Method for predicting erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness

Abstract

This invention discloses a method for predicting erosion and damage to concrete structures under dual-cycle conditions of wet / dry and saline-salt environments. Based on the area of ​​the concrete structure to be predicted, combined with structural design drawings and on-site survey data, an unstructured mesh is generated. The unstructured mesh is then divided into regions, and an environmental load model is constructed by coupling a wet / dry cycle model and a saline-salt migration model. Various high-precision sensors are installed to acquire real-time on-site monitoring data of key parts of the concrete structure, optimizing the parameters of the environmental load model to predict erosion and damage to the concrete structure. The risk of erosion and damage to the concrete structure is determined, with different risk levels distinguished by color coding, and preventative maintenance plans are established. This invention accurately recreates the complex geometry of concrete structures, couples wet / dry cycle models and saline-salt migration models, improves the accuracy of the model's description of the erosion and damage process, and intuitively presents the risk levels of different parts of the concrete structure, helping maintenance personnel to rationally plan maintenance schedules.

Description

Technical Field

[0001] This invention relates to the field of concrete technology, and more specifically, to a method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness. Background Technology

[0002] In marine, coastal, and saline soil environments, concrete structures are often subjected to the dual effects of wet-dry cycles and saline-freshwater migration. Wet-dry cycles lead to salt accumulation and volume changes within the concrete, while saline-freshwater migration introduces corrosive media such as chloride ions and sulfates. The interaction between these two factors accelerates the erosion and damage of the concrete. Under these dual-cycle conditions, the physicochemical properties of concrete undergo significant changes. For example, alternating wet and dry conditions promote salt crystallization and dissolution, generating crystallization pressure and leading to the formation and propagation of microcracks within the concrete. Simultaneously, chloride ion penetration triggers steel reinforcement corrosion, and sulfate erosion damages the hydration products of cement paste, reducing the strength and durability of the concrete.

[0003] In existing technologies, many erosion damage prediction models mainly target single environmental factors, such as chloride or sulfate erosion, while neglecting the coupling effect of wet-dry cycles and salinity migration. Most existing prediction methods are based on static experimental data or limited field monitoring data, making it difficult to capture the dynamic performance changes of concrete structures during long-term use and accurately predict the spatiotemporal evolution of erosion damage. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting the erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness, in order to solve the above-mentioned problems existing in the prior art.

[0005] The application is as follows:

[0006] A method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and alkalinity is provided, comprising the following steps:

[0007] Step 1: Based on the concrete structure area to be predicted, combined with the structural design drawings and on-site survey data, determine the boundary of the computational domain and construct a three-dimensional mesh structure model; mark key areas according to exposure conditions, divide the mesh refinement area based on the geometric characteristics of the concrete structure and the environmental interface, discretize the concrete body using tetrahedral elements, and locally refine the mesh in the key areas to generate an unstructured mesh.

[0008] Step 2: Divide the unstructured grid into regions, use the moisture diffusion equation and environmental humidity time series data as boundary conditions to construct a wet-dry cycle model, use the multi-component transport equation to calculate the crystallization and dissolution of ion migration to construct a freshwater migration model, and couple the wet-dry cycle model and the freshwater migration model to construct an environmental load model.

[0009] Step 3: Install various high-precision sensors to acquire real-time on-site monitoring data of key parts of the concrete structure, preprocess the on-site monitoring data; optimize the parameters of the environmental load model based on the preprocessed on-site monitoring data, correct the environmental load model, and predict the erosion and damage of the concrete structure through the concrete structure erosion and damage prediction model.

[0010] Step 4: Integrate historical monitoring data with erosion damage prediction results to set critical thresholds, determine the risk of erosion damage to concrete structures, and use a 3D visualization platform to distinguish areas with different risk levels by color coding. The dynamic changes of risk are displayed in the form of time series animations to establish preventive maintenance plans.

