Method, device, medium and equipment for detecting steel bar corrosion information in reinforced concrete

Abstract

The invention discloses a method, a device, a medium and equipment for detecting steel bar corrosion information in reinforced concrete, and relates to the technical field of concrete steel bar corrosion detection. The method comprises the following steps: acquiring semi-potential distribution of reinforced concrete; establishing a corrosion model of reinforcement corrosion potential distribution in the reinforced concrete according to the function relationship among the saturation degree, the oxygen diffusion coefficient and the concrete resistance; establishing a particle swarm related to the steel bar corrosion information, and based on the semi-potential distribution and corrosion model, determining the adaptive value of each particle in the particle swarm related to the steel bar corrosion information by adopting a particle swarm optimization algorithm; and based on the adaptive value of each particle in the particle swarm, performing inversion on the information of reinforcement corrosion in the reinforced concrete to obtain the position of reinforcement corrosion and the size of the corrosion area in the reinforced concrete. By means of the scheme, accurate quantitative information of steel bar corrosion can be obtained, and the limitation that only the steel bar corrosion probability can be provided through an existing half-cell potential method is broken through.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete reinforcement corrosion detection, and in particular to a method, device, medium and equipment for detecting reinforcement corrosion information in reinforced concrete. Background Art

[0002] Reinforced concrete structure is the most widely used structural form in the field of civil engineering, accounting for about 60% to 70% of the total area of ​​industrial and civil buildings, and even higher in other infrastructure (such as road and bridge engineering, water conservancy engineering, and marine engineering). It is estimated that corrosion losses account for about 5% of the national economic output each year, of which about 40% is related to the corrosion of reinforced concrete. Therefore, the detection of steel corrosion in concrete structures is of great significance to the safe operation and maintenance of infrastructure.

[0003] The existing method for detecting steel corrosion in concrete structures is mainly the half-cell potential (HCP) method. As a non-destructive testing method, it has the advantages of simple operation, wide coverage, low detection cost, and simple data analysis. It is widely used in steel corrosion detection of concrete structures in actual engineering.

[0004] However, the current half-potential method can only give the probability of steel bar corrosion in concrete structures, but cannot provide accurate corrosion quantification information (the specific location and size of steel bar corrosion), which seriously restricts the application value of the HCP method in actual engineering. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, medium and equipment for detecting steel corrosion information in reinforced concrete in response to the above technical problems.

[0006] The present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for detecting steel bar corrosion information in reinforced concrete, the method comprising: Obtain the half-potential distribution of reinforced concrete; A corrosion model of the steel bar corrosion potential distribution in the reinforced concrete is established according to the functional relationship between the saturation degree, the oxygen diffusion coefficient and the concrete resistance; Establishing a particle group related to the information of the steel bar corrosion, wherein the parameters of the particles in the particle group include the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area; Based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in a particle swarm related to the information of the steel bar corrosion; based on the fitness value of each particle in the particle swarm, the information of the steel bar corrosion in the reinforced concrete is inverted to obtain the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area.

[0007] Further, based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in the particle swarm related to the information of the steel bar corrosion, specifically including: Substituting the parameters of each particle in the particle group into the corrosion model to obtain the predicted potential value corresponding to each particle; Obtaining the actual potential value of each particle in the particle group at the position where the particle is located from the half-potential distribution of the reinforced concrete, and constructing an objective function according to the predicted potential value and the actual potential value; The actual potential value at the position of each particle and the predicted potential value corresponding to each particle are respectively input into the objective function to obtain the fitness value of each particle.

[0008] Furthermore, the information of steel bar corrosion in the reinforced concrete is inverted based on the fitness value of each particle in the particle group, specifically including: Compare the fitness value of the i-th particle in the particle group with the individual optimal position and the global optimal position of the i-th particle respectively to obtain a first comparison result and a second comparison result, where i is a positive integer; When the first comparison result is that the fitness value of the i-th particle is greater than the individual optimal position of the i-th particle, the individual optimal position of the i-th particle is replaced by the fitness value of the i-th particle; When the first comparison result is that the fitness value of the i-th particle is not greater than the individual optimal position of the i-th particle, the individual optimal position of the i-th particle is kept unchanged; When the second comparison result is that the fitness value of the i-th particle is greater than the global optimal position of the i-th particle, the global optimal position of the i-th particle is replaced by the fitness value of the i-th particle; When the second comparison result is that the fitness value of the i-th particle is not greater than the global optimal position of the i-th particle, the global optimal position of the i-th particle is kept unchanged.

