Data augmentation method and device, storage medium and computer program product

Abstract

The invention discloses a data augmentation method and device, a storage medium and a computer program product, and relates to the technical field of bridge performance prediction.The method comprises the steps that steel bar corrosion rate space distribution is obtained, and a steel bar corrosion rate standard deviation is determined according to the steel bar corrosion rate space distribution; determining a logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar through a fitting method; based on the standard deviation and the average corrosion rate, utilizing a Nataf transformation method to simulate steel bar corrosion rate space distribution so as to generate steel bar corrosion rate distribution obeying logarithm distribution, and inputting the steel bar corrosion rate distribution into a reinforced concrete beam finite element model to obtain crack width distribution; and circularly executing the steps until a termination condition is reached, and returning to establish the mapping relation between the steel bar corrosion rate spatial distribution and the crack width distribution after the operation is ended. According to the method and the device, the problem that the data of the sequence-sequence mapping relation obtained under the existing laboratory condition is very limited can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of long-term performance degradation prediction of bridges, and in particular to a data augmentation method and its device, storage medium and computer program product. Background Art

[0002] In chloride environments, steel corrosion is a major cause of performance degradation of reinforced concrete structures. The passive film of steel bars is relatively stable in the highly alkaline environment of concrete. When the passive film of steel bars is destroyed by chloride ion erosion, the steel bars will corrode. The volume expansion of corrosion products will cause cracking or even peeling of the protective layer of concrete. The corrosive media outside the structure can more easily reach the surface of the steel bars, causing the corrosion process to accelerate. With the significant decrease in the cross-sectional area of ​​the steel bars and the bond between concrete and steel bars, the bearing capacity is also reduced, resulting in service performance failure and degradation of long-term structural performance.

[0003] Among the structural inspection methods, visual inspection is a low-cost and general technology for evaluating the degree of deterioration of existing reinforced concrete structures, and has been widely used in the formulation of maintenance strategies for existing bridges. For corroded reinforced concrete structures, the width of the concrete surface crack caused by steel corrosion is the most commonly used visual inspection indicator. Many researchers have tried to link the steel corrosion state (i.e., cross-sectional area loss) with the crack width, and have proposed many empirical models and mechanical models to describe the relationship between the degree of steel corrosion and the crack width. Once the random variables involved in predicting steel corrosion are determined and the relationship between the amount of steel corrosion and the crack width is established, the renewal theory and nonlinear filtering techniques can be applied to reduce the uncertainty of predicting structural performance degradation.

[0004] Due to the combined influence of multiple factors, such as different environmental exposure conditions, concrete cover thickness, and construction quality, the corrosion of steel bars is spatially non-uniformly distributed. At the same time, the crack width of the surface corrosion of bridge structures is also random and non-uniformly distributed in space. The structural bearing capacity of reinforced concrete beam components strongly depends on the local conditions of their steel bars. Ignoring the spatial variability of steel bar corrosion will oversimplify the prediction of the remaining service life of bridge structures. Therefore, it is very important to establish the relationship between the distribution of steel bar corrosion rate and the distribution of crack width (i.e., the mapping relationship from sequence to sequence) for the performance analysis and prediction of bridge structures. However, the data for obtaining such sequence-sequence mapping relationship under existing laboratory conditions is very limited. Summary of the invention

[0005] The main purpose of this application is to propose a data augmentation method and its equipment, storage medium and computer program product, aiming to solve the problem of very limited data for obtaining such sequence-sequence mapping relationship under existing laboratory conditions.

[0006] To achieve the above objectives, the data augmentation method proposed in this application includes: Obtain the spatial distribution of the corrosion rate of the steel bar along the length direction, determine the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determine the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar by a fitting method; Based on the standard deviation of steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution. The distribution is then input into the reinforced concrete beam finite element model to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution. The steps of looping and executing are to obtain the spatial distribution of the corrosion rate of the steel bar along the length direction, determine the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determine the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar by fitting method, and the steps of simulating the spatial distribution of the corrosion rate of the steel bar along the length direction based on the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution, and input it into the finite element model of the reinforced concrete beam to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution, until the termination condition is reached, the operation ends and returns to establish the mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width distribution.

[0007] In one embodiment, the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate is:

[0008] in, is the standard deviation of steel bar corrosion rate, is the average corrosion rate of steel bars.

[0009] In one embodiment, the step of simulating the spatial distribution of the corrosion rate of the steel bars along the length direction by using the Nataf transformation method based on the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution, and inputting it into the reinforced concrete beam finite element model to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution specifically includes: Based on the standard deviation of steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution. The calibration coefficient is obtained, and the steel corrosion rate distribution that obeys the logarithmic distribution is input into the steel corrosion expansion model to calculate the unit node expansion displacement applied to the steel-concrete interface; The expansion displacement of the unit node applied to the reinforced concrete interface is input into the finite element model of reinforced concrete beam to simulate the non-uniform corrosion around the steel bars and along the steel bar axis, and the crack width distribution corresponding to the steel bar corrosion rate distribution obeying the logarithmic distribution is obtained.

[0010] In one embodiment, the non-uniform corrosion around the steel bars will generate non-uniform radial expansion pressure on the surrounding concrete. A corrosion distribution curve is used to express the corrosion expansion behavior of each steel bar section. The expression of the corrosion distribution curve is:

[0011] in, is the function value corresponding to the corrosion distribution curve, is the original radius of the reinforcement, is the maximum corrosion layer thickness closest to the concrete surface, is the thickness of the corrosion layer on the side away from the concrete surface.