[0011] Based on the concrete structure area to be predicted, combined with structural design drawings and on-site survey data, the boundary of the computational domain is determined, and a three-dimensional mesh structure model is constructed, including:

[0012] Computer-aided design software is used to interpret structural design drawings, converting design information in the drawings into digital information. A 3D laser scanner is used to collect point cloud data, and point cloud registration and noise reduction are performed.

[0013] By comparing and integrating the structural design drawings with the on-site survey data, the horizontal and vertical boundaries are delineated, and the boundaries of the calculation domain are determined.

[0014] Based on the actual shape and size of the concrete structure, geometric models of each component are created using finite element analysis software, and Boolean operations are used to process the connection relationships between components and complex structural areas.

[0015] Based on the preset mesh division rules and parameters, a preliminary three-dimensional mesh structure model of the concrete structure area to be predicted is generated.

[0016] Key areas are marked according to exposure conditions. Based on the geometric features of the concrete structure and the environmental interface, mesh refinement zones are divided. The concrete body is discretized using tetrahedral elements, and local refinement is applied in key areas to generate an unstructured mesh, including:

[0017] The exposure conditions refer to the external environmental characteristics of the concrete structure, including humidity, temperature, salinity, and pH. Key areas are identified and marked according to the exposure conditions.

[0018] The geometric features of the concrete structure include cracks and the distribution of reinforcing bars, and the environmental interface includes the water level fluctuation zone; the geometric analysis algorithm and mesh quality assessment tool of the finite element analysis software are used to divide the mesh into a finer zone;

[0019] The concrete body is discretized using tetrahedral elements. Local densification is performed in key areas according to preset densification parameters, and the mesh density in key areas is automatically adjusted to generate unstructured meshes.

[0020] Step two includes:

[0021] The concrete structure area is divided into extreme environment zone, alternating action zone and continuous erosion zone according to the unstructured mesh size range and the number of densification layers, and the mesh size is dynamically and adaptively adjusted.

[0022] Based on the nonlinear diffusion of temperature and humidity, a moisture diffusion equation is constructed considering the evaporation and diffusion migration capabilities of water molecules, and boundary conditions are set for environmental humidity time series data.

[0023] A multi-component transport equation involving diffusion, convection, crystallization, and dissolution was established to obtain the crystallization and dissolution rates of ion migration, and a saltwater migration model was constructed.

[0024] The parameters obtained from the wet-dry cycle model are introduced into the freshwater migration model to correct the effective diffusion coefficient. The pore water flow velocity is considered to be affected by both humidity and ions, and coupled into an environmental load model.

[0025] Based on the nonlinear diffusion of temperature and humidity, a moisture diffusion equation is constructed considering the evaporation and diffusion migration capabilities of water molecules. Boundary conditions for environmental humidity time-series data are set as follows:

[0026] Based on the nonlinear diffusion of temperature and humidity, and considering the evaporation and diffusion migration capabilities of water molecules, the water diffusion equation is as follows:

[0027]

[0028] in, Indicates the pore water saturation of concrete; This represents the amount of water evaporated per unit volume per unit time; t represents time; T represents the absolute temperature of the concrete. Indicates the ability of water to migrate within concrete; RH indicates the surface humidity of the concrete structure.

[0029]

[0030] in, Represents the baseline diffusion coefficient. This represents the energy threshold required for water migration. Represents the gas constant. The calibration temperature for the diffusion coefficient;

[0031]

[0032] in, Indicates the evaporation coefficient. Indicates the relative humidity of the surface. Indicates balanced humidity;

[0033] The boundary conditions driven by environmental humidity time-series data are:

[0034]

[0035] in, Indicates the average daily humidity. The period of humidity change is represented by t, where t represents time.

[0036] Establishing multi-component transport equations involving diffusion, convection, crystallization, and dissolution; obtaining the crystallization-dissolution rates of ion migration; and constructing a saltwater migration model include:

[0037] The multi-component transport equation is as follows:

[0038]

[0039] in, This represents the concentration of the i-th ion; Indicates the effective diffusion coefficient; Indicates the pore water flow velocity. Indicates the rate of crystallization and dissolution.