[0009] Furthermore, the method further comprises: When the fitness value of the i-th particle is greater than a preset threshold, taking the individual optimal position and the global optimal position as targets, updating the speed and position of the i-th particle until the fitness value of the i-th particle is no greater than the preset threshold; When the fitness value of the i-th particle is not greater than the preset threshold and satisfies a priori conditions, the i-th particle is output, and the priori conditions are obtained based on the half-potential distribution of the reinforced concrete.

[0010] Furthermore, the establishing of a particle group related to the information of the steel bar corrosion specifically includes: Converting the area of ​​the steel bar corrosion into one dimension to obtain a one-dimensional model of the steel bar corrosion, wherein the coordinates of the one-dimensional model correspond to the area of ​​the steel bar corrosion in a one-to-one manner; The coordinates of different positions in the one-dimensional model are determined as the size of the corrosion area of ​​each particle in the particle group, and the different positions of the steel bar corrosion are determined as the positions of each particle in the particle group.

[0011] Furthermore, the relationship between the inertia weight parameter and the number of iterations of the particle swarm optimization algorithm is expressed as follows: ; in, w j is the inertia weight parameter, j is the number of iterations, j max is the maximum number of iterations, w min is the minimum inertia weight, w max is the maximum inertia weight.

[0012] Furthermore, the objective function is expressed as: ; in, X represents particles, N The number representing the actual potential value, φ l A vector representing the predicted potential values, is the vector of the actual potential value, is the vector maximum value of the actual potential value.

[0013] In a second aspect, the present invention provides a device for detecting steel bar corrosion information in reinforced concrete, comprising: An acquisition module, used for acquiring the half-potential distribution of reinforced concrete; A first establishing module is used to establish a corrosion model of steel corrosion potential distribution in the reinforced concrete according to the functional relationship between water saturation, oxygen diffusion coefficient and concrete resistance; A second establishing module is used to establish a particle group related to the information of the steel bar corrosion, wherein the parameters of the particles in the particle group include the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area; The inversion module is used to determine the fitness value of each particle in the particle swarm related to the information of the steel corrosion based on the half-potential distribution and the corrosion model by using a particle swarm optimization algorithm; based on the fitness value of each particle in the particle swarm, the information of the steel corrosion in the reinforced concrete is inverted to obtain the position of the steel corrosion in the reinforced concrete and the size of the corrosion area.

[0014] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting steel bar corrosion information in reinforced concrete is implemented.

[0015] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for detecting steel bar corrosion information in reinforced concrete when executing the program.

[0016] The at least one technical solution adopted by the present invention can achieve the following beneficial effects: the present invention establishes a corrosion model of the steel corrosion potential distribution in reinforced concrete based on the functional relationship between saturation, oxygen diffusion coefficient and concrete resistance, and establishes a particle group related to the information of steel corrosion, and uses the location of steel corrosion in reinforced concrete and the size of the corrosion area as parameters of each particle in the particle group. Based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in the particle group; based on the fitness value of each particle in the particle group, the information of steel corrosion in reinforced concrete is inverted to obtain the location of steel corrosion in reinforced concrete and the size of the corrosion area. Accurate inversion of quantitative information of steel corrosion is achieved, breaking through the limitation that the current half-cell potential method can only provide the probability of steel corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a process flow of a method for detecting steel bar corrosion information in reinforced concrete provided by the present invention; Figure 2 A schematic diagram of the structure of reinforced concrete provided by the present invention; Figure 3 The measured surface potential distribution diagram of steel bars in concrete provided by the present invention; Figure 4 A two-dimensional geometric model of steel bar corrosion in concrete provided by the present invention; Figure 5 A schematic diagram of the polarization curves of the anode and cathode of steel bar corrosion in concrete provided by the present invention; Figure 6 A schematic diagram of the functional relationship between the concrete saturation, oxygen diffusion coefficient and resistivity provided by the present invention; Figure 7 The first inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Figure 8 The second inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Fig. 9 The third inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Fig.10 The fourth inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Fig.11 The fifth inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Fig.12 The sixth inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention; Fig.13 A schematic diagram of a device for detecting steel bar corrosion information in reinforced concrete provided by the present invention; Fig.14 A schematic diagram of a computer device for realizing a method for detecting steel bar corrosion information in reinforced concrete provided by the present invention; Figure numerals: 1. House, 2. Cross section of reinforced concrete column, 3. Longitudinal section of reinforced concrete column. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] The server mentioned in the present invention can be a server set on a business platform, or a device such as a desktop computer, a laptop computer, etc. that can execute the solution of the present invention. For the convenience of description, the following description is only based on the server as the execution subject. The following is a detailed description of the technical solutions provided by various embodiments of the present invention in conjunction with the accompanying drawings.

