. It is the figure quoted in order to demonstrate the verification result concerning other embodiment of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Frost damage deterioration prediction apparatus, 11 ... CPU, 12 ... RAM, 13 ... Storage device (13a: Storage medium), 14 ... Input device, 15 ... Display device, 111 ... Basic data input taking-in part, 112 ... Characteristic value calculation part 113 ... Deterioration prediction calculation unit, 114 ... Deterioration prediction curve output unit, 116 ... Depth direction analysis condition input input unit, 117 ... Heat conduction analysis processing unit

Claims (4)

It is a frost damage deterioration prediction device for concrete that is built on a computer and is subjected to freezing and thawing action in a natural environment.
An analysis depth of the concrete input by a user, and a depth direction analysis condition input capturing unit for capturing a heat conduction boundary condition;
Based on the heat conduction boundary condition, a heat conduction analysis processing unit that performs unsteady heat conduction analysis of the concrete by a finite element method,
Based on the number of times of freezing and thawing calculated according to the internal depth by the heat conduction analysis, a deterioration prediction calculation unit that calculates a deterioration curve inside the concrete,
An apparatus for predicting frost damage deterioration of concrete, comprising: The heat conduction analysis processing unit
Calculate the number of freeze-thaw cycles per year based on the outside temperature according to the basic data obtained from outside, and calculate the outside temperature in the year with the highest number of freeze-thaw cycles and the heat transfer direction on the concrete surface in contact with the outside air in advance. 2. The concrete frost damage deterioration prediction apparatus according to claim 1, wherein the heat conduction analysis inside the concrete is performed using the modeled heat transfer boundary. The degradation prediction calculation unit
Based on the deterioration characteristic curve of concrete based on the exposure test at the reference point in the natural environment, the influence of the concrete on the frost damage deterioration characteristics due to at least one of aggregate, binder, air entraining agent and water saturation , Converted into partial coefficients obtained by executing predetermined arithmetic expressions defined in advance, respectively, and multiplying the number of freeze-thaw cycles of the exposure test at the reference point by the partial coefficient, the reference deterioration curve at the predicted point Based on the concrete internal temperature obtained from the heat conduction analysis result, the number of times of freezing and thawing by the minimum temperature and the number of cycles of fracture were measured. Is converted to a deterioration prediction curve inside the concrete using a fracture function indicated by the water cement ratio and the minimum temperature. Frost deterioration prediction apparatus of the concrete according to. A program used for a frost damage deterioration prediction device for concrete subjected to freeze-thaw action in a natural environment,
Capturing the analysis depth of the concrete and the heat conduction boundary condition input by the user;
Based on the heat conduction boundary condition, performing unsteady heat conduction analysis of the concrete by a finite element method,
Based on the freeze-thaw frequency calculated by the internal depth by the heat conduction analysis, calculating a deterioration curve inside the concrete;
This program predicts the deterioration of concrete caused by frost damage.

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