[0040] In the brackish water migration model, parameters calculated using the wet-dry cycle model are introduced to correct the effective diffusion coefficient. Considering the combined influence of humidity and ions on pore water flow velocity, the model is coupled into an environmental load model, including:

[0041] The correction to the effective diffusion coefficient is expressed as:

[0042]

[0043] in, Represents the reference diffusion coefficient. This represents the pore water saturation of concrete, imported from the wet-dry cycle model. Indicates activation energy; Represents the gas constant; Indicates the absolute temperature of the concrete; The calibration temperature for the effective diffusion coefficient; This represents the saturation concentration of the j-th ion; This represents the concentration of the j-th ion;

[0044] The pore water flow velocity v is affected by both humidity and ions:

[0045]

[0046] in, Indicates saturated permeability; Represents the osmotic pressure gradient; Indicates the dynamic viscosity of pore water; This indicates the thermal penetration effect induced by the ion concentration gradient; This represents the correction factor.

[0047] Step three includes:

[0048] An optimization problem for predicting the erosion and damage of concrete structures was constructed by combining pre-processed field monitoring data. An optimization algorithm was used to determine the conditions for updating model parameters. The model parameters of the environmental load model were continuously adjusted and optimized through multiple iterations to correct the environmental load model.

[0049] Based on the modified environmental load model, the damage evolution of the concrete structure area to be predicted is predicted, a concrete structure erosion and damage prediction model is constructed to predict the risk of concrete structure erosion and damage, and the risk level is classified.

[0050] The optimization problem for predicting erosion and damage in concrete structures is expressed as:

[0051]

[0052] in, express Monitor data constantly. Indicates the total monitoring time; Indicates the output parameters of the environmental load model ,in This represents the parameters to be optimized in the model. This represents the input parameters in the model; This represents the regularization coefficient.

[0053] Based on the modified environmental load model, the damage evolution of the concrete structure region to be predicted is calculated. A concrete structure erosion failure prediction model is constructed to predict the risk of concrete structure erosion failure, and the risk level is classified, including:

[0054] The output of the modified environmental load model is used as the input of the damage evolution model. Damage variables are defined to describe the generation and expansion of damage in concrete structures, and a damage evolution model is established.

[0055] Numerical simulation using the finite difference method is employed to predict the damage evolution process of concrete structures. Based on the damage evolution model, the degree and distribution of damage are predicted, thereby forecasting the erosion and damage of concrete structures.

[0056] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0057] 1. This invention constructs a three-dimensional mesh structure model based on the concrete structure area to be predicted, combined with structural design drawings and on-site survey data. Based on the geometric characteristics of the concrete structure and the environmental interface, it divides the mesh into a finer mesh area and locally finer meshes in key areas to generate an unstructured mesh. This accurately restores the complex geometry of the concrete structure, providing a high-precision geometric basis for subsequent analysis. Local mesh finer meshes are applied to areas susceptible to erosion or stress concentration to capture changes in physical quantities more precisely and improve simulation accuracy.

[0058] 2. Considering the impact of dual-cycle changes in dry, wet, and saline conditions on the erosion and damage of concrete structures, a dual-cycle model is constructed, consisting of a dry-wet cycle model and a saline migration model. The effective diffusion correction coefficient for the influence of environmental humidity calculated by the dry-wet cycle model is introduced into the saline migration model. The pore water flow velocity is considered to be affected by both humidity and ions, and coupled into an environmental load model. This model closely reflects the physicochemical processes inside concrete under complex environments, improves the accuracy of the model in describing the erosion and damage process, and facilitates comprehensive analysis and prediction.

[0059] 3. Install various high-precision sensors to acquire real-time on-site monitoring data of key parts of the concrete structure. Combine the on-site monitoring data to optimize the parameters of the environmental load model, construct a concrete structure erosion and damage prediction model to predict the erosion and damage of the concrete structure and visualize the risk. Based on accurate model parameters and real-time monitoring data, the constructed concrete structure erosion and damage prediction model can more accurately predict the erosion and damage of the structure, intuitively present the risk level of different parts of the concrete structure, and help maintenance personnel to reasonably arrange maintenance plans. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness provided in this embodiment of the invention. Detailed Implementation

[0061] The present invention will now be described in detail with reference to the accompanying drawings.