[0020] Figure 1The present invention is a schematic flow chart of a method for detecting steel bar corrosion information in reinforced concrete, which specifically includes the following steps: S10: Obtain the half-potential distribution of reinforced concrete.

[0021] In this embodiment, the half-potential method is used to obtain the half-potential distribution of reinforced concrete, which provides a priori constraints for the inverse calculation of the location of steel corrosion and the size of the corrosion area in the reinforced concrete.

[0022] S20: Based on the functional relationship between saturation, oxygen diffusion coefficient and concrete resistance, a corrosion model for the steel corrosion potential distribution in reinforced concrete is established.

[0023] In this embodiment, the saturation refers to the ratio of the volume occupied by the pore space in the concrete to the total volume of the concrete. The higher the saturation, the greater the porosity of the concrete, and the lower the strength.

[0024] Optionally, a partial differential (Laplace) equation is used to establish a corrosion model for the corrosion potential distribution of steel bars in reinforced concrete, which is solved using the finite element method, namely: (1) in, φ represents the electric potential, 2 represents the square of the gradient, that is, the gradient of the potential is 0.

[0025] In this embodiment, the surface of the steel bars in the reinforced concrete includes a cathode region and an anode region, and the relationship between the current density and the potential in the cathode region and the anode region needs to consider the influence of the oxygen concentration, that is: (2) (3) in, Indicates the oxygen concentration on the steel bar surface. represents the initial oxygen concentration, then / The influence of the change of oxygen concentration on the steel bar surface; c , a represent the cathode and anode reaction potentials, respectively, i c , i a represent the cathode and anode reaction current densities respectively. f ( i c ) is a function of the cathode reaction current density, f ( i a) is a function of the anode reaction current density and needs to be determined based on experimental data. For example, Figure 5 middle φ a = 0.6-10 i a is determined by experimental data about i a The function of Figure 5 middle φ c = 0.27-10 i c is determined by experimental data about i c function.

[0026] In this embodiment, the diffusion of oxygen in the pore gas phase of reinforced concrete is described by Fick's second law, namely: (4) in, D O2 represents the oxygen diffusion coefficient.

[0027] In this embodiment, the relationship expressions between the saturation degree, the oxygen diffusion coefficient and the concrete resistivity are: (5) (6) in, ρ 0 represents the resistivity of concrete when it is fully saturated with water; f(T) The function representing the effect of temperature on resistivity; g (θ) The function representing the effect of water saturation on resistivity; θ Indicates water saturation; D O2 represents the oxygen diffusion coefficient; It indicates the oxygen diffusion coefficient of concrete in dry state; h(θ) The function representing the effect of water saturation on oxygen diffusion coefficient.

[0028] S30: establishing a particle group related to the information of steel bar corrosion, wherein the parameters of the particles in the particle group include the position of steel bar corrosion in the reinforced concrete and the size of the corrosion area.

[0029] In this embodiment, the information of steel bar corrosion includes the corrosion position and the corrosion area. The position and the size of the corrosion area of ​​the steel bar corrosion in the reinforced concrete are used as the parameters of each particle to obtain a particle group related to the information of steel bar corrosion.

[0030] S40: Based on the half-potential distribution and corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in the particle swarm related to the information of steel corrosion; based on the fitness value of each particle in the particle swarm, the information of steel corrosion in reinforced concrete is inverted to obtain the location of steel corrosion in reinforced concrete and the size of the corrosion area.

[0031] In this embodiment, the parameters of each particle in the particle swarm are used as independent variables, the particle swarm optimization algorithm is used to iterate the particle swarm, and all particles in the iterated particle swarm are output to obtain the positions and sizes of multiple corrosion areas of steel bars in reinforced concrete. The iterative formula of the particle swarm optimization algorithm is: (7) (8) in, X j Indicates j The particle position of the iteration, V j Indicates j Iteration particle velocity, w j Indicates j The inertia weight parameter of the iteration, c 1 and c 2 represents the learning factor, r 1 、r 2 represents a random number [0,1], pbest It represents the position where particle i has reached the best fitness value in the iteration process. gbest It represents the position of the best fitness value ever achieved by all particles in the particle swarm during the iteration process.

