A method and system for calculating constitutive model of fiber concrete material

Abstract

The present invention discloses a method and system for calculating a constitutive model of a fiber concrete material, and belongs to the field of calculating a constitutive model of a fiber concrete material. The method comprises obtaining a stress-strain curve of the fiber concrete in a tensile state and a stress-strain curve in a compressive state respectively; obtaining a tensile data set and a compressive data set respectively according to the stress-strain curve in the tensile state and the stress-strain curve in the compressive state; obtaining a constitutive equation in a tensile state and a constitutive equation in a compressive state according to the tensile data set and the compressive data set by using a genetic programming algorithm; obtaining a constitutive model according to the constitutive equation in a tensile state and the constitutive equation in a compressive state. The present invention solves the problems of low efficiency and insufficient precision of the existing method for calculating a constitutive model of a fiber concrete material.

Description

Technical Field

[0001] The invention belongs to the field of constitutive model calculation of fiber concrete materials, and in particular relates to a constitutive model calculation method and a system thereof for fiber concrete materials. Background Art

[0002] In recent years, the research on civil engineering materials has developed rapidly, and high-performance concrete has always been a hot topic. The development of fiber concrete is an important way to improve the quality of high-performance concrete. Fiber concrete is usually a cement-based composite material composed of cement paste, mortar or concrete as the base material and discontinuous short fibers or continuous long fibers as the reinforcing material. Its main function is to limit the development of cracks through bridging, thereby improving the performance of concrete. However, there are few studies on the constitutive model of fiber concrete at present, and it is relatively complex and difficult to be widely used in various simulation software for simulation. With the rapid development of computer technology, the computing power level has long broken through the limit. It is urgent to propose a scientific and effective constitutive calculation model method for fiber concrete materials. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, a method and system for calculating a constitutive model of a fiber concrete material provided by the present invention solves the problems of low efficiency and insufficient precision of the existing method for calculating a constitutive model of a fiber concrete material.

[0004] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for calculating the constitutive model of fiber concrete material, comprising the following steps:

[0005] S1. Obtain the stress-strain curve of the fiber concrete in the tensile state and the stress-strain curve in the compressive state respectively;

[0006] S2. obtaining a tensile data set and a compressive data set according to the stress-strain curve in the tensile state and the stress-strain curve in the compressive state, respectively;

[0007] S3, according to the tensile data set and the compressive data set, using a genetic programming algorithm, obtaining a constitutive equation under a tensile state and a constitutive equation under a compressive state;

[0008] S4. According to the constitutive equation under tension and the constitutive equation under compression, a constitutive model is obtained.

[0009] The beneficial effects of the present invention are: collecting the stress-strain curve of fiber concrete under real conditions, and using the genetic programming algorithm to find the constitutive equation, while making full use of the computer computing power, digging out the hidden forms of the constitutive equation as much as possible, not missing other forms of constitutive equations, improving the accuracy of the constitutive model, and relying on computers for calculations to avoid the inefficiency caused by manpower.

[0010] Furthermore, the step S2 is specifically as follows:

[0011] S201, setting the number of screening points, and randomly selecting data points of the stress-strain curves under each tensile state and data points of the stress-strain curves under each compressive state according to the number of screening points, to obtain a tensile data point set and a compressive data point set respectively;

[0012] S202 , respectively obtaining values ​​of influencing factors of each data point in the stretched data point set and each data point in the compressed data point set, and merging them with the stretched data point set and the compressed data point set to obtain a stretched data set and a compressed data set.

[0013] The beneficial effect of the above further scheme is: collecting the stress-strain curve of the fiber concrete under real conditions, extracting data points according to the number of screening points, and avoiding excessive data to increase the calculation time.