[0062] Example 1

[0063] like Figure 1 As shown, this application provides a method for predicting erosion damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness, comprising the following steps:

[0064] Step 1: Based on the concrete structure area to be predicted, combined with the structural design drawings and on-site survey data, determine the boundary of the computational domain and construct a three-dimensional mesh structure model; mark key areas according to exposure conditions, divide the mesh refinement area based on the geometric characteristics of the concrete structure and the environmental interface, discretize the concrete body using tetrahedral elements, and locally refine the mesh in the key areas to generate an unstructured mesh.

[0065] Step 2: Divide the unstructured grid into regions, use the moisture diffusion equation and environmental humidity time series data as boundary conditions to construct a wet-dry cycle model, use the multi-component transport equation to calculate the crystallization and dissolution of ion migration to construct a freshwater migration model, and couple the wet-dry cycle model and the freshwater migration model to construct an environmental load model.

[0066] Step 3: Install various high-precision sensors to acquire real-time on-site monitoring data of key parts of the concrete structure, preprocess the on-site monitoring data; optimize the parameters of the environmental load model based on the preprocessed on-site monitoring data, correct the environmental load model, and predict the erosion and damage of the concrete structure through the concrete structure erosion and damage prediction model.

[0067] Step 4: Integrate historical monitoring data with erosion damage prediction results to set critical thresholds, determine the risk of erosion damage to concrete structures, and use a 3D visualization platform to distinguish areas with different risk levels by color coding. The dynamic changes of risk are displayed in the form of time series animation, and a preventive maintenance plan is established.

[0068] Based on the concrete structure area to be predicted, combined with structural design drawings and on-site survey data, the boundary of the computational domain is determined, and a three-dimensional mesh structure model is constructed, including:

[0069] Computer-aided design software is used to interpret structural design drawings, converting design information in the drawings into digital information. A 3D laser scanner is used to collect point cloud data, and point cloud registration and noise reduction are performed.

[0070] By comparing and integrating the structural design drawings with the on-site survey data, the horizontal and vertical boundaries are delineated, and the boundaries of the calculation domain are determined.

[0071] Based on the actual shape and size of the concrete structure, geometric models of each component are created using finite element analysis software, and Boolean operations are used to process the connection relationships between components and complex structural areas.

[0072] Based on the preset mesh division rules and parameters, a preliminary three-dimensional mesh structure model of the concrete structure area to be predicted is generated.

[0073] The computer-aided design software includes ArchiCAD, PKPM, AutoCAD, etc.; the 3D laser scanner emits a laser beam, receives reflected laser light, measures the distance to the area of ​​the concrete structure to be predicted, and obtains the 3D coordinates of a large number of points to form point cloud data; key points in the point cloud are extracted, feature descriptions are established, and point pairs corresponding to different viewpoints are determined by matching to obtain approximate relative positional relationships, and initial point cloud registration is performed. Based on the initial registration results, iterative nearest point is used to further reduce the error. Through continuous iterative optimization, the registration accuracy reaches a higher level; a Gaussian filter is used to smooth the point cloud data, reduce the impact of noise, and make noisy points gradually move closer to the real point positions.

[0074] The digitized structural design drawings and processed on-site survey point cloud data are imported into the same coordinate system. Data registration techniques are typically used to align the two in space. The design drawings and on-site survey data are compared to identify differences between the actual structure and the design model, including dimensional deviations, shape changes, and component position offsets. Based on the results of the comparative analysis and the actual engineering requirements, horizontal and vertical boundaries are defined. The horizontal boundary is the edge contour of the concrete structure in the horizontal direction, while the vertical boundary is the start and end position of the concrete structure in the height direction. The horizontal and vertical boundaries together determine the computational domain range used for subsequent finite element analysis.