[0012] In one embodiment, the relationship between the expansion displacement of the unit node applied to the reinforced concrete interface is:

[0013] in, is the expansion displacement of the unit node applied to the reinforced concrete interface, and is also the maximum corrosion layer thickness closest to the concrete surface; η is the corrosion rate measured experimentally, is the calibration coefficient, R is the original radius of the steel bar, is the thickness of the porous zone formed at the steel / concrete interface.

[0014] In one embodiment, the steps of establishing the finite element model of the reinforced concrete beam specifically include: The fracture softening curve is input into the concrete model to simulate the tensile behavior of concrete; Based on the tensile behavior of concrete, a finite element model of reinforced concrete beam is established.

[0015] In one embodiment, the steps of obtaining the corrosion rate distribution of the steel bar along the length direction specifically include: Acquire design parameters of the reinforced concrete beam component, wherein the design parameters include the length, cross-sectional dimensions, concrete strength, concrete cover thickness, and steel bar diameter of the reinforced concrete beam component; Based on the design parameters of reinforced concrete beams, an accelerated corrosion experiment was carried out on reinforced concrete beam components to obtain the spatial distribution of corrosion rate of the steel bars along the length direction.

[0016] In addition, to achieve the above-mentioned purpose, the present application also proposes a data augmentation device, which includes a memory, a processor, and a data augmentation program stored in the memory and executable on the processor, wherein the data augmentation program is configured to implement the steps of the data augmentation method described above.

[0017] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a data augmentation program is stored on the storage medium. When the data augmentation program is executed, the steps of the data augmentation method described above are implemented.

[0018] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a data augmentation program, and when the data augmentation program is executed, the steps of the data augmentation method described above are implemented.

[0019] The present application obtains the spatial distribution of the corrosion rate of the steel bar along the length direction, determines the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determines the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar through a fitting method; based on the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar, the spatial distribution of the corrosion rate of the steel bar along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution, and inputs it into the finite element model of the reinforced concrete beam to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution; the above steps are executed cyclically until the termination condition is reached, and the operation ends and returns to establish a mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width distribution. In this way, the present application can generate artificial data samples with the same characteristics as the experimental samples, realize the expansion of the relationship data between the spatial distribution of steel corrosion rate and the crack width distribution, predict the spatial distribution of steel corrosion rate inside the concrete structure by detecting the crack width distribution on the surface of reinforced concrete beam components, and realize the probabilistic prediction of the long-term performance of the concrete structure. It provides a new data basis for establishing performance degradation evaluation of corroded concrete structures based on detection information, and also provides data-level support for the performance evaluation of bridge components affected by corrosion in coastal environments, which has far-reaching significance for the formulation of maintenance strategies for bridges throughout their life cycle under the influence of corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 A flowchart of an embodiment of a data augmentation method of the present application is provided; Figure 2 A flowchart diagram of another embodiment of the data augmentation method of the present application is provided; Figure 3 A flowchart diagram of another embodiment of the data augmentation method of the present application is provided; Figure 4 A flowchart diagram of another embodiment of the data augmentation method of the present application is provided; Figure 5 A schematic diagram of an embodiment of a data augmentation method of the present application; Figure 6 A schematic diagram of the front structure of a reinforced concrete beam member provided in accordance with an embodiment of the data augmentation method of the present application; Figure 7 A schematic diagram of the side structure of a reinforced concrete beam member provided in an embodiment of the data augmentation method of the present application; Figure 8 A schematic diagram of a reinforced concrete beam finite element model provided in accordance with an embodiment of a data augmentation method of the present application; Fig. 9 A distribution diagram of steel bar corrosion rate that obeys logarithmic distribution provided in the first embodiment of the data augmentation method of the present application; Fig.10 A schematic diagram of a steel bar corrosion expansion model provided in an embodiment of a data augmentation method of the present application; Fig.11 A schematic diagram of a steel bar corrosion expansion model provided in another embodiment of the data augmentation method of the present application; Fig.12 A data sample scatter plot of the mapping relationship between the steel bar corrosion rate distribution and the crack width distribution generated by the first embodiment of the data augmentation method of the present application; Fig.13 This is a schematic diagram of the device structure of the hardware operating environment involved in the data augmentation method of this application.

[0023] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0025] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0026] In chloride environments, steel corrosion is a major cause of performance degradation of reinforced concrete structures. The passive film of steel bars is relatively stable in the highly alkaline environment of concrete. When the passive film of steel bars is destroyed by chloride ion erosion, the steel bars will corrode. The volume expansion of corrosion products will cause cracking or even peeling of the protective layer of concrete. The corrosive media outside the structure can more easily reach the surface of the steel bars, causing the corrosion process to accelerate. With the significant decrease in the cross-sectional area of ​​the steel bars and the bond between concrete and steel bars, the bearing capacity is also reduced, resulting in service performance failure and degradation of long-term structural performance.

[0027] Among the structural inspection methods, visual inspection is a low-cost and general technology for evaluating the degree of deterioration of existing reinforced concrete structures, and has been widely used in the formulation of maintenance strategies for existing bridges. For corroded reinforced concrete structures, the width of the concrete surface crack caused by steel corrosion is the most commonly used visual inspection indicator. Many researchers have tried to link the steel corrosion state (i.e., cross-sectional area loss) with the crack width, and have proposed many empirical models and mechanical models to describe the relationship between the degree of steel corrosion and the crack width. Once the random variables involved in predicting steel corrosion are determined and the relationship between the amount of steel corrosion and the crack width is established, the renewal theory and nonlinear filtering techniques can be applied to reduce the uncertainty of predicting structural performance degradation.