[0014] Furthermore, the step S3 is specifically as follows:

[0015] S301, obtaining influencing factors of data points in a stretching data set and strains of data points in the stretching data set to obtain a stretching termination set;

[0016] S302, obtaining the influencing factors of the data points in the compressed data set and the strains of the data points in the compressed data set to obtain a compression termination set;

[0017] S303, setting function set, maximum number of iterations and population size;

[0018] S304, according to the population size and the function set, initializing based on the stretching termination set and the compression termination set respectively to obtain a stretching population and a compression population;

[0019] S305, calculating the fitness of each individual in the stretched population according to the stretched data set;

[0020] S306, calculating the fitness of each individual in the compressed population according to the compressed data set;

[0021] S307, judging whether the maximum number of iterations has been reached, if so, taking the individual with the smallest fitness in the stretched population as the constitutive equation in the stretched state, and taking the individual with the smallest fitness in the compressed population as the constitutive equation in the compressed state, otherwise, proceeding to step S308;

[0022] S308, according to the fitness of each individual in the stretched population and the fitness of each individual in the compressed population, using the tournament optimization method, replication, crossover and mutation, to obtain the stretched population offspring and the compressed population offspring;

[0023] S309. Obtain a stretched new population and a compressed new population according to the stretched population parent, stretched population child, compressed population parent and compressed population child, respectively, and update the stretched population to the stretched new population and the compressed population to the compressed new population, and return to step S305.

[0024] The beneficial effects of the above further scheme are: collecting the stress-strain curve of fiber concrete under real conditions, and using the genetic programming algorithm to find the constitutive equation, while making full use of the computer computing power, digging out the hidden forms of the constitutive equation as much as possible, not missing other forms of constitutive equations, improving the accuracy of the constitutive model, and relying on computers for calculations to avoid the inefficiency caused by manpower.

[0025] Furthermore, the fitness of each individual in the stretched population in step S305 is expressed as:

[0026]

[0027] in, To stretch the population The fitness of each individual; To filter points; For the stretched data set The error value of the stretching data; For the stretched data set The true stress of the tensile data; For the stretched data set Predicted stress for tensile data; is an absolute value.

[0028] The beneficial effect of the above further scheme is: using the error value as the individual fitness of the stretched population to provide a direction for the constitutive equation search, while gradually reducing the individual error and improving the accuracy of the constitutive equation.

[0029] Furthermore, the fitness of each individual in the compressed population in step S305 is expressed as:

[0030]

[0031] in, To stretch the population The fitness of each individual; To filter points; To compress the data set The error value of compressed data; To compress the data set The true stress of the compression data; To compress the data set Predicted stress for compression data; is an absolute value.

[0032] The beneficial effect of the above further scheme is: using the error value as the individual fitness of the compressed population to provide a direction for the constitutive equation search, while gradually reducing the individual error and improving the accuracy of the constitutive equation.

[0033] Furthermore, the step S308 is specifically as follows:

[0034] S3081, setting the number of sample extractions, stretching subgroups and compressing subgroups;

[0035] S3082. According to the number of sample extractions, randomly extract individuals from the stretched population and the compressed population to obtain a stretched subgroup and a compressed subgroup;

[0036] S3083, respectively determine whether there are individuals with the same fitness in the stretching subgroup and the compression subgroup, if so, remove the individuals with longer nodes from the stretching subgroup or the compression subgroup, and proceed to step S3084, otherwise, directly proceed to step S3084;

[0037] S3084, selecting the individual with the smallest fitness in the stretching subgroup as the stretching parent, and performing replication, crossover and mutation on the stretching parent to obtain a stretching offspring, and adding the stretching offspring to the stretching subgroup;

[0038] S3085, selecting the individual with the smallest fitness in the compressed subgroup as the compressed parent, and performing replication, crossover and mutation on the compressed parent to obtain a compressed offspring, and adding the compressed offspring to the compressed subgroup;

[0039] S3086. Determine whether the size of the stretching subgroup and the compression subgroup is smaller than the population size. If so, return to step S3082. Otherwise, treat the stretching subgroup as a descendant of the stretching population, and treat the compression subgroup as a descendant of the compression population.