[0075] The finite element analysis software includes ANSYS and ABAQUS, which are not specifically limited here. When using Boolean operations to process the connection relationship between components and complex structural regions, as a specific example, when two components intersect, their overlapping part is determined by Boolean intersection operation to accurately simulate the connection relationship between them. For some complex shaped structural regions, Boolean union or difference operations are used to create a geometric shape that conforms to reality.

[0076] The preset mesh generation rules and parameters include mesh cell type, cell size, and mesh density;

[0077] Key areas are marked according to exposure conditions. Based on the geometric features of the concrete structure and the environmental interface, mesh refinement zones are divided. The concrete body is discretized using tetrahedral elements, and local refinement is applied in key areas to generate an unstructured mesh, including:

[0078] The exposure conditions refer to the external environmental characteristics of the concrete structure, including humidity, temperature, salinity, and pH. Key areas are identified and marked according to the exposure conditions.

[0079] The geometric features of the concrete structure include cracks and the distribution of reinforcing bars; the environmental interface includes the water level fluctuation zone; the geometric analysis algorithm and mesh quality assessment tool of the finite element analysis software are used to divide the mesh into a finer zone;

[0080] The concrete body is discretized using tetrahedral elements. Local densification is performed in key areas according to preset densification parameters, and the mesh density in key areas is automatically adjusted to generate unstructured meshes.

[0081] Specifically, different exposure conditions lead to different forms and degrees of erosion and damage to concrete structures; for example, in high humidity areas, concrete structures are more susceptible to moisture erosion; in high salinity areas, concrete structures are more prone to salt crystallization damage; it is necessary to mark key areas based on these exposure conditions.

[0082] The tetrahedral element has four vertices, and each node has three degrees of freedom, namely displacement in the X, Y, and Z directions. The shape function is expressed as:

[0083]

[0084] Where i = 1, 2, 3, 4; Represents local coordinates; , , , This represents the coefficients determined by interpolation conditions; the shape function has a value of 1 at the vertices and a value of 0 at the other vertices.

[0085] The preset encryption parameters include: unit size, encryption range, encryption level, curvature adaptability, environmental factor sensitivity and / or user-defined parameters, which are not specifically limited here;

[0086] Step two includes:

[0087] The concrete structure area is divided into extreme environment zone, alternating action zone and continuous erosion zone according to the unstructured mesh size range and the number of densification layers, and the mesh size is dynamically and adaptively adjusted.

[0088] Based on the nonlinear diffusion of temperature and humidity, a moisture diffusion equation is constructed considering the evaporation and diffusion migration capabilities of water molecules, and boundary conditions are set for environmental humidity time series data.

[0089] A multi-component transport equation involving diffusion, convection, crystallization, and dissolution was established to obtain the crystallization and dissolution rates of ion migration, and a saltwater migration model was constructed.

[0090] The parameters obtained from the wet-dry cycle model are introduced into the freshwater migration model to correct the effective diffusion coefficient. The pore water flow velocity is considered to be affected by both humidity and ions, and coupled into an environmental load model.

[0091] The extreme environment zone has a grid size of 1.0-2.0 mm, a density layer of 5, and a control index of salt spray deposition rate ≥0.15 mg / cm² / day; the alternating action zone has a grid size of 2.0-3.0 mm, a density layer of 3, and a control index of daily wet-dry cycle count ≥2; the continuous erosion zone has a grid size of 3.0-5.0 mm, a density layer of 1, and a control index of flow velocity ≥0.3 m / s.

[0092] The formula for the dynamic adaptive adjustment of the grid size is:

[0093]

[0094] in, This indicates that the grid size is adjusted adaptively. Indicates the basic mesh size; F represents the overall damage quantification.

[0095] A wet-dry cycle model is constructed using the moisture diffusion equation and ambient humidity time-series data as boundary conditions, including:

[0096] Based on the nonlinear diffusion of temperature and humidity, and considering the evaporation and diffusion migration capabilities of water molecules, the water diffusion equation is as follows:

[0097]

[0098] in, Indicates the pore water saturation of concrete; This represents the amount of water evaporated per unit volume per unit time; t represents time; T represents the absolute temperature of the concrete. Indicates the ability of water to migrate within concrete; RH indicates the surface humidity of the concrete structure.