[0028] Due to the combined influence of multiple factors, such as different environmental exposure conditions, concrete cover thickness, and construction quality, the corrosion of steel bars is spatially non-uniformly distributed. At the same time, the crack width of the corrosion on the surface of bridge structures is also random and non-uniformly distributed in space. The structural bearing capacity of reinforced concrete beam components strongly depends on the local conditions of their steel bars. Ignoring the spatial variability of steel bar corrosion will oversimplify the prediction of the remaining service life of bridge structures. Therefore, it is very important to establish the relationship between the distribution of steel bar corrosion rate and the distribution of crack width (i.e., the sequence-to-sequence mapping relationship) for the performance analysis and prediction of bridge structures. However, the existing data for obtaining such sequence-sequence mapping relationships under laboratory conditions is very limited.

[0029] To solve the above problems, this application proposes a data augmentation method.

[0030] It should be noted that the execution subject of the data augmentation method of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions. The following takes the execution subject being a data augmentation device as an example to illustrate this embodiment and the following embodiments.

[0031] Based on this, the present application embodiment provides a data augmentation method, please refer to Figure 1 , the data augmentation method includes S10~S30: S10, obtaining the spatial distribution of the corrosion rate of the steel bar along the length direction, determining the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determining the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar by a fitting method; In this embodiment, the design parameters of the reinforced concrete beam component are manually obtained, which may include but are not limited to the length of the component, the cross-sectional size, the concrete strength, the thickness of the protective layer, the diameter of the steel bar, the type of the steel bar, the yield strength and tensile strength of the steel bar, the reinforcement ratio (the number, diameter, and spacing of the main bars and stirrups), and the placement position of the steel bar. In this way, the proportion of the experimental model can be designed based on the similarity theory to ensure that the model can reflect the mechanical behavior of the prototype in the experiment, and the environmental conditions of the experiment (such as temperature, humidity, medium, etc.) are set, and the corrosion or durability experiment of the reinforced concrete beam component is carried out. Strain gauges, displacement sensors and other data acquisition devices are arranged at the key parts of the reinforced concrete beam component to monitor parameters such as stress, strain, and deflection, and obtain the appearance and development of cracks, so as to obtain but not limited to the corrosion rate distribution of the steel bar along the length direction, the crack width distribution on the surface of the concrete component, and the steel bar corrosion position distribution corresponding to the crack width distribution on the surface of the concrete component, and the steel bar corrosion rate distribution along the length direction, the crack width distribution on the surface of the concrete component and the steel bar corrosion position distribution are stored as sample data in the data augmentation device.

[0032] When performing data augmentation, the data augmentation equipment extracts the spatial distribution of the corrosion rate of the steel bars along the length direction from the sample data, and uses the standard deviation formula and the mean formula to calculate the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate. The standard deviation of the steel bar corrosion rate is also called the characteristic parameter of the corrosion spatial variability. Then, the relationship between the standard deviation of the steel bar corrosion and the average steel bar corrosion rate is determined through the fitting method.

[0033] Based on the above, it should be noted that fitting methods are a set of statistical techniques used to determine the relationship between observed data and one or more theoretical models, and use these models to predict or explain unknown data. Fitting methods are usually used to find the mathematical function or curve that best fits the observed data, and these functions or curves can be used to predict or infer unknown data. Commonly used fitting methods include least squares method, nonlinear least squares method, polynomial fitting method, and local fitting method.

[0034] In one embodiment of the present application, the relationship between the standard deviation of the steel bar corrosion rate and the average steel bar corrosion is a logarithmic function relationship, specifically:

[0035] in, is the standard deviation of steel bar corrosion rate, is the average corrosion rate of steel bars.

[0036] S20. Based on the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of the steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution, and input it into the reinforced concrete beam finite element model to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution; In this example, since the corrosion of the steel bar has great uncertainty along the length direction, it needs to be simulated by a probabilistic method, and the spatial variability of steel bar corrosion (standard deviation of steel bar corrosion rate) is simulated by a logarithmic distribution. The logarithmic distribution combined with the Nataf transformation method can take the corrosion correlation between steel bar units into account in the simulation.

[0037] Specifically, the corrosion correlation between steel bar units can be characterized by the autocorrelation function, which is expressed by the exponential correlation function: :

[0038] in, is the function value used to represent the corrosion correlation between steel bar elements; is a parameter representing the relevant length, which can be obtained based on the corrosion rate distribution of the steel bars along the length direction of the above embodiment. In this embodiment, L 31.4mm; It is the center distance between two adjacent steel bar units along the length of the steel bar.

[0039] It should be noted that the Nataf transformation can be used to transform any random variable into a standard Gaussian distribution. n dimensional correlated random vector The marginal cumulative density function is , the correlation coefficient matrix is . Through Nataf transformation, Can be transformed into independent standard normal variables The process of Nataf transformation is: the relevant standard normal variable It can be obtained by the following formula Conversion:

[0040] in, for Array elements of is the marginal cumulative density function, is the marginal cumulative density function of the inverse of the standard Gaussian distribution. The correlation coefficient matrix of Y is . and The relationship between them is shown as follows:

[0041] in, From the equation

[0042] Given, and The relevant variables are and The mean of and The relevant variables are and The standard deviation of .

[0043] in, Can be Calculation, the relationship between the two can be simplified as follows:

[0044] It can be estimated by polynomial approximation as follows:

[0045] Among them, the parameters to It can be calculated by Monte Carlo method. It can be decomposed by Cholesky Decomposed into a lower triangular matrix and an upper triangular matrix as follows:

[0046] in, is a lower triangular matrix. Therefore, the variable Y can be expressed as The product of the independent standard normal variable U is shown as follows:

[0047] The relationship between the independent standard normal variable U and the original dependent variable X can be established as an equation:

[0048]

[0049]

[0050] ; After generating a steel corrosion rate distribution that obeys a logarithmic distribution, the data augmentation device inputs the steel corrosion rate distribution into a reinforced concrete beam finite element model, and uses the reinforced concrete beam finite element model to perform a finite element simulation of the surface concrete cracking process caused by concrete surface corrosion expansion, thereby obtaining a crack width distribution corresponding to the steel corrosion rate distribution that obeys a logarithmic distribution.