[0032] According to the size of the fitness value of each particle, the particles that meet the preset fitness value threshold are output, and the parameters contained in each particle are inverted into the location and size of the corrosion area of ​​the steel bar corrosion in the reinforced concrete.

[0033] based on Figure 1The method for detecting steel corrosion information in reinforced concrete shown in the figure establishes a corrosion model of the steel corrosion potential distribution in reinforced concrete based on the functional relationship between saturation, oxygen diffusion coefficient and concrete resistance, and establishes a particle group related to the information of steel corrosion, and uses the position of steel corrosion in reinforced concrete and the size of the corrosion area as the parameters of each particle in the particle group. Based on the half-potential distribution and the corrosion model, the particle swarm optimization algorithm is used to determine the fitness value of each particle in the particle group; based on the fitness value of each particle in the particle group, the information of steel corrosion in reinforced concrete is inverted to obtain the position of steel corrosion in reinforced concrete and the size of the corrosion area. Accurate inversion of quantitative information of steel corrosion is achieved, breaking through the limitation that the current half-cell potential method can only provide the probability of steel corrosion.

[0034] When the method for detecting steel bar corrosion information in reinforced concrete provided by the present invention is applied, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0035] In addition, in one or more embodiments of the present invention, based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in a particle swarm related to the information of steel bar corrosion, specifically including: The parameters of each particle in the particle group are substituted into the corrosion model to obtain the predicted potential value corresponding to each particle.

[0036] In this embodiment, the corrosion model is regarded as a function, and the parameters of each particle in the particle group are taken as independent variables. Each particle is substituted into the corrosion model for numerical calculation, and the potential calculation result corresponding to each particle can be obtained, that is, the predicted potential value corresponding to each particle.

[0037] The actual potential value of each particle in the particle group is obtained from the half-potential distribution of reinforced concrete, and the objective function is constructed according to the predicted potential value and the actual potential value.

[0038] In this embodiment, the location of each particle refers to the different locations of the steel bar corrosion area in the reinforced concrete. After obtaining the actual potential value of each particle from the half-potential distribution of the reinforced concrete, the objective function is constructed according to the absolute value of the difference between the predicted potential value and the actual potential value of each particle.

[0039] Collect the actual potential value of each particle location , and extract the predicted potential value corresponding to each particle according to the potential coordinates of each actual potential value l ,in l Represents the number of particles.

[0040] In this embodiment, the potential coordinate refers to a reference coordinate that can reflect the position of a particle, and different potential coordinates represent different positions.

[0041] The actual potential value at the position of each particle in the particle swarm and the predicted potential value corresponding to each particle are respectively input into the objective function to obtain the fitness value of each particle in the particle swarm.

[0042] In this embodiment, the expression of the objective function is: (9) in, X represents particles, N The number representing the actual potential value, φ l A vector representing the predicted potential values, is the vector of the actual potential value, is the vector maximum value of the actual potential value. The fitness value refers to the result obtained by substituting the actual potential value and the predicted potential value of the corrosion area of ​​the particle into the objective function for calculation. The fitness value reflects the quality of the calculation result.

[0043] The scheme shown in this embodiment establishes a particle group related to the information of steel bar corrosion, and then substitutes each particle in the particle group into the corrosion model to obtain the predicted potential value corresponding to each particle, and collects the actual potential value of the position of each particle in the particle group. Finally, the objective function is constructed according to the predicted potential value corresponding to each particle in the particle group and the actual potential value of the position of each particle. The predicted potential value of each position obtained by considering the influence of saturation on the oxygen diffusion coefficient and concrete resistance of the reinforced concrete structure can be combined with the actual potential value of each position in the steel bar corrosion area, thereby reducing the error generated in the process of measuring the actual potential value, improving the accuracy of evaluating the adaptability of each particle in the particle group, and thus improving the accuracy of detecting the position and area size of the steel bar corrosion area.

[0044] In addition, in one or more embodiments of the present invention, the information of steel corrosion in reinforced concrete is inverted based on the fitness value of each particle in the particle group, specifically including: The fitness value of the i-th particle in the particle swarm is compared with the individual optimal position and the global optimal position of the i-th particle respectively to obtain the first comparison result and the second comparison result, where i is a positive integer.

