Non-destructive testing method for concrete structural member exposure test

Abstract

The application discloses a nondestructive testing method for concrete structure member exposure test, a sensor for detection is pre-buried in a concrete member, and detection data of the concrete member under the condition of salt and fresh water alternation is acquired in real time; a relative dynamic elastic modulus change characteristic is obtained by using ultrasonic detector data calculation; a high-resolution optical camera system and a three-dimensional laser scanner are used to quantize concrete member denudation area distribution characteristic information; a concrete member deterioration model and a bearing capacity calculation model under the condition of salt and fresh water alternation are established, the remaining service life of the concrete member under the salt and fresh water alternation environment is predicted according to a set failure standard; various detection data acquired in the whole exposure test process are integrated and stored, and a database is established; and the application discloses a multi-modal data correlation analysis, reveals a synergistic mechanism of the concrete member under the salt and fresh water alternation environment, provides a data cornerstone for a cross-sea area project, and realizes nondestructive testing of the concrete structure member exposure test.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and more specifically, to a nondestructive testing method for exposure testing of concrete structural components. Background Technology

[0002] Concrete structures, due to their excellent load-bearing capacity and economy, are widely used in various infrastructure projects, such as bridges, seaports, and nuclear power plants. Over long-term use, these structures are affected by various factors, including the natural environment and operational loads, leading to performance degradation and defects that threaten their safety and durability. To promptly detect and assess these problems and ensure structural safety and reliability, effective testing methods are needed. Many existing testing methods rely on single technical means. While these methods can provide some useful information to a certain extent, due to the complexity of concrete structures and the diversity of defects, single-faceted testing often fails to accurately and comprehensively reflect the true condition of the components; it also fails to organically integrate and comprehensively analyze data obtained from different testing methods.

[0003] Most existing technologies can only provide test data, but they are insufficient in systematically analyzing this data to reveal the deterioration patterns and damage mechanisms of concrete structures. Test results are often just isolated data points, making it difficult to correlate them with the actual stress conditions of the structure and its service environment for overall analysis, thus failing to provide strong technical support for structural maintenance decisions.

[0004] Due to the incompleteness of test data and the limitations of analytical methods, existing technologies struggle to establish accurate and reliable models for predicting the remaining service life of concrete structures based on test results. This leaves engineers without a scientifically sound basis for structural maintenance and management, forcing them to rely on experience and periodic inspections to assess structural safety, thus increasing the risks and uncertainties associated with the structure's use. Summary of the Invention

[0005] The purpose of this invention is to provide a non-destructive testing method for exposed concrete structural components, in order to solve the above-mentioned problems existing in the prior art.

[0006] The application is as follows:

[0007] A non-destructive testing method for exposed concrete structural members is provided, comprising the following steps:

[0008] Step 1: Embed sensors for detection in concrete components to acquire real-time detection data of concrete components under alternating fresh and salt water conditions; fit the diffusion coefficient of chloride ions in concrete components based on Fick's second law to generate a chloride ion diffusion feature set of concrete components under alternating fresh and salt water conditions;

[0009] Step 2: Using an ultrasonic detector, ultrasonic pulse signals of a specific frequency are emitted to the concrete component. Based on the elastic wave theory of concrete, the characteristics of the relative dynamic elastic modulus change of concrete under the alternating action of fresh and salt water are calculated.

[0010] Step 3: Using a high-resolution optical camera system and a 3D laser scanner, the surface of the concrete component is periodically scanned and images are acquired. Multimodal algorithms are used to identify and quantify the area, depth, and distribution characteristics of the eroded areas on the surface of the concrete component.

[0011] Step 4: Based on the analysis results of the detection data of chloride ion diffusion characteristic set, relative dynamic elastic modulus and distribution characteristics, establish a deterioration model of concrete components under alternating fresh and salt water conditions. Combine the actual stress conditions and boundary conditions of concrete components, construct a load-bearing capacity calculation model of reinforced concrete components under alternating fresh and salt water conditions, and predict the remaining service life of concrete components in the alternating fresh and salt water environment according to the set failure criteria.

[0012] Step 5: Systematically integrate and store all types of test data obtained throughout the exposure test, establish a database, and provide data references on the damage and deterioration mechanisms and performance evolution laws of concrete structural components under alternating fresh and salt water conditions.

[0013] Step one includes establishing a chloride ion diffusion model in concrete components based on Fick's second law, determining boundary conditions and initial conditions; fitting the obtained chloride ion concentration under alternating fresh and salt water conditions to the diffusion model to obtain the diffusion coefficient, and generating a chloride ion diffusion feature set of concrete components under alternating fresh and salt water conditions.

[0014] Step three includes:

[0015] Bilateral filtering is used to remove noise from point cloud data and image data. A segmentation algorithm is used to distinguish multiple independent erosion blocks in the image, identify and label the erosion areas, and clarify the boundaries and range of each erosion block.

[0016] Analyze the curvature changes of point cloud data to detect irregular surfaces, and use clustering algorithms to group points with similar curvature characteristics together to distinguish eroded areas from normal areas;

[0017] Feature matching is used to find the corresponding feature points in the point cloud data of laser scanning and the image data acquired by the optical camera system. The ICP algorithm is used to optimize the matching accuracy of the feature points and align the point cloud data and image data to the same coordinate system.

[0018] A multimodal algorithm was used to quantify the erosion area and obtain the distribution characteristics of the erosion area of ​​concrete components under alternating fresh and salt water conditions, including concrete morphological features.

[0019] A multimodal algorithm is used to quantify the eroded area, obtaining the area, depth, and distribution characteristics including concrete morphology features of the eroded area of ​​the concrete component.

[0020] The eroded area of ​​the concrete component is projected onto a two-dimensional plane, and the area of ​​the convex hull is calculated to obtain the area of ​​the eroded area. The local reference plane method is used to model a local reference plane for the intact area around each eroded block to obtain the maximum depth and average depth, thereby quantifying the depth characteristics of the eroded area.

[0021] The K-function is used for nearest neighbor analysis to assess the spatial randomness or clustering of erosion points. The fractal dimension is calculated to quantify the complexity of the erosion boundary. The minimum bounding rectangle is used to analyze the aspect ratio orientation of the erosion shape and obtain the distribution characteristics that include the morphological features of the concrete.