[0051] S30, looping execution step to obtain the spatial distribution of steel corrosion rate along the length direction, determine the standard deviation of steel corrosion rate according to the spatial distribution of steel corrosion rate along the length direction, and determine the logarithmic function relationship between the standard deviation of steel corrosion rate and the average steel corrosion rate by fitting method, and step based on the standard deviation of steel corrosion rate and the average steel corrosion rate, using Nataf transformation method to simulate the spatial distribution of steel corrosion rate along the length direction to generate a steel corrosion rate distribution that obeys logarithmic distribution, and input it into the finite element model of reinforced concrete beam to obtain the crack width distribution corresponding to the steel corrosion rate distribution that obeys logarithmic distribution, until the termination condition is reached, the operation ends and returns to establish the mapping relationship between the spatial distribution of steel corrosion rate and the crack width distribution.

[0052] In this embodiment, steps S10 to S20 are executed in a loop, and the spatial distribution of steel corrosion rate under different corrosion degrees is generated by multiple samplings, and the crack width distribution on the concrete surface generated under each spatial distribution of steel corrosion rate is matched one by one, until the number of the spatial distribution of steel corrosion rate and the number of the crack width distribution reach the target number of samples, indicating that the number of the spatial distribution of steel corrosion rate and the number of the crack width distribution have reached the condition of establishing a mapping relationship, and the operation is terminated and the mapping relationship between the spatial distribution of steel corrosion rate and the crack width distribution is established, and an artificial data sample with the same characteristics as the experimental sample is generated, so as to expand the relationship data between the spatial distribution of steel corrosion rate and the crack width distribution.

[0053] The present application obtains the spatial distribution of the corrosion rate of the steel bar along the length direction, determines the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determines the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar through a fitting method; based on the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar, the spatial distribution of the corrosion rate of the steel bar along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution, and inputs it into the finite element model of the reinforced concrete beam to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution; the above steps are executed cyclically until the termination condition is reached, and the operation ends and returns to establish a mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width distribution. In this way, the present application can generate artificial data samples with the same characteristics as the experimental samples, realize the expansion of the relationship data between the spatial distribution of steel corrosion rate and the crack width distribution, predict the spatial distribution of steel corrosion rate inside the concrete structure by detecting the crack width distribution on the surface of reinforced concrete beam components, and realize the probabilistic prediction of the long-term performance of the concrete structure. It provides a new data basis for establishing performance degradation evaluation of corroded concrete structures based on detection information, and also provides data-level support for the performance evaluation of bridge components affected by corrosion in coastal environments, which has far-reaching significance for the formulation of maintenance strategies for bridges throughout their life cycle under the influence of corrosion.

[0054] In an embodiment of the present application, please refer to Figure 2 , S20 specifically includes S21~S23: S21. Based on the standard deviation of steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution; It should be noted that the above content has already described the generation process of the steel bar corrosion rate distribution that obeys the logarithmic distribution. For details, please refer to the above content, and this embodiment and the following embodiments will not be repeated.

[0055] S22, inputting the steel corrosion rate distribution that obeys logarithmic distribution into the steel corrosion expansion model, and calculating the unit node expansion displacement applied to the steel-concrete interface; S23. The unit node expansion displacement applied to the reinforced concrete interface is input into the reinforced concrete beam finite element model to simulate the non-uniform corrosion around the steel bars and along the steel bar axis, and the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution is obtained.

[0056] It should be noted that the steel bar corrosion expansion model and the reinforced concrete beam finite element model are both models pre-stored in the data augmentation device. Among them, the steel bar corrosion expansion model is used to simulate the corrosion expansion of the concrete surface, and the reinforced concrete beam finite element model is used to simulate the cracking process of the concrete surface caused by corrosion. The synergistic effect of the two models is mainly used to perform finite element simulation of the cracking process of the concrete surface caused by corrosion expansion of the concrete surface.

[0057] In order to reduce the complexity of establishing the steel bar corrosion expansion model and the reinforced concrete beam finite element model, some parameters that have little effect on the mapping relationship between the spatial distribution of steel bar corrosion rate and the crack width distribution can be ignored, assumed, or simulated and replaced by similar methods. For example, since the compressive behavior of concrete has little effect on the calculation results of the crack width caused by corrosion, it can be assumed that the compressive behavior of concrete is an ideal elastic material. The steel bar corrosion expansion model can be regarded as the expansion displacement of the unit node applied to the steel bar concrete interface, that is, the radial displacement applied to the steel bar concrete interface. When establishing the finite element model of the reinforced concrete beam, the steel bar can be simulated as a "hole", and the non-uniform corrosion around the steel bar and along the axis of the steel bar is simulated by applying radial displacement to represent the spatial expansion of steel bar corrosion. For another example, several assumptions can be established in the simulation of the corrosion expansion process, including: first, rust can be regarded as rigid and its deformation can be ignored. Second, the influence of steel bar corrosion of the longitudinal steel bar on the crack width caused by corrosion near the adjacent longitudinal steel bar is not considered, and only the relationship between the spatial distribution of the steel corrosion rate of the longitudinal steel bar and the crack width caused by corrosion is considered. Third, the influence of stirrups on the crack width is ignored in the finite element model. In this way, the complexity of establishing the steel bar corrosion expansion model and the reinforced concrete beam finite element model can be simplified, and the subsequent analysis of the mapping relationship between the spatial distribution of steel bar corrosion rate and the crack width distribution based on the steel bar corrosion expansion model and the reinforced concrete beam finite element model will not be affected.