[0045] When the first comparison result is that the fitness value of the ith particle is greater than the individual optimal position of the ith particle, the individual optimal position of the ith particle is replaced by the fitness value of the ith particle.

[0046] When the first comparison result is that the fitness value of the ith particle is not greater than the individual optimal position of the ith particle, the individual optimal position of the ith particle is kept unchanged.

[0047] When the second comparison result is that the fitness value of the ith particle is greater than the global optimal position of the ith particle, the global optimal position of the ith particle is replaced by the fitness value of the ith particle.

[0048] When the second comparison result is that the fitness value of the ith particle is not greater than the global optimal position of the ith particle, the global optimal position of the ith particle is kept unchanged.

[0049] In this embodiment, the first comparison result refers to the comparison result between the fitness value of the ith particle and the individual optimal position of the ith particle, and the second comparison result refers to the comparison result between the fitness value of the ith particle and the global optimal position of the ith particle. Each particle will record the best position it has found (individual optimal position), and will also pay attention to the best position found by the entire particle swarm (global optimal position). In each iteration, the particle will adjust its speed (step) and position according to its own individual optimal position and global optimal position. This information sharing mechanism enables particles to move more efficiently in the search space. The particle swarm optimization algorithm continuously updates the position and speed of the particles through multiple iterations. After each iteration, the particles will update the individual optimal position and the global optimal position according to the new fitness value, and then adjust their movement direction and speed until the maximum number of iterations is reached or the error meets the requirements.

[0050] This embodiment iterates the fitness value of each particle through the particle swarm optimization algorithm, and can obtain the final steel bar corrosion position and corrosion area size from the combination of multiple corrosion positions and corrosion area sizes of steel bar corrosion, thereby realizing more accurate detection of steel bar corrosion information in reinforced concrete.

[0051] In addition, in one or more embodiments of the present invention, it also includes: When the fitness value of the ith particle is greater than the preset threshold, the speed and position of the ith particle are updated with the individual optimal position and the global optimal position as the target until the fitness value of the ith particle is no greater than the preset threshold.

[0052] When the fitness value of the ith particle is not greater than a preset threshold and satisfies a priori conditions, the ith particle is output, and the priori conditions are obtained based on the half-potential distribution of reinforced concrete.

[0053] In this embodiment, the particle swarm is first initialized, the number of particles N is set, the search space is limited, and the position and speed of the particle swarm are initialized.

[0054] Among them, the number of particles N can be set according to the size of the search space. The method of limiting the search space includes but is not limited to: based on the half-potential distribution of reinforced concrete, obtaining the area where the half-potential value is in the preset potential range, and the preset potential range refers to the range of the half-potential generated after the steel bars in the reinforced concrete are corroded. The particle group position refers to the position of each particle in the particle group on the surface of the steel bar, and the particle group speed refers to the speed of iteration of each particle in the particle group, that is, the distance traveled by each particle from the current position to the next position after one iteration (that is, the step distance of the particle iteration).

[0055] In this embodiment, the iterative calculation formula used by the particle swarm algorithm is shown in formula (7) and formula (8), which updates the individual optimal position of each particle. pbest and the global optimal position gbest , by comparing the current particle with the individual optimal position pbest and the global optimal position gbest of ε(X) , if better than, then update to get a better pbest and gbest ,Right now: like ε(X i ) < ε(pbest) ,but pbest=X i ， like ε(X i ) < ε(gbest) ,but gbest=X i .

[0056] By comparison ε(X i ) Whether the set threshold is met ε(X 0 ) ,like ε(X i ) ≥ ε(X 0 ) , then the condition for stopping iteration is not met, and the particles are updated according to formulas (7) and (8), and pbest and gbest Get closer, repeatedly measure the actual potential value of each particle and calculate the predicted potential value of each particle, and input the predicted potential value and the actual potential value corresponding to each particle into the objective function for adaptability evaluation until the iteration stop condition is met, and output the particles that meet the bright condition, that is, inversion identifies the actual location and size of the steel bar surface corrosion.

[0057] This embodiment uses a particle swarm optimization algorithm combined with an objective function that evaluates the adaptability of each particle, compares each particle with its own current fitness value and the individual optimal position and the global optimal position, and updates the particle swarm based on the comparison results. Eventually, each particle converges after multiple iterations. The converged particles contain steel corrosion information, that is, the location and size of steel corrosion, and can identify the location and size of steel corrosion without damaging the reinforced concrete structure. This makes up for the defect that the current traditional detection method cannot detect the location and size of steel corrosion, especially in the early detection of steel corrosion in concrete. Since the signs of corrosion are not obvious, traditional detection methods are inconvenient to detect. The present invention has obvious advantages. It can also provide key theoretical support for the service life prediction, safety assessment, and maintenance and reinforcement of reinforced concrete structures, and has important engineering application prospects.