[0022] Step four includes:

[0023] Based on the chloride ion diffusion feature set and the distribution characteristics of concrete components, the variation law of chloride ion concentration on the surface of concrete components with time is calculated using numerical methods to obtain the starting time of erosion of concrete components.

[0024] Using the relative dynamic elastic modulus data obtained from the test, combined with the characteristics of alternating fresh and salt water conditions and the start time, the erosion rate and loss cross-sectional area of ​​the concrete component are calculated, and a deterioration model of the concrete component is established.

[0025] Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key capacity of the concrete member to resist failure is calculated, and the member deterioration is incorporated into the bearing capacity calculation model.

[0026] Based on the bearing capacity judgment threshold, the current age of the concrete component is taken as the starting point. Based on the bearing capacity degradation function and failure judgment criteria, the total life of the concrete component is predicted, and the remaining service life of the current concrete component in the alternating fresh and salt water environment is obtained.

[0027] Using the relative dynamic elastic modulus data obtained from the tests, combined with the characteristics of alternating fresh and salt water conditions and the start time, the erosion rate and loss cross-sectional area of ​​the concrete members are calculated, and a deterioration model of the concrete members is established, including:

[0028] Based on the relative dynamic elastic modulus, erosion rate, and lost cross-sectional area, and considering the influence of the freshwater-salt water alternation period T on the deterioration process, the deterioration model of concrete components is determined as follows:

[0029]

[0030] A loss(t) represents the degree of deterioration of the concrete member, α represents the coefficient related to the geometry and stress characteristics of the concrete member, A0 represents the initial degree of deterioration, and i c E0(t) represents the erosion rate at time t, E0(t) represents the initial relative dynamic elastic modulus, λ represents the attenuation coefficient, T represents the period of alternation between fresh and salt water, and k represents a constant related to the properties of concrete building materials and environmental concentration.

[0031] Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key resistance capacity of the concrete member to failure is calculated, and member deterioration is incorporated into the bearing capacity calculation model, including:

[0032] The types of loads actually borne by concrete members are divided into member loads, variable loads, and environmental loads, and the magnitude of the load combination is calculated.

[0033] The constraint conditions are determined based on the actual support method of the concrete member, and the initial bearing capacity is calculated. The initial bearing capacity includes the bending bearing capacity, shear bearing capacity, and axial compression bearing capacity.

[0034] Based on the degree of deterioration of the concrete component, the deterioration factor is determined, the comprehensive bearing capacity is calculated, and the bearing threshold of the concrete component is determined.

[0035] The comprehensive bearing capacity is expressed as C. Z =A loss (t)·(M u0 *K dM +V u0 *K dV +N u0 *K dN ), where C Z Indicates comprehensive bearing capacity, A loss (t) represents the degree of deterioration of the concrete component, M u0 K represents the bending capacity. dM V represents the degradation factor corresponding to the flexural bearing capacity. u0 K represents the shear bearing capacity. dV N represents the degradation factor corresponding to the shear bearing capacity. u0 K represents the axial compression bearing capacity. dN This represents the degradation factor corresponding to the axial compression bearing capacity.

[0036] Based on the bearing capacity threshold, taking the current age of the concrete member as a starting point, and using the bearing capacity degradation function and failure criteria, the total lifespan of the concrete member is predicted, resulting in the remaining service life of the current concrete member in an alternating fresh and saltwater environment, including:

[0037] Obtain the current service age t0 and current comprehensive bearing capacity C of the concrete member. Z(t), considering the chloride ion concentration C in the environment where the concrete component is located. l (t), alternating frequency f e and the current ambient temperature T N (t);

[0038] A bearing capacity degradation function under alternating fresh and salt water conditions is constructed, and the future bearing capacity is predicted step by step with a certain time step, starting from the current service age t0.

[0039] In each step, the chloride ion concentration, the degree of deterioration of the concrete component, and the bearing capacity are updated according to the bearing capacity degradation function. The time when the bearing capacity first falls below the bearing threshold L is recorded, which is the remaining service life t. s .

[0040] The bearing capacity degradation function is expressed as:

[0041]

[0042] Among them, C Z (t) represents the current comprehensive bearing capacity, C0 represents the bearing capacity of the component in its initial state, ∈ represents the weighting coefficient of the influence of erosion on the bearing capacity, and A s (t) represents the damaged cross-sectional area of ​​the concrete member, A s0 Let A represent the initial cross-sectional area, ζ represent the nonlinear exponent of erosion influence, and η represent the weighting coefficient of the impact of concrete deterioration on bearing capacity; loss (t) represents the degree of deterioration of the concrete member, A0 represents the initial degree of deterioration, θ represents the nonlinear exponent of the effect of concrete deterioration, and ψ represents the temperature influence coefficient. N (t) represents the current ambient temperature, T C Indicates the reference temperature, η h This indicates the influence coefficient of environmental humidity.

[0043] Compared with the prior art, the present invention achieves the following beneficial effects:

[0044] 1. By employing various methods such as pre-embedded sensors, ultrasonic detectors, optical imaging systems, and 3D laser scanners, concrete components are monitored comprehensively and in multiple dimensions. Based on Fick's law, the chloride ion diffusion coefficient is fitted in real time to accurately reflect permeability degradation, solving the problem that traditional single-detection methods cannot quantify the dynamics of ion erosion. Ultrasonic dynamic modulus testing is used to non-destructively track the development of micro-cracks inside the concrete, overcoming the limitations of surface damage monitoring. The integration of optical and laser scanning technologies enables high-precision spatial modeling of the erosion area, quantifying the geometric characteristics of damage and avoiding errors from manual observation. It can more comprehensively and accurately reflect the true condition of concrete components in alternating fresh and salt water environments.

[0045] 2. Combining detection data from multiple aspects, such as chloride ion diffusion characteristics, relative dynamic elastic modulus changes, and surface erosion characteristics, helps to conduct in-depth research on the deterioration mechanism of concrete components under alternating fresh and salt water conditions, reveal the interaction and influence laws between different factors, and the concrete component deterioration model and bearing capacity calculation model established based on the detection data can more accurately simulate the performance evolution process of concrete components in alternating fresh and salt water environments, providing a more reliable basis for predicting their remaining service life.