[0058] In an embodiment of the present application, please refer to Figure 3 The establishment of the reinforced concrete beam finite element model specifically includes steps S201 to S202: S201, inputting the fracture softening curve into the concrete model to simulate the tensile behavior of the concrete; S202. Based on the tensile behavior of concrete, a finite element model of reinforced concrete beam is established.

[0059] In this embodiment, the concrete structure can be divided into multiple three-dimensional eight-node solid units. Each unit has eight vertices, which can more accurately capture complex geometric shapes and stress distributions. Among them, concrete exhibits nonlinear behavior during the stretching process, especially when it reaches the limit state, it will undergo brittle fracture. This embodiment uses a linear softening curve to describe the process from the beginning of microcracks to complete fracture of concrete, that is, the phenomenon that the stress-strain relationship of concrete gradually softens (that is, the stress decreases while the strain continues to increase). Among them, the introduction of fracture energy ( G f ) can measure the ability of a material to absorb energy before fracture to quantify this softening behavior. In addition, the crack bandwidth is introduced h Parameter, which can be used to characterize the width of cracks in concrete. Definition of crack bandwidth h It is the cube root of the grid unit volume, which aims to ensure that the crack width matches the discretization degree of the model and improve the rationality of the simulation.

[0060] It should be noted that the CEB-FIP (Joint Committee of the International Concrete Association and the European Concrete Institute) model code is one of the international standards for concrete structure design and analysis. The 2010 version of the model code provides a method for calculating the fracture energy of concrete. This embodiment can accurately evaluate the energy absorption capacity of concrete during the fracture process through the 2010 version of the model. It can be assumed that concrete is an ideal elastic material when under pressure, ignoring the plastic deformation or damage that may occur in actual concrete under high pressure, so as to simplify the data analysis of the model and focus on the tensile and corrosion problems of concrete.

[0061] In combination with this embodiment, the volume expansion caused by steel bar corrosion can be simulated by applying displacement to the steel bar concrete interface. In this way, the non-uniform expansion behavior caused by corrosion is taken into account, and the spatial variation of this corrosion phenomenon along the axis of the steel bar is simulated. In the process of establishing the finite element model, the steel bar is not directly modeled as a solid, but by creating a "hole" at its location (i.e., deducting the space occupied by the steel bar), and by applying radial displacement to the concrete unit around the hole to simulate the expansion effect of the steel bar, which simplifies the modeling of the interaction between the steel bar and the concrete, and can effectively capture the complex stress state caused by the corrosion of the steel bar. In this way, this embodiment proposes a numerical simulation method for establishing a finite element model of a reinforced concrete beam, which aims to comprehensively analyze the mechanical response of the reinforced concrete structure under the action of steel bar corrosion, including the formation and expansion of cracks and the interaction between concrete and steel bars, and provides an important data basis for determining the mapping relationship between the spatial distribution of steel bar corrosion rate and the crack width distribution.

[0062] In this embodiment, the data augmentation device inputs the generated steel bar corrosion rate distribution that obeys the logarithmic distribution into the steel bar corrosion expansion model. The steel bar corrosion expansion model can be used to obtain the unit node expansion displacement applied to the steel bar concrete interface, and the unit node expansion displacement of the steel bar concrete interface is input into the reinforced concrete beam finite element model. The reinforced concrete beam finite element model is used to simulate the non-uniform corrosion around the steel bar and along the axis of the steel bar, and the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution is obtained.

[0063] In one embodiment of the present application, since the non-uniform corrosion around the steel bars will produce non-uniform radial expansion pressure on the surrounding concrete, the corrosion distribution curve of the elliptical expression can be used to describe the corrosion expansion behavior of each steel bar section. As a continuous function, the elliptical expression can accurately describe the non-uniformity of the corrosion expansion of the steel bar section, and reflect the distribution characteristics of the corrosion on the steel bar section, such as the coexistence of local severe corrosion and slight corrosion, and simulate the difference in the impact of local corrosion on the structure.

[0064] In one embodiment of the present application, the expression of the corrosion distribution curve is:

[0065] in, is the function value corresponding to the corrosion distribution curve, is the original radius of the reinforcement, is the maximum corrosion layer thickness closest to the concrete surface, is the thickness of the corrosion layer on the side away from the concrete surface.

[0066] It should be noted that the shape of the ellipse is determined by Decide, here assume .

[0067] In one embodiment of the present application, after the steel bar begins to corrode, the rust can expand freely. Once the gaps in the porous area between the steel bar and the surrounding concrete are completely filled with rust, the expansion of the rust will exert pressure on the surrounding concrete, thereby generating tensile stress and causing concrete cracking. Not all rust will cause increased stress and initial cracking of the overlying concrete. Some rust may penetrate into the porous area. The present application assumes that the porous area is uniform and its thickness is δ It can be 12.5µm. In the free expansion stage of corrosion products, the unit length l The volume of corrosion It can be expressed as:

[0068] in, is the volume of corrosion products that penetrate into the porous area of ​​the steel / concrete interface (i.e. 2π Rδl ), is the volume of the corroded steel bar during the free expansion stage of the corrosion products. Once the voids in the porous area are completely filled with rust, tensile stress will be generated in the surrounding concrete. l The volume of rust for:

[0069]

[0070] in, is the unit length l The expansion volume, It is the volume of steel bars corroded after the gaps are filled with rust. is the maximum corrosion layer thickness closest to the concrete surface, is the thickness of the corrosion layer on the side away from the concrete surface, is the original radius of the reinforcing bar.