[0058] In addition, in one or more embodiments of the present invention, establishing a particle group related to information about steel bar corrosion specifically includes: The area of ​​steel bar corrosion is converted into one dimension to obtain a one-dimensional model of steel bar corrosion, and the coordinates of the one-dimensional model correspond to the area of ​​steel bar corrosion.

[0059] The coordinates of different positions in the one-dimensional model are determined as the size of the corrosion area of ​​each particle in the particle group, and the different positions of steel bar corrosion are determined as the positions of each particle in the particle group.

[0060] In this embodiment, the size of the steel bar corrosion area is converted into the size along the length direction of the steel bar, that is: (10) X i represents a particle group, CL i Indicates the location of steel bar corrosion, CS i Indicates the size of the steel bar corrosion area.

[0061] Through this embodiment, the corrosion area of ​​steel bars in reinforced concrete can be quantified, and the association between the corrosion area and corrosion position of steel bars in reinforced concrete and each particle is established. Different particles are used to represent corrosion areas of different sizes and different corrosion positions, which can fully include various possibilities of steel bar corrosion information.

[0062] In addition, in one or more embodiments of the present invention, the relationship between the inertia weight parameter and the number of iterations of the particle swarm optimization algorithm is expressed as follows: (11) in, w j is the inertia weight parameter, jis the number of iterations, j max is the maximum number of iterations, w min is the minimum inertia weight, w max is the maximum inertia weight.

[0063] In this embodiment, in order to improve the global search capability of the particle swarm optimization algorithm and avoid falling into the local optimal solution, the inertia weight parameter w j Set to follow the number of iterations j A linearly decreasing variable.

[0064] Optionally, in order to illustrate the universality of this method, a rectangular concrete column with 8 built-in steel bars is used here. At the same time, in order to reduce the time cost of calculation, one of the steel bars is randomly selected to establish a two-dimensional model to demonstrate the specific inversion process of the present invention as follows: like Figure 2 As shown, the cross-sectional area of ​​the reinforced concrete column is 300mm×300mm, the height is 4m, and the thickness of the concrete cover is 40mm.

[0065] Specifically, the half-potential distribution of one of the corroded steel bars in a reinforced concrete column is as follows: Figure 3 As shown, the horizontal axis is the half-potential distribution, the unit is millivolt (mV), and the vertical axis is the position of the steel bar corrosion in the Z direction (i.e. along the length of the steel bar), the unit is centimeter (cm).

[0066] refer to Figure 4 , take one of the steel bars to establish a two-dimensional geometric model, define the steel bar corrosion position CL represents the distance from the bottom of the column to the center of the corrosion, and the corrosion size (i.e. the size of the corrosion area) CS represents the corrosion size along the length direction of the steel bar.

[0067] The boundary conditions and model parameters are set, and the functional relationship between the potential and current density i of the cathode reaction and the anode reaction affected by oxygen concentration adopts the experimentally measured polarization curve, such as Figure 5 As shown, the horizontal axis is the current value, the unit is ampere / square meter (A / m 2 ), the ordinate is the potential φ , the unit is millivolt (mV).

[0068] The relationship between concrete saturation, oxygen diffusion coefficient and resistivity is obtained by fitting the actual test data, such as Figure 6 Shown: Resistivity of concrete in saturated state ρ 0 The initial oxygen concentration on the concrete surface is 66Ωm. 8.6 mol / m 3. Oxygen diffusion coefficient in concrete in completely dry state 1.71×10 -8 m 2 / s.

[0069] Set the particle swarm optimization algorithm parameters, the number of particles in the particle swarm is set to 25, the search space is CL∈(0,400), CS∈(0,80); the maximum number of iterations j max is 60; learning factor c 1 , c 2 Both are 0.8, and the stopping iteration threshold ε(X) is 1.0066.

[0070] For the weight parameter setting, in order to improve the global search capability of the particle swarm optimization algorithm and avoid falling into the local optimal solution, the inertia weight parameter w j Set to follow the number of iterations j It decreases linearly, as shown in formula (11).