[0046] 3. Through multimodal data correlation analysis, the synergistic mechanism of concrete components in alternating fresh and salt water environments is revealed. Early and accurate warnings guide the maintenance window period of key components. A standardized database is constructed to provide a data foundation for concrete selection and protection design in cross-sea engineering projects, and non-destructive testing of concrete structural components through exposure experiments is realized. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the non-destructive testing method for exposed concrete structural components provided in this embodiment of the invention.

[0048] Figure 2 This is a flowchart illustrating step three according to an embodiment of the present invention.

[0049] Figure 3 This is a flowchart illustrating step four according to an embodiment of the present invention. Detailed Implementation

[0050] The present invention will now be described in detail with reference to the accompanying drawings.

[0051] Example

[0052] like Figure 1 As shown, the present invention provides a non-destructive testing method for exposed concrete structural members, comprising the following steps:

[0053] Step 1: Embed sensors for detection in concrete components to acquire real-time detection data of concrete components under alternating fresh and salt water conditions; fit the diffusion coefficient of chloride ions in concrete components based on Fick's second law to generate a chloride ion diffusion feature set of concrete components under alternating fresh and salt water conditions;

[0054] Step 2: Using an ultrasonic detector, ultrasonic pulse signals of a specific frequency are emitted to the concrete component. Based on the elastic wave theory of concrete, the characteristics of the relative dynamic elastic modulus change of concrete under the alternating action of fresh and salt water are calculated.

[0055] Step 3: Using a high-resolution optical camera system and a 3D laser scanner, the surface of the concrete component is periodically scanned and images are acquired. Multimodal algorithms are used to identify and quantify the area, depth, and distribution characteristics of the eroded areas on the surface of the concrete component.

[0056] Step 4: Based on the analysis results of the detection data of chloride ion diffusion characteristic set, relative dynamic elastic modulus and distribution characteristics, establish a deterioration model of concrete components under alternating fresh and salt water conditions. Combine the actual stress conditions and boundary conditions of concrete components, construct a load-bearing capacity calculation model of reinforced concrete components under alternating fresh and salt water conditions, and predict the remaining service life of concrete components in the alternating fresh and salt water environment according to the set failure criteria.

[0057] Step 5: Systematically integrate and store all kinds of test data obtained during the entire exposure test, establish a database, and provide data reference on the damage and deterioration mechanism and performance evolution law of concrete structural components under alternating fresh and salt water conditions;

[0058] The detection data includes chloride ion concentration, alternation frequency, ambient temperature, and immersion depth;

[0059] Step one includes establishing a chloride ion diffusion model in concrete components based on Fick's second law, determining boundary conditions and initial conditions; fitting the obtained chloride ion concentration under alternating fresh and salt water conditions to the diffusion model to obtain the diffusion coefficient, and generating a chloride ion diffusion feature set of concrete components under alternating fresh and salt water conditions.

[0060] The diffusion model is as follows:

[0061]

[0062] Among them, C f C represents the concentration of free chloride ions in concrete components. s C represents the concentration of free chloride ions on the surface of concrete components. f0 Let L1 represent the initial free chloride ion concentration in the concrete member, D represent the chloride ion diffusion coefficient of the concrete member, L1 represent the depth of the concrete member in the x direction, L2 represent the depth in the y direction, and m and n represent the diffusion time in the x and y directions, respectively.

[0063] The initial condition assumes that at the initial time t = 0, the chloride ion concentration inside the concrete member is uniform and zero. The boundary condition is:

[0064] At a chloride ion concentration of 0, the chloride ion concentration changes with alternating fresh and salt water conditions. In a saltwater environment, the chloride ion concentration is C. f When in a freshwater environment, the chloride ion concentration is approximately 0; this alternating change can be described by a periodic function, C(0,t)=Cf0 ×[1+C×cos(ωt)], where C is the amplitude (0≤C≤1) and ω is the alternating angular frequency;

[0065] The chloride ion diffusion feature set includes using a diffusion model to simulate the diffusion process of chloride ions in concrete components, and calculating the distribution of chloride ion concentration and chloride ion concentration and diffusion coefficient under different saltwater alternation cycles, different locations and times.

[0066] Step two includes:

[0067] The relative dynamic modulus of elasticity is calculated by measuring the propagation velocity of ultrasonic longitudinal waves in a concrete member and utilizing the relationship between elastic wave velocity and material stiffness. The relative dynamic modulus of elasticity is expressed as:

[0068]

[0069] Among them, E d (t) represents the relative dynamic elastic modulus at time t, ρ represents the concrete density, v represents the propagation speed of ultrasonic longitudinal waves in the concrete member, and λ represents Poisson's ratio.

[0070] like Figure 2 As shown, step three of the present invention includes:

[0071] Bilateral filtering is used to remove noise from point cloud data and image data. A segmentation algorithm is used to distinguish multiple independent erosion blocks in the image, identify and label the erosion areas, and clarify the boundaries and range of each erosion block.

[0072] Analyze the curvature changes of point cloud data to detect irregular surfaces, and use clustering algorithms to group points with similar curvature characteristics together to distinguish eroded areas from normal areas;

[0073] Feature matching is used to find the corresponding feature points in the point cloud data of laser scanning and the image data acquired by the optical camera system. The ICP algorithm is used to optimize the matching accuracy of the feature points and align the point cloud data and image data to the same coordinate system.

[0074] A multimodal algorithm was used to quantify the erosion area and obtain the distribution characteristics of the erosion area of ​​concrete components under alternating fresh and salt water conditions, including concrete morphological features.

[0075] Bilateral filtering is used to remove noise from point cloud data and image data, including:

[0076] Centered on the current point, a spherical or cubic neighborhood is defined to determine the neighborhood range for filtering point cloud data. The spatial distance weight and normal vector weight of each point within the neighborhood are calculated relative to the current point. The spatial distance weight is calculated based on a Gaussian function, with higher weights for closer points. The normal vector weight is determined based on the angle between normal vectors, with higher weights for smaller angles. A weighted average is then applied to the points within the neighborhood based on these weights to obtain the filtered point coordinates.

[0077] The size of the neighborhood window for image data filtering is determined, and the spatial distance weight and gray value weight of each pixel in the neighborhood relative to the current pixel are calculated. The spatial distance weight is calculated based on the positional difference between pixels, and the gray value weight is determined according to the magnitude of the gray value difference, with a higher weight for smaller differences. The gray values ​​of the pixels in the neighborhood are then weighted and averaged according to the weights to obtain the filtered pixel gray values.