[0071] Total amount of rust = + It comes from three interrelated processes, each of which is directly or indirectly related to steel corrosion. Specifically, first, the total amount of corrosion includes the corrosion associated with the consumed steel. Among them, the corrosion occurs directly on the surface of the steel, and the volume of the corrosion products (mainly iron oxide) is larger than the original volume of the steel, causing the physical volume of the steel itself to expand. Second, the total amount of corrosion includes the corrosion associated with the penetration into the porous area of ​​the steel / concrete interface. Among them, the iron oxide produced during the corrosion process can penetrate from the surface of the steel into the tiny gaps or pores between the steel and the concrete. This penetration will further aggravate the bond failure between the steel and the concrete, and may also form a rust channel, promote the intrusion of more water and corrosive media, and accelerate the corrosion process. Third, the total amount of corrosion includes the corrosion associated with the expansion pressure on the surrounding concrete. Among them, the volume expansion of the corrosion products will exert pressure on the surrounding concrete, causing the concrete to crack or peel off, reducing the integrity and durability of the structure. Since all rust is produced by steel corrosion, the relationship is:

[0072] in is the total amount of rust, is the volume of all corroded steel bars, is the volume expansion ratio of the corrosion products and can be assumed to be 2.0.

[0073] Some rust will flow out of the structure through the cracks caused by corrosion and penetrate into the surrounding concrete. The amount of rust that penetrates into the surrounding concrete depends on different experimental conditions. For example, when conducting experiments by accelerating corrosion through electricity, the amount of rust that penetrates into the surrounding concrete depends on the current density level during the experiment. Lower current densities easily provide opportunities for rust to fill the pores of the surrounding concrete. On the contrary, when the current density is high, some rust is not easy to induce cracking in the surrounding concrete. Therefore, the corrosion rate in the expansion simulation The corrosion rate is not consistent with the experimental η Similarly, in order to simplify this problem, the calibration factor can be introduced , in order to consider the impact of impulse current density and average corrosion rate of steel bars on the pressure causing corrosion cracks. and The relationship between them can be:

[0074]

[0075] in, is the corrosion rate in the expansion simulation, η is the corrosion rate measured experimentally, is the calibration factor, I corr is the impulse current density, is the average corrosion rate of steel bars, can be 0.903, It can be 10.3, It can be 24.2, It can be −0.663.

[0076] In one embodiment of the present application, is the maximum corrosion layer thickness closest to the concrete surface, which can be The expansion displacement of the unit node applied to the reinforced concrete interface is input into the reinforced concrete beam finite element model. Depend on Determine, the relationship is:

[0077] Where η is the corrosion rate measured experimentally, is the calibration factor, R is the original radius of the steel bar; is the thickness of the porous region, which may be specifically 12.5 µm.

[0078] In this way, this embodiment can calculate the total amount of corrosion based on the finite element model simulation of the reinforced concrete beam, and can take into account the corrosion effect of corrosion on the steel bars and concrete, as well as the impact of the impact current density and the average corrosion rate of the steel bars on the pressure causing corrosion cracks under different experimental conditions. The simulated corrosion rate is corrected using the calibration coefficient to improve the accuracy of the finite element simulation, so as to better quantify the mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width distribution.

[0079] In an embodiment of the present application, please refer to Figure 4 The establishment of the spatial distribution of the corrosion rate of the steel bar along the length direction specifically includes steps S101 to S102: S101, obtaining design parameters of a reinforced concrete beam component, wherein the design parameters include the length, cross-sectional dimensions, concrete strength, concrete cover thickness, and steel bar diameter of the reinforced concrete beam component; In this embodiment, regarding the length and cross-sectional dimensions, for beams with longer lengths, due to the large mid-span bending moment, initial cracks are easily formed in the tension area. Once the steel bar begins to corrode, the crack width may rapidly expand due to the reduction in the effective cross-sectional area of ​​the steel bar; for beams with larger cross-sectional dimensions, due to the large cross-sectional inertia moment, cracks are relatively less likely to occur, but if corrosion occurs, internal corrosion is difficult to detect and repair is more difficult. Regarding concrete strength, high-strength concrete can provide better crack resistance and slow down the formation of initial cracks, but the steel bars in high-strength concrete are more susceptible to alkali-aggregate reaction or chloride ion erosion, which will accelerate steel bar corrosion. The reduction in steel bar cross-sectional area caused by corrosion will increase stress concentration, thereby exacerbating the expansion of existing cracks. Regarding the thickness of the protective layer, the thickness of the protective layer directly affects the degree of contact between the steel bar and the external corrosive medium. For example, a thinner protective layer makes the steel bar more susceptible to corrosion by the corrosive medium, accelerates the corrosion process, and may form severe corrosion pits in local areas, resulting in a reduction in the cross-sectional area of ​​the steel bar in the area, prompting cracks to form and rapidly expand in these areas; on the contrary, a thicker protective layer can effectively delay the corrosion process and reduce the occurrence and expansion of cracks. Regarding the diameter of steel bars, although large-diameter steel bars can improve the bearing capacity, the corrosion on their surface is more likely to form obvious corrosion pits, which will produce greater local pressure on the surrounding concrete and promote the formation and expansion of cracks; on the contrary, small-diameter steel bars cause more uniform cross-sectional loss, and the impact on the crack width distribution may be relatively small, but it will increase the density of the steel bars and affect the pouring quality of the concrete.