[0099]

[0100] in, Represents the baseline diffusion coefficient. This represents the energy threshold required for water migration. Represents the gas constant. The calibration temperature for the diffusion coefficient;

[0101]

[0102] in, Indicates the evaporation coefficient. Indicates the relative humidity of the surface. Indicates balanced humidity;

[0103] The boundary conditions driven by environmental humidity time-series data are:

[0104]

[0105] in, Indicates the average daily humidity. The period representing the humidity change, where t represents time;

[0106] The correction condition for considering the impact of rainfall is as follows:

[0107]

[0108] in, This indicates the corrected humidity level. Rainfall per unit time indicates rainfall intensity;

[0109] Establishing multi-component transport equations involving diffusion, convection, crystallization, and dissolution; obtaining the crystallization-dissolution rates of ion migration; and constructing a saltwater migration model include:

[0110] The multi-component transport equation is as follows:

[0111]

[0112] in, This represents the concentration of the i-th ion; Indicates the effective diffusion coefficient; Indicates the pore water flow velocity. Indicates crystallization and dissolution rates;

[0113] The crystallization dissolution rate for obtaining ion migration includes:

[0114] The crystallization rate due to ion migration is:

[0115]

[0116] in, =max( ,0); This represents the saturation concentration of the i-th ion; This represents the concentration of the i-th ion; The crystallization rate constant of the i-th ion;

[0117] The dissolution rate of ion migration is:

[0118]

[0119] in, =max( ,0); This represents the saturation concentration of the i-th ion; This represents the concentration of the i-th ion; The dissolution rate constant of the i-th ion;

[0120] In the brackish water migration model, parameters calculated using the wet-dry cycle model are introduced to correct the effective diffusion coefficient. Considering the combined influence of humidity and ions on pore water flow velocity, the model is coupled into an environmental load model, including:

[0121] The correction to the effective diffusion coefficient is expressed as:

[0122]

[0123] in, Represents the reference diffusion coefficient. This represents the pore water saturation of concrete, imported from the wet-dry cycle model. Indicates activation energy; Represents the gas constant; Indicates the absolute temperature of the concrete; The calibration temperature for the effective diffusion coefficient; This represents the saturation concentration of the j-th ion; This represents the concentration of the j-th ion;

[0124] The pore water flow velocity v is affected by both humidity and ions:

[0125]

[0126] in, Indicates saturated permeability; Represents the osmotic pressure gradient; Indicates the dynamic viscosity of pore water; This indicates the thermal penetration effect induced by the ion concentration gradient; Indicates the correction factor;

[0127] The coupling with the environmental load model is represented as follows:

[0128]

[0129] Step three includes:

[0130] An optimization problem for predicting the erosion and damage of concrete structures was constructed by combining pre-processed field monitoring data. An optimization algorithm was used to determine the conditions for updating model parameters. The model parameters of the environmental load model were continuously adjusted and optimized through multiple iterations to correct the environmental load model.

[0131] Based on the modified environmental load model, the damage evolution of the concrete structure area to be predicted is predicted, a concrete structure erosion and damage prediction model is constructed to predict the risk of concrete structure erosion and damage, and the risk level is classified.

[0132] Preprocessing the field monitoring data includes: data cleaning to remove abnormal data points and noise interference. Statistical analysis methods, filtering algorithms, or machine learning algorithms can be used to identify and remove abnormal data, without specific limitations; data normalization to convert data with different dimensions and ranges to the same numerical range; and time series alignment to ensure consistency of various sensor data in the time dimension.