[0078] A segmentation algorithm is used to distinguish multiple independent erosion blocks in the image, identify and label the erosion regions, and clarify the boundaries and extent of each erosion block, including:

[0079] The region proposal network generates candidate regions for multiple independent erosion blocks segmented in the concrete component image data. The candidate regions are then feature-extracted and classified to determine whether they are erosion regions. At the same time, corresponding masks are generated and labeled. After training, the mask, category, and location information of each erosion block are output, clarifying the boundary and range of each erosion block.

[0080] Analyzing the curvature variations of point cloud data to detect irregular surfaces, clustering algorithms are used to group points with similar curvature characteristics together, distinguishing eroded areas from normal areas, including:

[0081] The curvature of each point in the point cloud data of the concrete component is calculated, and the curvature is determined by analyzing the distribution of neighboring points. The K-means++ clustering algorithm is used to cluster the point cloud data, and the points are divided into different categories according to their curvature characteristics. Based on the clustering results, the eroded area and the normal area are distinguished.

[0082] Specifically, feature matching is used to find the corresponding feature points in the laser scanning point cloud data and the image data acquired by the optical camera system. The ICP algorithm is used to optimize the matching accuracy of the feature points, and the point cloud data and image data are aligned to the same coordinate system, including:

[0083] Feature matching algorithms are used to match feature points in point cloud data and image data to find corresponding feature point pairs. These matching point pairs are then used to initially align the point cloud data and image data. The ICP algorithm is then used to iteratively calculate the nearest point distance between the point cloud and the image, and the transformation parameters of the point cloud or image are adjusted to gradually reduce the error between them. Finally, the aligned point cloud data and image data are fused. The feature matching algorithm used is FLANN (Fast Library for Approximate Nearest Neighbors) or brute-force matching, etc., and is not specifically limited here.

[0084] Specifically, the step of iteratively calculating the nearest point distance between the point cloud and the image using the ICP algorithm, adjusting the transformation parameters of the point cloud or image to gradually reduce the error between them, and then fusing the aligned point cloud data and image data includes:

[0085] Given an initial transformation parameter, including a rotation matrix and a translation vector, in each iteration, the distance between each point in the point cloud and its nearest feature point in the image under the current transformation parameters is first calculated. Based on these nearest point distances, the transformation parameters are adjusted using the least squares method. The objective function to be minimized is set as the sum of the squares of the Euclidean distances between all point cloud points and their corresponding image feature points. By solving the optimization problem of this objective function, a new rotation matrix and translation vector are obtained, which are used to update the transformation parameters of the point cloud. An error threshold is set. When the error change between two adjacent iterations is less than this threshold, or when the maximum number of iterations is reached, the algorithm is considered to have converged and the iteration stops. Once the point cloud and the image are aligned using the ICP algorithm, they are fused.

[0086] A multimodal algorithm is used to quantify the eroded area, obtaining the area, depth, and distribution characteristics including concrete morphology features of the eroded area of ​​the concrete component.

[0087] The eroded area of ​​the concrete component is projected onto a two-dimensional plane, and the area of ​​the convex hull is calculated to obtain the area of ​​the eroded area. The local reference plane method is used to model a local reference plane for the intact area around each eroded block to obtain the maximum depth and average depth, thereby quantifying the depth characteristics of the eroded area.

[0088] The K-function is used for nearest neighbor analysis to evaluate the spatial randomness or clustering of erosion points. The fractal dimension is calculated to quantify the complexity of the erosion boundary. The minimum bounding rectangle is used to analyze the orientation characteristics of the aspect ratio of the erosion shape and obtain the distribution characteristics that include the morphological features of concrete.

[0089] The formula for calculating the convex hull area is: Where S represents the convex hull area of ​​the concrete member, e represents the total number of vertices of the convex hull, (xi y i () represents the coordinates of the i-th vertex of the convex hull;

[0090] For each erosion area, within a certain distance around each erosion area, points not marked as eroded are selected to identify surrounding intact areas. Using the selected intact area points, a smooth reference surface is established through a fitting algorithm, which employs the least squares method for fitting. For each point within the erosion area, the vertical distance from that point to its corresponding local reference surface is calculated. This vertical distance represents the depth of the depression of that point relative to the original surface. The maximum value of all erosion point distances is found, representing the depth of the most severe local damage to the concrete member. The arithmetic mean of all erosion point distances is calculated to obtain the average depth.

[0091] The K function is expressed as:

[0092] Where A represents the area of ​​the concrete component, l represents the number of erosion points, and d fk The distance from erosion point f to erosion point k is represented by r, the search radius is represented by I(), and the indicator function is represented by e. kf Indicates the boundary correction factor; The parameters represent the nearest neighbor analysis results; the boundary correction factor e kf The value is automatically calculated by spatial analysis software and is used to compensate for the underestimation of the distance of the erosion point due to the boundary. The value is a proportional value between 0 and 1.

[0093] The fractal dimension is calculated to quantify the complexity of the erosion boundary, and the aspect ratio orientation characteristics of the erosion shape are analyzed using the minimum bounding rectangle, obtaining distribution characteristics that include concrete morphological features, including:

[0094] Boundary point sequences are extracted from point cloud data and sorted into closed polygons. A grid scale sequence is set, and for each grid scale ε, the study area is covered with an ε×ε grid. The number of grids containing boundary points N(ε) is counted. Logarithmic coordinates are set as X = log(1 / ε) and Y = log(N(ε)). Linear regression is performed on the (X,Y) data, and the regression slope is the fractal dimension. For each erosion shape, the smallest area rectangle that can completely contain the erosion shape is found, i.e., the minimum bounding rectangle. The long side and short side of this rectangle correspond to the maximum and minimum extension lengths of the erosion shape in a certain direction, respectively. By analyzing the direction of the long side, the main orientation of the erosion shape is determined. For each erosion region, in addition to analyzing its aspect ratio and orientation characteristics, its location, area, and depth characteristics are recorded. The feature data of all erosion regions are summarized to obtain the distribution characteristics of erosion in the entire concrete structure component.

[0095] like Figure 3As shown, step four of the present invention includes:

[0096] Based on the chloride ion diffusion feature set and the distribution characteristics of concrete components, the variation law of chloride ion concentration on the surface of concrete components with time is calculated using numerical methods to obtain the starting time of erosion of concrete components.