[0040] The beneficial effects of the above further scheme are: constructing the population offspring, while making full use of the computer computing power, digging out the hidden forms of the constitutive equation as much as possible, not missing other forms of the constitutive equation, improving the accuracy of the constitutive model, and relying on computers for calculations to avoid the inefficiency caused by manpower.

[0041] The present invention provides a fiber concrete material constitutive model calculation system, comprising a processor and a memory;

[0042] The processor is used to obtain a constitutive model using a genetic programming algorithm according to a stress-strain curve of the fiber concrete in a tensile state and a stress-strain curve in a compressive state;

[0043] The memory is used to store the stress-strain curve of the fiber concrete in a tensile state, the stress-strain curve in a compressive state, and the constitutive model.

[0044] The beneficial effects of the present invention are as follows: using a genetic programming algorithm to search for constitutive equations, while fully utilizing computer computing power, digging out hidden forms of constitutive equations as much as possible, not missing other forms of constitutive equations, improving the accuracy of the constitutive model, and relying on computers for calculations to avoid the inefficiency caused by manpower; at the same time, the constitutive models that have been calculated are stored for easy subsequent search and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flow chart of the method.

[0046] Figure 2 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0048] Example 1

[0049] like Figure 1 As shown, in one embodiment of the present invention, a method for calculating a constitutive model of a fiber concrete material comprises the following steps:

[0050] S1. Obtain the stress-strain curve of the fiber concrete in the tensile state and the stress-strain curve in the compressive state respectively;

[0051] S2. obtaining a tensile data set and a compressive data set according to the stress-strain curve in the tensile state and the stress-strain curve in the compressive state, respectively;

[0052] S3, according to the tensile data set and the compressive data set, using a genetic programming algorithm, obtaining a constitutive equation under a tensile state and a constitutive equation under a compressive state;

[0053] S4. According to the constitutive equation under tension and the constitutive equation under compression, a constitutive model is obtained.

[0054] In this embodiment, the obtained constitutive model can be used for mechanical analysis of engineering structures and foundations, and for engineering stability analysis.

[0055] The step S2 is specifically as follows:

[0056] S201, setting the number of screening points, and randomly selecting data points of the stress-strain curves under each tensile state and data points of the stress-strain curves under each compressive state according to the number of screening points, to obtain a tensile data point set and a compressive data point set respectively;

[0057] S202 , respectively obtaining values ​​of influencing factors of each data point in the stretched data point set and each data point in the compressed data point set, and merging them with the stretched data point set and the compressed data point set to obtain a stretched data set and a compressed data set.

[0058] In this embodiment, the GP method requires input and output data to find the mathematical relationship between them. The output of GP is the stress of fiber concrete (i.e., the functional relationship between the independent variable and the dependent variable, where the stress of fiber concrete is the dependent variable). Since the mechanical properties of fiber concrete in compression and tension are different, and the types of factors affecting the effective stress-strain relationship of the fiber in uniaxial tension and compression are not completely consistent, it is necessary to discuss these two situations separately and use different input variables to find the constitutive equation (supplement: that is, two equations about the material will be obtained in the end, one for compression and the other for tension).

[0059] ① The factors affecting the effective stress-strain relationship of fibers under uniaxial compression can be the compressive strength A1 of concrete, the strain A2 when the concrete reaches the compressive strength A1, the fiber volume content A3, the fiber length A4, the fiber diameter A5 and the fiber tensile strength A6. The compressive strength A1 of concrete and the strain A2 when the concrete reaches the compressive strength A1 are the mechanical properties of the concrete matrix, which can be directly obtained by referring to the data and specifications according to the concrete grade or by performing a compression test on the concrete; the fiber volume content A3, the fiber length A4, the fiber diameter A5 and the fiber tensile strength A6 are the basic properties of the fiber, which can be directly obtained according to the product introduction of the manufacturer. After the influencing factors of A1-A6 are determined, the uniaxial compression test of the fiber concrete can be carried out to obtain the unique corresponding stress Ya-strain Xa curve of the fiber concrete under the uniaxial compression state; when the influencing factors of A1-A6 change, the stress Ya-strain Xa curve will also change accordingly.