[0080] Therefore, the length, cross-sectional dimensions, concrete strength, concrete cover thickness, and steel bar diameter of reinforced concrete beam components are crucial in data augmentation to establish the mapping relationship between the spatial distribution of steel bar corrosion rate and crack width distribution, and should all be taken into account for experimental analysis.

[0081] S102. Based on the design parameters of the reinforced concrete beam, an accelerated corrosion test is performed on the reinforced concrete beam component to obtain the spatial distribution of the corrosion rate of the steel bars along the length direction.

[0082] In this embodiment, the energized accelerated corrosion experiment can accelerate the corrosion rate of the steel bars by applying direct current to the steel bars of the reinforced concrete beam components to simulate the corrosion process in the natural environment. Specifically, the positive electrode of the DC power supply is connected to the steel bars, and the negative electrode of the DC power supply is connected to the electrolyte (such as simulated seawater or sodium sulfate solution) to form an electrolytic cell, which can accelerate the dissolution process of the steel bars. Electrochemical techniques such as electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LPR) can be used to regularly measure the corrosion current density of the steel bars, and combined with the weight loss method, the average corrosion rate and distribution of the steel bars can be calculated. The unevenness of the corrosion rate will cause the corrosion rate of the steel bars in some areas to be much higher than that in other areas. The formation and expansion process of cracks on the surface and inside of reinforced concrete can be monitored by means such as X-ray technology, digital image processing technology or ultrasonic detection. Special attention can be paid to the crack dynamics near the steel bar corrosion area, because the concrete in these areas is subjected to additional stress due to the expansion of the steel bars and is prone to cracking. As the corrosion progresses, the crack width around the steel bar corrosion area shows a non-uniform distribution, which echoes the distribution of the steel bar corrosion rate. The crack width is larger near the corrosion hotspot area (area with high corrosion rate).

[0083] In summary, by conducting an electric-accelerated corrosion experiment on reinforced concrete beam components, the present application can obtain, but is not limited to, the spatial distribution of the corrosion rate of the steel bars along the length direction, and can also obtain the crack width distribution on the surface of the concrete component, as well as the steel bar corrosion position distribution corresponding to the crack width distribution on the surface of the concrete component. This can provide experimental data support for subsequent statistical analysis and finite element analysis simulations to achieve the expansion of the mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width.

[0084] For example, please refer to Figures 5 to 12 , Figure 6 and Figure 7 The reinforced concrete beam element shown includes one longitudinal reinforcement and the cross section of the beam is 80×140 mm 2 , with a length of 1460 mm. Through the electric accelerated corrosion experiment, the spatial distribution of steel corrosion rate and the crack width distribution on the surface of reinforced concrete beam components under different corrosion degrees can be obtained. Specifically, different experimental parameters can be used for the experiment. For example, the steel bar diameter can be 13 mm or 19 mm, the protective layer thickness can be 10 mm or 20 mm, and the current density can be 10μA / cm 2 , 50μA / cm 2 , 100μA / cm 2, 200μA / cm2, 500μA / cm 2 Or 1000μA / cm 2 . Furthermore, according to the spatial distribution of the corrosion rate of the steel bars along the length direction under experimental conditions, the standard deviation formula and the average difference formula can be used to calculate the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate under experimental conditions, and the relationship between the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate can be determined as a logarithmic function relationship through the fitting method. Based on the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate, the Nataf transformation method can be introduced to simulate the spatial distribution of the steel bar corrosion rate along the length direction to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution. For details, see Fig. 9 , Fig. 9 The steel bar corrosion rate distribution along the length direction of the steel bar when the average steel bar corrosion rate is 5%, 10% and 15% is shown respectively, and input into Fig.10 and Fig.11 The steel bar corrosion expansion model shown in the figure calculates the expansion displacement of the unit node applied to the steel bar concrete interface to simulate the corrosion expansion of the steel bar. The expansion displacement of the unit node applied to the steel bar concrete interface can be input into Figure 8 The finite element model of reinforced concrete beams is used to obtain the crack width distribution corresponding to the logarithmic distribution of steel corrosion rate. Finally, multiple samplings are performed to generate steel corrosion rate distributions under different corrosion degrees, and the concrete surface crack width distribution results generated under each steel corrosion rate distribution are matched one by one. The data augmentation of the mapping relationship between the spatial distribution of steel corrosion rate and the crack width distribution can be obtained. Fig.12 As shown. Among them, C is the thickness of the protective layer, d The sequence-to-sequence mapping database obtained in this embodiment can be used as a training data set for training a neural network model (sequence-to-sequence data mode), and the spatial distribution of steel corrosion inside a concrete structure can be predicted by surface detection of crack width distribution, thereby realizing a probabilistic prediction of the long-term performance of the concrete structure.

[0085] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data augmentation method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0086] This application provides a data augmentation device, please refer to Fig.13 The data augmentation device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data augmentation method in the above-mentioned embodiment 1.

[0087] Reference below Fig.13 , which shows a schematic diagram of the structure of a data augmentation device suitable for implementing the embodiment of the present application. The data augmentation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0088] It should be noted that Fig.13 The data augmentation device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] like Fig.13 As shown, the data augmentation device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the data augmentation device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the data augmentation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a data augmentation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0090] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a data augmentation program carried on a computer-readable medium, and the data augmentation program contains program code for executing the method shown in the flowchart. In such an embodiment, the data augmentation program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the data augmentation program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0091] The data augmentation device provided by the present application adopts the data augmentation method in the above embodiment, which can solve the problem that the data of the sequence-sequence mapping relationship is very limited under the existing laboratory conditions. Compared with the prior art, the beneficial effects of the data augmentation device provided by the present application are the same as the beneficial effects of the data augmentation method provided by the above embodiment, and the other technical features in the data augmentation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0092] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0094] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, data augmentation program) stored thereon, wherein the computer-readable program instructions are used to execute the data augmentation method in the above-mentioned embodiment.