[0097] Using the relative dynamic elastic modulus data obtained from the test, combined with the characteristics of alternating fresh and salt water conditions and the start time, the erosion rate and loss cross-sectional area of ​​the concrete component are calculated, and a deterioration model of the concrete component is established.

[0098] Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key capacity of the concrete member to resist failure is calculated, and the member deterioration is incorporated into the bearing capacity calculation model.

[0099] Based on the bearing capacity judgment threshold, the current age of the concrete component is taken as the starting point. Based on the bearing capacity degradation function and failure judgment criteria, the total life of the concrete component is predicted, and the remaining service life of the current concrete component in the alternating fresh and salt water environment is obtained.

[0100] The variation of chloride ion concentration on the surface of concrete components over time is expressed as follows:

[0101]

[0102] in, This represents the chloride ion concentration at spatial location (pΔx, qΔy) and time step wΔt; p and q represent the indices of spatial locations in the x and y directions, respectively; Δx and Δy represent the spatial steps in the x and y directions, respectively; w represents the index of the time step; and Δt represents the time step.

[0103] Based on the variation of chloride ion concentration over time, and using the concrete surface as a reference, the chloride ion concentration was determined point by point inwards to determine whether it reached the critical concentration C. crit When the calculated chloride ion concentration at a certain point inside the concrete first reaches the critical concentration, the corresponding time is the starting time t0 of the erosion of the concrete component.

[0104] The erosion rate is:

[0105] i c (t)=i0·f r (t)·(1+K E ·(1-E d (t)))

[0106] i c (t) represents the erosion rate at time t, i0 represents the baseline erosion rate, and f r(t) represents the circulation factor under brackish water conditions, K E E represents the damage acceleration coefficient. d (t) represents the relative elastic modulus; the circulation factor f under the brackish water conditions. r (t) is calculated based on on-site environmental monitoring data, characterizing the acceleration factor of the baseline erosion rate during the alternating wet and dry and concentration change cycles in brackish water construction; the damage acceleration coefficient K is... E Experimental measurements show that the additional accelerating effect of internal damage to concrete components on the baseline erosion rate is usually an empirical constant greater than zero.

[0107] The lost cross-sectional area of ​​the concrete member is expressed as:

[0108]

[0109] A s (t) represents the damaged cross-sectional area of ​​the concrete member, A s0 Let d0 represent the initial cross-sectional area, d0 represent the diameter of the concrete member, and τ represent the time integration variable.

[0110] Based on the relative dynamic elastic modulus, erosion rate, and lost cross-sectional area, and considering the influence of the freshwater-salt water alternation period T on the deterioration process, the deterioration model of concrete components is determined as follows:

[0111]

[0112] A loss (t) represents the degree of deterioration of the concrete member, α represents the coefficient related to the geometry and stress characteristics of the concrete member, A0 represents the initial degree of deterioration, and i c E0(t) represents the erosion rate at time t, E0(t) represents the initial relative dynamic elastic modulus, λ represents the attenuation coefficient, T represents the period of alternation between fresh and salt water, and k represents a constant related to the properties of concrete building materials and environmental concentration.

[0113] The coefficient α, related to the geometry and stress characteristics of the concrete component, was obtained through finite element analysis. This analysis involved creating a geometric model of the concrete component using finite element software, setting concrete material properties, applying appropriate loads based on the component's stress state in an actual structure, simulating concrete component damage, and quantifying the influence of the component's geometry and stress characteristics on the deterioration process by analyzing the rate of bearing capacity decrease under different geometries and stress states and comparing it with the deterioration data of standard specimens. The attenuation coefficient λ was determined experimentally using environmental simulation. Rapid experiments were conducted, and multiple sets of concrete component specimens were prepared and placed in a brackish water wet-dry cycle accelerated test chamber simulating the actual environment. The exponential decay law of performance over time in the environment was simulated, and the decay coefficient λ was obtained by curve fitting, which is an empirical coefficient of the performance degradation rate of concrete components. The constant k, which is related to the properties of concrete building materials and environmental concentration, was determined experimentally. Through erosion rate measurement experiments with multiple concentration gradients, the performance response of materials under different erosion concentrations was tested, and the constant k, which is related to the properties of concrete building materials and environmental concentration, was obtained by fitting and calibration. It is a comprehensive constant of the balance between the erosion resistance of concrete components and the intensity of environmental erosion.

[0114] Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key resistance capacity of the concrete member to failure is calculated, and member deterioration is incorporated into the bearing capacity calculation model, including:

[0115] The types of loads actually borne by concrete members are divided into member loads, variable loads, and environmental loads, and the magnitude of the load combination is calculated.

[0116] The constraint conditions are determined based on the actual support method of the concrete member, and the initial bearing capacity is calculated. The initial bearing capacity includes the bending bearing capacity, shear bearing capacity, and axial compression bearing capacity.

[0117] The degradation factor is determined based on the degree of degradation of the concrete component, and the comprehensive bearing capacity is calculated to determine the bearing threshold of the concrete component.

[0118] The permanent loads include the self-weight of the concrete structure and the weight of fixedly installed equipment; the variable loads include vehicle traffic loads and pedestrian activity loads during the use of the concrete structure; the environmental loads include wind loads and wave loads under alternating fresh and salt water conditions; the load combination magnitude S is calculated using the partial factor method. d Calculation;

[0119] As a specific implementation, the permanent load is multiplied by a partial factor of 1.3, the variable load by a partial factor of 1.5, and the environmental load by a partial factor of 1.1. Then, the various combinations of loads are added together to obtain the design load effect value, which serves as a key indicator for subsequent bearing capacity verification.

[0120] Analyze the actual support conditions at both ends of the concrete member, including fixed, hinged, elastically constrained, free ends, and lateral support conditions; determine the boundary conditions for the type of concrete member, including whether the bending member is simply supported, continuous, or cantilevered; determine the calculation length for the compression member; and consider how the constraint conditions of the shear member affect the calculation of the shear span ratio.

[0121] The shear span ratio is A = a1 / h0, where a1 is the distance from the concentrated force to the support and h0 is the effective height;

[0122] The bending bearing capacity is: Where a2 represents the correlation coefficient, f c The axial compressive strength of the concrete member is represented by b, the cross-sectional width of the concrete member is represented by h1, and the height of the compression zone of the concrete member is represented by f. y The value of As represents the tensile strength of the steel reinforcement, and As represents the cross-sectional area of ​​the concrete member. The correlation coefficient a2 is obtained through experiments, which include a series of concrete members with different degrees of damage. For each member, its damage index and mechanical properties are measured, and the data are fitted to obtain the correlation coefficient, which is an empirical calibration parameter for correcting the deviation between the theoretical and measured bearing capacity.