[0060] ② The factors affecting the effective stress-strain relationship of fibers under uniaxial tension can be the compressive strength of concrete B1, the tensile strength of concrete B2, the fiber volume content B3, the fiber length B4, the fiber diameter B5 and the tensile strength B6 of the fiber. The compressive strength B1 and the tensile strength B2 of concrete are the mechanical properties of the concrete matrix, which can be directly obtained by referring to the data and specifications according to the concrete grade or by performing compression and tensile tests on concrete; the fiber volume content B3, the fiber length B4, the fiber diameter B5 and the tensile strength B6 of the fiber are the basic properties of the fiber, which can be directly obtained according to the product introduction of the manufacturer. After the influencing factors of B1-B6 are determined, the uniaxial tensile test of the fiber concrete can be carried out to obtain the unique corresponding stress Yb-strain Xb curve of the fiber concrete under uniaxial tension; when the influencing factors of B1-B6 change, the stress Yb-strain Xb curve will also change accordingly.

[0061] In addition, other influencing factors may be added as needed, such as the bending angle and bending length of the fiber end hook, etc., and the method is the same.

[0062] The obtained stress-strain relationship data of the tensile and compression tests of fiber concrete components are summarized into two tables of tension and compression. Take the compression state as an example, and the same applies to the tension state. In order to find the constitutive equation more conveniently, 14 pairs of data (Xa, Ya) can be selected for each stress-strain relationship curve, and the 14 pairs of data should be able to roughly describe the shape of the curve; 30-0.5, 50-0.5, 30-0.75, 50-75, 30-1.0, 50-1.0, 30-1.5 and 50-1.5 in Table 1 are all compression input data group numbers. The input data in the tensile state are A1, A2, A3, A4, A5, A6, Xa, and the output data is Ya. The GP method is used to find the constitutive equation between them. To find the constitutive equation by the GP method, at least 30 stress-strain curves should be collected. As more data are collected, the error of the constitutive equation obtained by this method is smaller and the applicability is stronger. Table 1 is a data diagram of factors affecting the effective stress-strain relationship of the fiber; Table 2 is a data diagram of 14 pairs of data selected from the stress Ya-strain Xa curve.

[0063] Table 1

[0064]

[0065] Table 2

[0066]

[0067] Finally, the GP method obtains the constitutive equation under compression , constitutive equation under tension .

[0068] After completing the process of discovering the constitutive equation under compression, this method can determine the specific values ​​of the influencing factors A1, A2, A3, A4, A5, and A6, and obtain the accurate stress-strain relationship of the influencing factors under compression.

[0069] The step S3 is specifically as follows:

[0070] S301, obtaining influencing factors of data points in a stretching data set and strains of data points in the stretching data set to obtain a stretching termination set;

[0071] S302, obtaining the influencing factors of the data points in the compressed data set and the strains of the data points in the compressed data set to obtain a compression termination set;

[0072] S303, setting function set, maximum number of iterations and population size;

[0073] S304, according to the population size and the function set, initializing based on the stretching termination set and the compression termination set respectively to obtain a stretching population and a compression population;

[0074] S305, calculating the fitness of each individual in the stretched population according to the stretched data set;

[0075] S306, calculating the fitness of each individual in the compressed population according to the compressed data set;

[0076] S307, judging whether the maximum number of iterations has been reached, if so, taking the individual with the smallest fitness in the stretched population as the constitutive equation in the stretched state, and taking the individual with the smallest fitness in the compressed population as the constitutive equation in the compressed state, otherwise, proceeding to step S308;

[0077] S308, according to the fitness of each individual in the stretched population and the fitness of each individual in the compressed population, using the tournament optimization method, replication, crossover and mutation, to obtain the stretched population offspring and the compressed population offspring;

[0078] S309. Obtain a stretched new population and a compressed new population according to the stretched population parent, stretched population child, compressed population parent and compressed population child, respectively, and update the stretched population to the stretched new population and the compressed population to the compressed new population, and return to step S305.