[0071] The particle swarm optimization algorithm performs optimization iteration based on the update rule. Different saturation will directly affect the resistivity and oxygen diffusion coefficient of concrete, and then affect the numerical calculation potential of the steel bar surface. However, the potential distribution is a priori constraint condition for numerical inversion, so the inversion results are different.

[0072] like Figures 7 to 12 is the inverse iteration process of the location and size of steel bar corrosion when the concrete saturation is 0.6. Figure 7 The first inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention, Figure 8 The second inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention, Fig. 9 The third inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention, Fig.10 The fourth inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention, Fig.11 The fifth inversion iteration process of the steel bar corrosion position and size when the concrete saturation is 0.6 provided by the present invention, Fig.12 The sixth inversion iteration process of the location and size of steel bar corrosion when the concrete saturation is 0.6 provided by the present invention. Figures 7 to 12 It can be seen that after multiple iterations, the overall optimal solution is gradually found, and all particles converge to a certain point (CL=35.31cm, CS=68.15cm), that is, the position of the actual concrete column steel bar corrosion is 35.31cm, and the corrosion size is 68.15cm.

[0073] Table 1 shows the calculation results of numerical inversion under different saturations. The numerical inversion calculation results under different saturations are compared with the actual steel bar corrosion state of the concrete column to verify the accuracy of the inversion results. The actual concrete column corrosion state, the actual steel bar corrosion position and size: CL = 35.31cm, CS = 68.15cm, concrete saturation is 0.6. The verification results show that when the saturation is 0.6, the inversion result error is the smallest, the corrosion position error is 10.3%, and the corrosion size error is 6.1%. The results accurately correspond to the steel bar corrosion state in the actual concrete column. The present invention can accurately invert the steel bar corrosion position and size of the actual concrete structure.

[0074] Table 1 Calculation results of numerical inversion at different water saturations

[0075] The above is a method for detecting steel bar corrosion information in reinforced concrete provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for detecting steel bar corrosion information in reinforced concrete, such as Fig.13 shown.

[0076] Fig.13 A schematic diagram of a device for detecting steel bar corrosion information in reinforced concrete provided by the present invention, comprising: The acquisition module is used to obtain the half-potential distribution of reinforced concrete.

[0077] The first building module is used to build a corrosion model of steel bar corrosion potential distribution in reinforced concrete according to the functional relationship between water saturation, oxygen diffusion coefficient and concrete resistance.

[0078] The second establishing module is used to establish a particle group related to the information of steel bar corrosion, and the parameters of the particles in the particle group include the position of steel bar corrosion in reinforced concrete and the size of the corrosion area.

[0079] The inversion module is used to determine the fitness value of each particle in the particle swarm related to the information of steel corrosion based on the half-potential distribution and corrosion model by using the particle swarm optimization algorithm; the information of steel corrosion in reinforced concrete is inverted based on the fitness value of each particle in the particle swarm to obtain the location of steel corrosion in reinforced concrete and the size of the corrosion area.

[0080] The specific definition of the steel bar corrosion information detection device in reinforced concrete can refer to the definition of the steel bar corrosion information detection method in reinforced concrete above, which will not be repeated here. Each module in the steel bar corrosion information detection device in reinforced concrete can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0081] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the Figure 1 A method for detecting steel corrosion information in reinforced concrete is provided.

[0082] The present invention also provides Fig.14 The structural diagram of the computer device shown in FIG. Fig.14 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the Figure 1 A method for detecting steel corrosion information in reinforced concrete is provided.

[0083] A person of ordinary skill in the art can understand that all or part of the processes in the embodiment method can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the methods described. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for detecting steel bar corrosion information in reinforced concrete, characterized in that: include: Obtain the half-potential distribution of reinforced concrete; A corrosion model of the steel bar corrosion potential distribution in the reinforced concrete is established according to the functional relationship between the saturation degree, the oxygen diffusion coefficient and the concrete resistance; Establishing a particle group related to the information of the steel bar corrosion, wherein the parameters of the particles in the particle group include the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area; Based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in a particle swarm related to the information of the steel bar corrosion; based on the fitness value of each particle in the particle swarm, the information of the steel bar corrosion in the reinforced concrete is inverted to obtain the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area.