[0123] The shear bearing capacity is Among them, a cv f represents the shear capacity coefficient of a sloping concrete structure. t f represents the axial tensile strength of a concrete structure. yv A represents the tensile strength of the stirrups. sv J represents the total cross-sectional area of ​​all parts of the stirrups arranged in the same section; J represents the stirrup spacing; the shear bearing capacity coefficient a of the inclined section concrete structure cv The values ​​are obtained experimentally. Shear loading is applied to the components on a testing machine until the oblique section fails, and the actual shear bearing capacity limit of each component is recorded. The experimental results are compared with the values ​​calculated by the theoretical formula, and regression analysis is used to correct the theoretical formula, thereby determining the shear bearing capacity coefficient of the oblique section concrete structure, which is an empirical coefficient reflecting the contribution of the concrete itself to the shear bearing capacity.

[0124] The axial compressive bearing capacity N u0 =0.9*d*(f c *As+f y '*As'), where d represents the stability coefficient, f cf represents the axial compressive strength of a concrete member, where As represents the cross-sectional area of ​​the concrete member; y ' represents the design value of the compressive strength of the steel reinforcement, and As' represents the cross-sectional area of ​​the compressed steel reinforcement; the stability coefficient d is determined experimentally by conducting axial compression tests on the component, measuring its actual failure load, and fitting the relationship curve to obtain the stability coefficient, which is a dimensionless coefficient reflecting the reduction effect of the axial compressive bearing capacity of the concrete component;

[0125] The theoretical flexural capacity, shear capacity, and axial compressive capacity of a concrete member in its intact state are obtained, which are the initial bearing capacities of the concrete member under various states.

[0126] Based on the influence pattern of deterioration degree on bearing capacity, the bearing capacity deterioration factor is determined, and the flexural bearing capacity deterioration factor K is determined. dM Loss of steel reinforcement section K s , Strength loss of steel reinforcement K y Adhesion loss K b Strength loss K in the compression zone of concrete c Cross-sectional damage K sec Impact; denoted as K dM ≈K s *K y *K b *K sec or K dM ≈min(K s *K y *K b ,K c *K sec ); Reinforcement section loss K s , Strength loss of steel reinforcement K y Adhesion loss K b Strength loss K in the compression zone of concrete c Cross-sectional damage K sec By obtaining the measured values ​​through on-site non-destructive testing and comparing them with the original design values ​​or standard values, a scaling factor between 0 and 1 is calculated to obtain the scaling factor for the deterioration of the flexural bearing capacity of concrete members.

[0127] Shear bearing capacity degradation factor K dV Loss of section K due to stirrups sL Strength loss of stirrups K yL loss of concrete shear capacity K j Cross-sectional damage K sec The influence is denoted as K. dV ≈K sL *K yL *K sec *K j Stirrup section loss K sL Strength loss of stirrups KyL loss of concrete shear capacity K j Cross-sectional damage K sec By obtaining the measured values ​​through on-site non-destructive testing and comparing them with the original design values, a scaling factor between 0 and 1 is calculated to obtain the scaling factor for the deterioration of the shear bearing capacity of concrete members.

[0128] Axial compression bearing capacity degradation factor K dN Mainly affected by concrete strength loss K c Longitudinal reinforcement section loss K sz Longitudinal reinforcement strength loss K yz Cross-sectional damage K sec The influence is denoted as K. dN ≈K c *K sz *K yz *K sec Concrete strength loss K c Longitudinal reinforcement section loss K sz Longitudinal reinforcement strength loss K yz Cross-sectional damage K sec The measured values ​​were obtained through on-site non-destructive testing. The measured values ​​were compared with the original design values, and a scaling factor between 0 and 1 was calculated to obtain the scaling factor for the degradation of the axial compressive bearing capacity of concrete components.

[0129] Multiplying the initial bearing capacity by the corresponding degradation factor yields the comprehensive bearing capacity of the component after considering degradation, which is denoted as C. Z =A loss (t)·(M u0 *K dM +V u0 *K dV +N u0 *K dN ), where C Z Indicates comprehensive bearing capacity, A loss (t) represents the degree of deterioration of the concrete component, M u0 K represents the bending capacity. dM V represents the degradation factor corresponding to the flexural bearing capacity. u0 K represents the shear bearing capacity. dV N represents the degradation factor corresponding to the shear bearing capacity. u0 K represents the axial compression bearing capacity. dN This indicates the degradation factor corresponding to the axial compression bearing capacity;

[0130] Based on the structural importance coefficient γ0 and the bearing capacity test coefficient γ u The core inequality for verifying the bearing capacity threshold of concrete members is: γ0*S d ≤(1 / γ u )*CZ , of which S d Indicates the magnitude of the load combination, C Z This represents the overall bearing capacity of a concrete member; the bearing capacity test coefficient γ. u The bearing capacity test coefficient is determined based on different evaluation standards, such as reliability assessment, seismic assessment, and different component stress states, such as bending, shear, and compression. The structural importance coefficient γ0 and the bearing capacity test coefficient γ are also set accordingly. u The safety factor, which characterizes the structural safety level and performance margin, is obtained by consulting the current national design codes and appraisal standards.

[0131] The load-bearing threshold of a concrete member represents the maximum load effect that the member can withstand after considering safety reserves; the load-bearing threshold is expressed as L = C. Z / γ u ;

[0132] Based on the bearing capacity threshold, taking the current age of the concrete member as a starting point, and using the bearing capacity degradation function and failure criteria, the total lifespan of the concrete member is predicted, resulting in the remaining service life of the current concrete member in an alternating fresh and saltwater environment, including:

[0133] Obtain the current service age t0 and current comprehensive bearing capacity C of the concrete member. Z (t), considering the chloride ion concentration C in the environment where the concrete component is located. l (t), alternating frequency f e and the current ambient temperature T N (t);

[0134] A bearing capacity degradation function under alternating fresh and salt water conditions is constructed, and the future bearing capacity is predicted step by step with a certain time step, starting from the current service age t0.