[0079] In this embodiment, there are three methods for initializing the stretched population and the compressed population: dividing the node set of the spanning tree into a termination set and a non-termination set, the non-termination set contains parameter function operators, and the termination set contains influencing factor variables, set constants and parameterless function operators.

[0080] ① Randomly take a function from the non-termination set as the root node, and randomly take a function from the non-termination set as the child node until the depth limit is reached. When the specified depth is reached, only the terminator can be randomly taken from the termination set as the leaf node.

[0081] ② In the standard process, each new node is randomly selected from the termination set and non-termination set until the depth limit is reached (or it stops automatically because all connectable nodes are randomly selected to the terminator or no-parameter function). The tree structure created in this way may be very unbalanced, with some branches much longer than others.

[0082] ③This method specifies a depth range (rather than a single depth limit) so that half of the total number of individuals in the population are generated using the first method and the other half using the second method. This method removes the single depth limit and has a mixed use of ① and ②, which enhances diversity in terms of tree shape and size (and is still simple and easy to implement).

[0083] In actual application, the parameters are generally set to type ③.

[0084] In this embodiment, the "tournament optimization" method is similar to the "tournament" method. Just like in the "tournament", a certain number of individuals are randomly selected from the population and the best one is selected from them. The main difference is that if two individuals are equally fit, the shortest tree (the one with fewer nodes) is selected as the best.

[0085] The "tournament" method is to take a certain number of individuals from the population each time (called the competition size), and then select the best one to enter the next generation population. Repeat this operation until the new population size reaches the original population size.

[0086] The fitness expression of each individual in the stretched population in step S305 is:

[0087]

[0088] in, To stretch the population The fitness of each individual; To filter points; For the stretched data set The error value of the stretching data; For the stretched data set The true stress of the tensile data; For the stretched data set Predicted stress for tensile data; is an absolute value.

[0089] The fitness of each individual in the compressed population in step S305 is expressed as:

[0090]

[0091] in, To stretch the population The fitness of each individual; To filter points; To compress the data set The error value of compressed data; To compress the data set The true stress of the compression data; To compress the data set Predicted stress for compression data; is an absolute value.

[0092] In this embodiment, the absolute error of the GP code is defined as the objective function. That is, the objective function of each equation in the GP calculation operation is the sum of the absolute values ​​of the differences between the experimental results of all sample points and the equation. The larger the absolute error of the equation, the less accurate the result obtained, and the best individual in the calculation is screened out by the fitness function.

[0093] Take compression as an example, the same goes for stretching (the formulas are all B, b).

[0094] The input data and output data sets obtained by statistics are recorded as (A1, A2, A3, A4, A5, A6, Xa; Ya). In this way, there are L data sets obtained by statistics. For each data set, the function equation is obtained by GP method. , Substitute the input data (A1, A2, A3, A4, A5, A6, Xa) into the equation to obtain the stress calculated by the GP method , calculate the single data error value , where |a| means taking the absolute value of a (e.g. |-8|=8, |6|=6), and Ya means the statistical stress. It represents the absolute error value of the equation calculated by the GP method for a single data, and the fitness function (objective function) is recorded as , which is the cumulative absolute error value of all statistical points. The fitness is calculated for each function equation obtained by the GP method.