2. The method for detecting steel bar corrosion information in reinforced concrete according to claim 1, characterized in that: Based on the half-potential distribution and the corrosion model, a particle swarm optimization algorithm is used to determine the fitness value of each particle in a particle swarm related to the information of the steel bar corrosion, specifically including: Substituting the parameters of each particle in the particle group into the corrosion model to obtain the predicted potential value corresponding to each particle; Obtaining the actual potential value of each particle in the particle group at the position where the particle is located from the half-potential distribution of the reinforced concrete, and constructing an objective function according to the predicted potential value and the actual potential value; The actual potential value at the position of each particle and the predicted potential value corresponding to each particle are respectively input into the objective function to obtain the fitness value of each particle.

3. The method for detecting steel bar corrosion information in reinforced concrete according to claim 1, characterized in that: Inverting the information of steel corrosion in the reinforced concrete based on the fitness value of each particle in the particle group specifically includes: Compare the fitness value of the i-th particle in the particle group with the individual optimal position and the global optimal position of the i-th particle respectively to obtain a first comparison result and a second comparison result, where i is a positive integer; When the first comparison result is that the fitness value of the i-th particle is greater than the individual optimal position of the i-th particle, the individual optimal position of the i-th particle is replaced by the fitness value of the i-th particle; When the first comparison result is that the fitness value of the i-th particle is not greater than the individual optimal position of the i-th particle, the individual optimal position of the i-th particle is kept unchanged; When the second comparison result is that the fitness value of the i-th particle is greater than the global optimal position of the i-th particle, the global optimal position of the i-th particle is replaced by the fitness value of the i-th particle; When the second comparison result is that the fitness value of the i-th particle is not greater than the global optimal position of the i-th particle, the global optimal position of the i-th particle is kept unchanged.

4. The method for detecting steel bar corrosion information in reinforced concrete according to claim 3, characterized in that: The method further comprises: When the fitness value of the i-th particle is greater than a preset threshold, taking the individual optimal position and the global optimal position as targets, updating the speed and position of the i-th particle until the fitness value of the i-th particle is no greater than the preset threshold; When the fitness value of the i-th particle is not greater than the preset threshold and satisfies a priori conditions, the i-th particle is output, and the priori conditions are obtained based on the half-potential distribution of the reinforced concrete.

5. The method for detecting steel bar corrosion information in reinforced concrete according to claim 1, characterized in that: The step of establishing a particle group related to the information of steel bar corrosion specifically includes: Converting the area of ​​the steel bar corrosion into one dimension to obtain a one-dimensional model of the steel bar corrosion, wherein the coordinates of the one-dimensional model correspond to the area of ​​the steel bar corrosion in a one-to-one manner; The coordinates of different positions in the one-dimensional model are determined as the size of the corrosion area of ​​each particle in the particle group, and the different positions of the steel bar corrosion are determined as the positions of each particle in the particle group.

6. The method for detecting steel bar corrosion information in reinforced concrete according to claim 1, characterized in that: The relationship between the inertia weight parameter and the number of iterations of the particle swarm optimization algorithm is expressed as follows: ； in, w j is the inertia weight parameter, j is the number of iterations, j max is the maximum number of iterations, w min is the minimum inertia weight, w max is the maximum inertia weight.

7. The method for detecting steel bar corrosion information in reinforced concrete according to claim 2, characterized in that: The expression of the objective function is: ； in, X represents particles, N The number representing the actual potential value, φ l A vector representing the predicted potential values, is the vector of the actual potential value, is the vector maximum value of the actual potential value.

8. A device for detecting steel bar corrosion information in reinforced concrete, characterized in that: include: An acquisition module, used for acquiring the half-potential distribution of reinforced concrete; A first establishing module is used to establish a corrosion model of steel corrosion potential distribution in the reinforced concrete according to the functional relationship between water saturation, oxygen diffusion coefficient and concrete resistance; A second establishing module is used to establish a particle group related to the information of the steel bar corrosion, wherein the parameters of the particles in the particle group include the position of the steel bar corrosion in the reinforced concrete and the size of the corrosion area; The inversion module is used to determine the fitness value of each particle in the particle swarm related to the information of the steel corrosion based on the half-potential distribution and the corrosion model by using a particle swarm optimization algorithm; based on the fitness value of each particle in the particle swarm, the information of the steel corrosion in the reinforced concrete is inverted to obtain the position of the steel corrosion in the reinforced concrete and the size of the corrosion area.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting steel bar corrosion information in reinforced concrete according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for detecting steel bar corrosion information in reinforced concrete as claimed in any one of claims 1 to 7 is implemented.

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