[0135] In each step, the chloride ion concentration, the degree of deterioration of the concrete component, and the bearing capacity are updated according to the bearing capacity degradation function. The time when the bearing capacity first falls below the bearing threshold L is recorded, which is the remaining service life t. s ;

[0136] The bearing capacity degradation function is expressed as:

[0137]

[0138] Among them, C Z (t) represents the current comprehensive bearing capacity, C0 represents the bearing capacity of the component in its initial state, ∈ represents the weighting coefficient of the influence of erosion on the bearing capacity, and A s (t) represents the damaged cross-sectional area of ​​the concrete member, A s0Let A represent the initial cross-sectional area, ζ represent the nonlinear exponent of erosion influence, and η represent the weighting coefficient of the impact of concrete deterioration on bearing capacity; loss (t) represents the degree of deterioration of the concrete member, A0 represents the initial degree of deterioration, θ represents the nonlinear exponent of the effect of concrete deterioration, and ψ represents the temperature influence coefficient. N (t) represents the current ambient temperature, T C Indicates the reference temperature, η h The following coefficients represent the influence of environmental humidity: ∈ (weighting coefficient for the influence of erosion on bearing capacity), ζ (nonlinear exponent of the influence of erosion), η (weighting coefficient for the influence of concrete deterioration on bearing capacity), θ (nonlinear exponent of the influence of concrete deterioration), ψ (temperature influence coefficient), and η (environmental humidity influence coefficient). h Empirical parameters for adjusting the influence of different degradation factors on bearing capacity are obtained through analysis of experimental and monitoring data. These parameters include the weighting coefficient ∈ (erosion's influence on bearing capacity), the nonlinear exponent ζ (erosion's influence), the weighting coefficient η (concrete deterioration's influence on bearing capacity), and the nonlinear exponent θ (concrete deterioration's influence). These parameters are obtained by precisely controlling temperature, humidity, concentration of corrosive media, and their changing cycles in a laboratory environment simulation chamber. Multiple concrete components are placed in the chamber for accelerated degradation, and the components are periodically removed for destructive testing. The weighting coefficients and nonlinear exponents that best match the predicted curves with the experimental data curves are calculated through inversion. Temperature and humidity sensors are installed on the concrete components to continuously record the temperature and humidity changes of the microenvironment over time, and the temperature influence coefficient ψ and the environmental humidity influence coefficient η are calculated. h .

[0139] Step five includes: designing a data table structure using a relational database (such as MySQL or SQL Server) or a non-relational database (such as MongoDB), including a sample information table, a test data table, and an environmental data table; organizing and storing various data collected during the testing of concrete structural components in a unified format and standard into the database, regularly backing up the database, and taking security measures to prevent data leakage and damage; statistically analyzing the data in the database to calculate the mean, standard deviation, coefficient of variation, etc. of various indicators, understanding the distribution pattern and dispersion of the data, analyzing the correlation between different factors, including chloride ion concentration, ambient temperature, humidity, and concrete damage and deterioration indicators, and determining the main influencing factors; and obtaining data on the load-bearing capacity of concrete components under the coupled effects of multiple factors such as alternating fresh and salt water conditions, chloride ion erosion, and temperature changes.

[0140] As a specific example, long-term monitoring points are set on concrete structural members to collect data periodically, monitor their performance changes, and update the monitoring data to the database in a timely manner to form complete time series data. Based on the monitoring data, the performance of concrete structural members is periodically evaluated using the established bearing capacity degradation function to predict their remaining service life.

[0141] A data sharing platform based on Web technology has been developed to enable online data query, download, and sharing. Different levels of user permissions have been set to ensure data security and legal use. Data visualization functions are also provided to help users intuitively understand the data and the current status of concrete components, providing a basis for structural maintenance and repair.

[0142] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0143] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. Any of the claimed embodiments can be used in any combination.

Claims

1. A non-destructive testing method for exposed concrete structural members, characterized in that, Includes the following steps: Step 1: Embed sensors in the concrete components for detection to acquire real-time data on the concrete components under alternating fresh and salt water conditions; Based on Fick's second law, the diffusion coefficient of chloride ions in concrete components was fitted, and a chloride ion diffusion characteristic set of concrete components under alternating fresh and salt water conditions was generated. Step 2: Using an ultrasonic detector, ultrasonic pulse signals of a specific frequency are emitted to the concrete component. Based on the elastic wave theory of concrete, the characteristics of the relative dynamic elastic modulus change of concrete under the alternating action of fresh and salt water are calculated. Step 3: Using a high-resolution optical camera system and a 3D laser scanner, the surface of the concrete component is periodically scanned and images are acquired. Multimodal algorithms are used to identify and quantify the area, depth, and distribution characteristics of the eroded areas on the surface of the concrete component. Step 4: Based on the analysis results of the detection data of chloride ion diffusion characteristic set, relative dynamic elastic modulus and distribution characteristics, establish a deterioration model of concrete components under alternating fresh and salt water conditions. Combine the actual stress conditions and boundary conditions of concrete components, construct a load-bearing capacity calculation model of reinforced concrete components under alternating fresh and salt water conditions, and predict the remaining service life of concrete components in the alternating fresh and salt water environment according to the set failure criteria. Step 5: Systematically integrate and store all types of test data obtained throughout the exposure test, establish a database, and provide data references on the damage and deterioration mechanisms and performance evolution laws of concrete structural components under alternating fresh and salt water conditions.

2. The non-destructive testing method for exposed concrete structural members according to claim 1, characterized in that, Step one includes establishing a chloride ion diffusion model in concrete components based on Fick's second law, determining boundary conditions and initial conditions; fitting the obtained chloride ion concentration under alternating fresh and salt water conditions to the diffusion model to obtain the diffusion coefficient, and generating a chloride ion diffusion feature set of concrete components under alternating fresh and salt water conditions.

3. The non-destructive testing method for exposed concrete structural members according to claim 1, characterized in that, Step three includes: Bilateral filtering is used to remove noise from point cloud data and image data. A segmentation algorithm is used to distinguish multiple independent erosion blocks in the image, identify and label the erosion areas, and clarify the boundaries and range of each erosion block. Analyze the curvature changes of point cloud data to detect irregular surfaces, and use clustering algorithms to group points with similar curvature characteristics together to distinguish eroded areas from normal areas; Feature matching is used to find the corresponding feature points in the point cloud data of laser scanning and the image data acquired by the optical camera system. The ICP algorithm is used to optimize the matching accuracy of the feature points and align the point cloud data and image data to the same coordinate system. A multimodal algorithm was used to quantify the erosion area and obtain the distribution characteristics of the erosion area of ​​concrete components under alternating fresh and salt water conditions, including concrete morphological features.