[0095] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0096] The computer-readable storage medium may be included in the data augmentation device; or may exist independently without being assembled into the data augmentation device.

[0097] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the data augmentation device, the data augmentation device performs data augmentation based on the mapping relationship between the steel bar corrosion rate distribution and the crack width distribution using a random finite element method.

[0098] The data augmentation program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0101] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., data augmentation program) for executing the above-mentioned data augmentation method, and can solve the problem that the data for obtaining such sequence-sequence mapping relationship under existing laboratory conditions is very limited. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the data augmentation method provided in the above-mentioned embodiment, and will not be described in detail here.

[0102] The present application also provides a computer program product, including a data augmentation program, which implements the steps of the above-mentioned data augmentation method when executed.

[0103] The computer program product provided in this application can solve the problem that the data of such sequence-sequence mapping relationship is very limited under existing laboratory conditions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the data augmentation method provided in the above embodiment, which will not be described in detail here.

[0104] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data augmentation method, characterized in that: include: Obtain the spatial distribution of the corrosion rate of the steel bar along the length direction, determine the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determine the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar by a fitting method; Based on the standard deviation of steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution. The distribution is then input into the reinforced concrete beam finite element model to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution. The steps of looping and executing are to obtain the spatial distribution of the corrosion rate of the steel bar along the length direction, determine the standard deviation of the steel bar corrosion rate according to the spatial distribution of the corrosion rate of the steel bar along the length direction, and determine the logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar by fitting method, and the steps of simulating the spatial distribution of the corrosion rate of the steel bar along the length direction by using the Nataf transformation method based on the standard deviation of the steel bar corrosion rate and the average corrosion rate of the steel bar to generate a steel bar corrosion rate distribution that obeys the logarithmic distribution, and input it into the finite element model of the reinforced concrete beam to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution, until the termination condition is reached, the operation ends and returns to establish the mapping relationship between the spatial distribution of the steel bar corrosion rate and the crack width distribution.

2. The data augmentation method according to claim 1, characterized in that: The logarithmic function relationship between the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate is: in, is the standard deviation of steel bar corrosion rate, is the average corrosion rate of steel bars.

3. The data augmentation method according to claim 1, wherein: The steps of simulating the spatial distribution of the corrosion rate of the steel bars along the length direction by using the Nataf transformation method based on the standard deviation of the steel bar corrosion rate and the average steel bar corrosion rate to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution, and inputting the generated steel bar corrosion rate distribution into the reinforced concrete beam finite element model to obtain the crack width distribution corresponding to the steel bar corrosion rate distribution that obeys the logarithmic distribution specifically include: Based on the standard deviation of steel bar corrosion rate and the average steel bar corrosion rate, the spatial distribution of steel bar corrosion rate along the length direction is simulated using the Nataf transformation method to generate a steel bar corrosion rate distribution that obeys a logarithmic distribution. The logarithmically distributed steel corrosion rate distribution is input into the steel corrosion expansion model to calculate the expansion displacement of the unit node applied to the steel-concrete interface. The expansion displacement of the unit node applied to the reinforced concrete interface is input into the finite element model of reinforced concrete beam to simulate the non-uniform corrosion around the steel bars and along the steel bar axis, and the crack width distribution corresponding to the steel bar corrosion rate distribution obeying the logarithmic distribution is obtained.

4. The data augmentation method according to claim 3, characterized in that: The non-uniform corrosion around the steel bars will produce non-uniform radial expansion pressure on the surrounding concrete. A corrosion distribution curve is used to express the corrosion expansion behavior of each steel bar section. The expression of the corrosion distribution curve is: in, is the function value corresponding to the corrosion distribution curve, is the original radius of the reinforcement, is the maximum corrosion layer thickness closest to the concrete surface, is the thickness of the corrosion layer on the side away from the concrete surface.

5. The data augmentation method according to claim 3, characterized in that: The relationship between the expansion displacement of the unit node applied to the reinforced concrete interface is: in, is the expansion displacement of the unit node applied to the reinforced concrete interface, and is also the maximum corrosion layer thickness closest to the concrete surface; η is the corrosion rate measured experimentally, is the calibration coefficient, R is the original radius of the steel bar, is the thickness of the porous zone formed at the steel / concrete interface.

6. The data augmentation method according to claim 3, characterized in that: The steps of establishing the finite element model of the reinforced concrete beam specifically include: The fracture softening curve is input into the concrete model to simulate the tensile behavior of concrete; Based on the tensile behavior of concrete, a finite element model of reinforced concrete beam is established.

7. The data augmentation method according to claim 1, wherein: The steps of obtaining the spatial distribution of the corrosion rate of the steel bar along the length direction specifically include: Acquire design parameters of the reinforced concrete beam component, wherein the design parameters include the length, cross-sectional dimensions, concrete strength, concrete cover thickness, and steel bar diameter of the reinforced concrete beam component; Based on the design parameters of reinforced concrete beams, an accelerated corrosion experiment was carried out on reinforced concrete beam components to obtain the spatial distribution of corrosion rate of the steel bars along the length direction.

8. A data augmentation device, characterized in that: The data augmentation device comprises a memory, a processor, and a data augmentation program stored in the memory and executable on the processor, wherein the data augmentation program is configured to implement the steps of the data augmentation method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a data augmentation program is stored on the storage medium. When the data augmentation program is executed, the steps of the data augmentation method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a data augmentation program, and when the data augmentation program is executed, the steps of the data augmentation method according to any one of claims 1 to 7 are implemented.

Images