[0095] The step S308 is specifically as follows:

[0096] S3081, setting the number of sample extractions, stretching subgroups and compressing subgroups;

[0097] S3082. According to the number of sample extractions, randomly extract individuals from the stretched population and the compressed population to obtain a stretched subgroup and a compressed subgroup;

[0098] S3083, respectively determine whether there are individuals with the same fitness in the stretching subgroup and the compression subgroup, if so, remove the individuals with longer nodes from the stretching subgroup or the compression subgroup, and proceed to step S3084, otherwise, directly proceed to step S3084;

[0099] S3084, selecting the individual with the smallest fitness in the stretching subgroup as the stretching parent, and performing replication, crossover and mutation on the stretching parent to obtain a stretching offspring, and adding the stretching offspring to the stretching subgroup;

[0100] S3085, selecting the individual with the smallest fitness in the compressed subgroup as the compressed parent, and performing replication, crossover and mutation on the compressed parent to obtain a compressed offspring, and adding the compressed offspring to the compressed subgroup;

[0101] S3086. Determine whether the size of the stretching subgroup and the compression subgroup is smaller than the population size. If so, return to step S3082. Otherwise, treat the stretching subgroup as a descendant of the stretching population, and treat the compression subgroup as a descendant of the compression population.

[0102] In this embodiment, the genetic operator is set to give the genetic programming algorithm a larger search space so that it does not converge to a local optimal solution. The genetic operator parameters usually set in the program are as follows:

[0103] ① In tree intersection, nodes are randomly selected from two parent trees and their respective branches are exchanged to create two subtrees. This method is equally likely to select internal nodes or terminal nodes as intersection points.

[0104] ②In tree mutation, a random node is selected from the parent tree and replaced by a new random tree with available terminals and functions.

[0105] ③In the exchange mutation, two subtrees are randomly selected from the parent tree and exchanged.

[0106] Example 2

[0107] like Figure 2 As shown, the present invention provides a fiber concrete material constitutive model calculation system, including a processor and a memory;

[0108] The processor is used to obtain a constitutive model using a genetic programming algorithm according to a stress-strain curve of the fiber concrete in a tensile state and a stress-strain curve in a compressive state;

[0109] The memory is used to store the stress-strain curve of the fiber concrete in a tensile state, the stress-strain curve in a compressive state, and the constitutive model.

[0110] In this embodiment, the calculated constitutive model can be incorporated into the computer database system. The fiber concrete constitutive model in this database system can include steel fiber concrete, glass fiber concrete, polypropylene fiber concrete, etc. As the test is carried out continuously or more data is collected, the constitutive model calculated by the computer is also updated, and when the computing power level is improved, the accuracy of the constitutive model will be higher, and the database system will also be updated. If other subsequent studies need to use the constitutive model of fiber concrete, the fiber concrete constitutive model that meets the needs of the current research can be directly called from the database system, which provides great convenience for related research. Figure 2 is a system structure diagram. It includes: at least one processor, a memory, a computer program running on at least one processor and a database system stored in the memory, and the program is a program for completing the above-mentioned GP method to solve the constitutive model of fiber concrete. The terminal device can be a desktop computer, a notebook computer, or a cloud server. The processor can be a central processing unit (CPU) or other general-purpose processor that can realize the required functions. The memory can be an internal storage unit of the terminal device, such as a hard disk or memory, or an external storage device, such as a plug-in hard disk, a secure digital card (SD) or a flash card.