4. The non-destructive testing method for exposed concrete structural members according to claim 3, characterized in that, A multimodal algorithm is used to quantify the eroded area, obtaining the area, depth, and distribution characteristics including concrete morphology features of the eroded area of ​​the concrete component. The eroded area of ​​the concrete component is projected onto a two-dimensional plane, and the area of ​​the convex hull is calculated to obtain the area of ​​the eroded area. The local reference plane method is used to model a local reference plane for the intact area around each eroded block to obtain the maximum depth and average depth, thereby quantifying the depth characteristics of the eroded area. The K-function is used for nearest neighbor analysis to assess the spatial randomness or clustering of erosion points. The fractal dimension is calculated to quantify the complexity of the erosion boundary. The minimum bounding rectangle is used to analyze the aspect ratio orientation of the erosion shape and obtain the distribution characteristics that include the morphological features of the concrete.

5. The non-destructive testing method for exposed concrete structural members according to claim 1, characterized in that, Step four includes: Based on the chloride ion diffusion feature set and the distribution characteristics of concrete components, the variation law of chloride ion concentration on the surface of concrete components with time is calculated using numerical methods to obtain the starting time of erosion of concrete components. Using the relative dynamic elastic modulus data obtained from the test, combined with the characteristics of alternating fresh and salt water conditions and the start time, the erosion rate and loss cross-sectional area of ​​the concrete component are calculated, and a deterioration model of the concrete component is established. Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key capacity of the concrete member to resist failure is calculated, and the member deterioration is incorporated into the bearing capacity calculation model. Based on the bearing capacity judgment threshold, the current age of the concrete component is taken as the starting point. Based on the bearing capacity degradation function and failure judgment criteria, the total life of the concrete component is predicted, and the remaining service life of the current concrete component in the alternating fresh and salt water environment is obtained.

6. The non-destructive testing method for exposed concrete structural members according to claim 5, characterized in that, Using the relative dynamic elastic modulus data obtained from the tests, combined with the characteristics of alternating fresh and salt water conditions and the start time, the erosion rate and loss cross-sectional area of ​​the concrete members are calculated, and a deterioration model of the concrete members is established, including: Based on the relative dynamic elastic modulus, erosion rate, and lost cross-sectional area, and considering the influence of the freshwater-salt water alternation period T on the deterioration process, the deterioration model of concrete members is determined as follows: A loss (t) represents the degree of deterioration of the concrete member, α represents the coefficient related to the geometry and stress characteristics of the concrete member, A0 represents the initial degree of deterioration, and i c E0(t) represents the erosion rate at time t, E0(t) represents the initial relative dynamic elastic modulus, λ represents the attenuation coefficient, T represents the period of alternation between fresh and salt water, and k represents a constant related to the properties of concrete building materials and environmental concentration.

7. The non-destructive testing method for exposed concrete structural members according to claim 5, characterized in that, Based on the actual load type and magnitude and boundary conditions borne by the concrete member, the key resistance capacity of the concrete member to failure is calculated, and member deterioration is incorporated into the bearing capacity calculation model, including: The types of loads actually borne by concrete members are divided into member loads, variable loads, and environmental loads, and the magnitude of the load combination is calculated. The constraint conditions are determined based on the actual support method of the concrete member, and the initial bearing capacity is calculated. The initial bearing capacity includes the bending bearing capacity, shear bearing capacity, and axial compression bearing capacity. Based on the degree of deterioration of the concrete component, the deterioration factor is determined, the comprehensive bearing capacity is calculated, and the bearing threshold of the concrete component is determined.

8. The non-destructive testing method for exposed concrete structural members according to claim 7, characterized in that, The comprehensive bearing capacity is expressed as C. Z =A loss (t)·(M u0 *K dM +V u0 *K dV +N u0 *K dN ), where C Z Indicates comprehensive bearing capacity, A loss (t) represents the degree of deterioration of the concrete component, M u0 K represents the bending capacity. dM V represents the degradation factor corresponding to the flexural bearing capacity. u0 K represents the shear bearing capacity. dV N represents the degradation factor corresponding to the shear bearing capacity. u0 K represents the axial compression bearing capacity. dN This represents the degradation factor corresponding to the axial compression bearing capacity.

9. The non-destructive testing method for exposed concrete structural members according to claim 5, characterized in that, Based on the bearing capacity threshold, taking the current age of the concrete member as a starting point, and using the bearing capacity degradation function and failure criteria, the total lifespan of the concrete member is predicted, resulting in the remaining service life of the current concrete member in an alternating fresh and saltwater environment, including: Obtain the current service age t0 and current comprehensive bearing capacity C of the concrete member. Z (t), considering the chloride ion concentration C in the environment where the concrete component is located. l (t), alternating frequency f e and the current ambient temperature T N (t); A bearing capacity degradation function under alternating fresh and salt water conditions is constructed, and the future bearing capacity is predicted step by step with a certain time step, starting from the current service age t0. In each step, the chloride ion concentration, the degree of deterioration of the concrete component, and the bearing capacity are updated according to the bearing capacity degradation function. The time when the bearing capacity first falls below the bearing threshold L is recorded, which is the remaining service life t. s .

10. The non-destructive testing method for exposed concrete structural members according to claim 9, characterized in that, The bearing capacity degradation function is expressed as: Among them, C Z (t) represents the current comprehensive bearing capacity, C0 represents the bearing capacity of the component in its initial state, ∈ represents the weighting coefficient of the influence of erosion on the bearing capacity, and A s (t) represents the damaged cross-sectional area of ​​the concrete member, A s0 Let A represent the initial cross-sectional area, ζ represent the nonlinear exponent of erosion influence, and η represent the weighting coefficient of the impact of concrete deterioration on bearing capacity; loss (t) represents the degree of deterioration of the concrete member, A0 represents the initial degree of deterioration, θ represents the nonlinear exponent of the effect of concrete deterioration, and ψ represents the temperature influence coefficient. N (t) represents the current ambient temperature, T C Indicates the reference temperature, η h This indicates the influence coefficient of environmental humidity.

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