Claims

1. A calculation method for the constitutive model of fiber concrete material, It is characterized in that The following steps are involved: S1. Obtain the stress-strain curve of the fiber concrete in the tensile state and the stress-strain curve in the compressive state respectively; S2. obtaining a tensile data set and a compressive data set according to the stress-strain curve in the tensile state and the stress-strain curve in the compressive state, respectively; S3, according to the tensile data set and the compression data set, using a genetic programming algorithm, obtain the constitutive equation under the tensile state and the constitutive equation under the compression state; the step S3 is specifically: S301, obtaining influencing factors of data points in a stretching data set and strains of data points in the stretching data set to obtain a stretching termination set; S302, obtaining the influencing factors of the data points in the compressed data set and the strains of the data points in the compressed data set to obtain a compression termination set; S303, setting function set, maximum number of iterations and population size; S304, according to the population size and the function set, initializing based on the stretching termination set and the compression termination set respectively to obtain a stretching population and a compression population; S305, calculating the fitness of each individual in the stretched population according to the stretched data set; S306, calculating the fitness of each individual in the compressed population according to the compressed data set; S307, judging whether the maximum number of iterations has been reached, if so, taking the individual with the smallest fitness in the stretched population as the constitutive equation in the stretched state, and taking the individual with the smallest fitness in the compressed population as the constitutive equation in the compressed state, otherwise, proceeding to step S308; S308, according to the fitness of each individual in the stretched population and the fitness of each individual in the compressed population, using the tournament optimization method, replication, crossover and mutation, to obtain the stretched population offspring and the compressed population offspring; S309, according to the stretched population parent, the stretched population child, the compressed population parent and the compressed population child, respectively obtain a stretched new population and a compressed new population, and update the stretched population to the stretched new population, and update the compressed population to the compressed new population, and return to step S305; S4. According to the constitutive equation under tension and the constitutive equation under compression, a constitutive model is obtained.

2. According to the calculation method of the constitutive model of fiber concrete material according to claim 1, It is characterized in that The step S2 is specifically as follows: S201, setting the number of screening points, and randomly selecting data points of the stress-strain curves under each tensile state and data points of the stress-strain curves under each compressive state according to the number of screening points, to obtain a tensile data point set and a compressive data point set respectively; S202 , respectively obtaining values ​​of influencing factors of each data point in the stretched data point set and each data point in the compressed data point set, and merging them with the stretched data point set and the compressed data point set to obtain a stretched data set and a compressed data set.

3. According to the calculation method of the constitutive model of fiber concrete material according to claim 1, It is characterized in that The fitness expression of each individual in the stretched population in step S305 is: in, To stretch the population The fitness of each individual; To filter points; For the stretched data set The error value of the stretching data; For the stretched data set The true stress of the tensile data; For the stretched data set Predicted stress for tensile data; is an absolute value.

4. According to the calculation method of the constitutive model of fiber concrete material according to claim 1, It is characterized in that The fitness of each individual in the compressed population in step S305 is expressed as: in, To stretch the population The fitness of each individual; To filter points; To compress the data set The error value of compressed data; To compress the data set The true stress of the compression data; To compress the data set Predicted stress for compression data; is an absolute value.

5. According to the calculation method of the constitutive model of fiber concrete material according to claim 1, It is characterized in that The step S308 is specifically as follows: S3081, setting the number of sample extractions, stretching subgroups and compressing subgroups; S3082. According to the number of sample extractions, randomly extract individuals from the stretched population and the compressed population to obtain a stretched subgroup and a compressed subgroup; S3083, respectively determine whether there are individuals with the same fitness in the stretching subgroup and the compression subgroup, if so, remove the individuals with longer nodes from the stretching subgroup or the compression subgroup, and proceed to step S3084, otherwise, directly proceed to step S3084; S3084, selecting the individual with the smallest fitness in the stretching subgroup as the stretching parent, and performing replication, crossover and mutation on the stretching parent to obtain a stretching offspring, and adding the stretching offspring to the stretching subgroup; S3085, selecting the individual with the smallest fitness in the compressed subgroup as the compressed parent, and performing replication, crossover and mutation on the compressed parent to obtain a compressed offspring, and adding the compressed offspring to the compressed subgroup; S3086. Determine whether the size of the stretching subgroup and the compression subgroup is smaller than the population size. If so, return to step S3082. Otherwise, treat the stretching subgroup as a descendant of the stretching population, and treat the compression subgroup as a descendant of the compression population.

6. A fiber concrete material constitutive model calculation system according to any one of claims 1 to 5, It is characterized in that including a processor and a memory; The processor is used to obtain a constitutive model using a genetic programming algorithm according to a stress-strain curve of the fiber concrete in a tensile state and a stress-strain curve in a compressive state; The memory is used to store the stress-strain curve of the fiber concrete in a tensile state, the stress-strain curve in a compressive state, and the constitutive model.

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