[0133] The optimization problem for predicting erosion and damage in concrete structures is expressed as:

[0134]

[0135] in, express Monitor data in real time; Indicates the total monitoring time; Indicates the output parameters of the environmental load model ,in This represents the parameters to be optimized in the model. This represents the input parameters in the model; Represents the regularization coefficient;

[0136] The optimization algorithm mentioned is stochastic gradient descent, particle swarm optimization, genetic algorithm, etc., and no specific limitation is made here;

[0137] As a specific implementation, when using a genetic algorithm to optimize model parameters, each individual in the genetic algorithm represents a set of parameters of the environmental load model; first, a population containing multiple individuals is initialized, and the parameter values ​​of these individuals are randomly generated within the constraint conditions.

[0138] The determination of parameter update conditions can be achieved through a fitness function; the fitness function is related to the optimization problem, and the higher the fitness, the better the individual (parameter combination) meets the prediction requirements; for example, fitness can be defined as... ;in, It is a very small positive number; the smaller the error E, the higher the fitness.

[0139] In the iterative process of the genetic algorithm, individuals are continuously updated through operations such as selection, crossover, and mutation. Selection selects individuals for reproduction based on fitness, with individuals having a higher probability of being selected. Crossover swaps some parameters between two individuals to generate new individuals. Mutation randomly changes some parameters of individuals to increase population diversity. The environmental load model parameters are optimized through multiple iterations. After each iteration, individuals are sorted according to fitness, and individuals with higher fitness are retained as part of the new generation. Simultaneously, new individuals are generated through crossover and mutation operations to supplement the new generation. This process is repeated until a stopping condition is met, such as reaching a preset maximum number of iterations or the error E falling below a certain threshold. For example, the maximum number of iterations is set to 1000, or iteration stops when the error E is less than 0.01. After multiple iterations, the parameters of the optimal individual obtained are the corrected environmental load model parameters, thus enabling more accurate prediction of the erosion and damage of concrete structures.

[0140] Based on the modified environmental load model, the damage evolution of the concrete structure region to be predicted is calculated. A concrete structure erosion failure prediction model is constructed to predict the risk of concrete structure erosion failure, and the risk level is classified, including:

[0141] The output of the modified environmental load model is used as the input of the damage evolution model. Damage variables are defined to describe the generation and expansion of damage in concrete structures, and a damage evolution model is established.

[0142] Numerical simulation using the finite difference method is employed to predict the damage evolution process of concrete structures. Based on the damage evolution model, the degree and distribution of damage are predicted, thereby forecasting the erosion and damage of concrete structures.

[0143] Specifically, a functional relationship is constructed between environmental load variables and damage variables, where damage variable D = f( The damage variable D ranges from 0 to 1, where 0 represents no damage and 1 represents complete damage.

[0144] Based on the damage mechanism of concrete structures under environmental loads, a damage evolution model is established, which is expressed as: ,in, This is the damage rate coefficient, which is related to environmental load variables. Indicates the time of damage evolution;

[0145] The finite difference method discretizes the time domain and uses explicit time integration to solve the damage evolution model, with a time step of [value missing]. Then the update formula for the damage variable is: ;in, This represents the damage variable at the nth time step.

[0146] Within each time step, the damage evolution model is solved to obtain the distribution of damage variables, and the process is iterated until a set model convergence threshold is reached; the convergence threshold is determined by the fact that the change in damage variables between two adjacent time steps is less than a certain threshold.

[0147] Step four includes:

[0148] A critical threshold for the risk of erosion and damage to concrete structures is set. Based on this threshold, the erosion and damage risk level of different parts of the concrete structure is determined, which can be divided into three levels: low risk, medium risk, and high risk. More specifically, D < 0.5 is classified as low risk; 0.5 ≤ D < 0.8 is classified as medium risk; and D ≥ 0.8 is classified as high risk. Areas of different risk levels are distinguished by color coding, for example, low-risk areas are represented by green, medium-risk areas by yellow, and high-risk areas by red. Based on the risk level assessment results, preventative maintenance strategies are developed. High-risk areas are prioritized for maintenance, with measures such as reinforcement, repair, and surface treatment. Medium-risk areas are monitored regularly, and maintenance plans are adjusted promptly according to the risk development. Low-risk areas are subject to routine maintenance.

[0149] 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.

[0150] 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. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Claims

1. A method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness, characterized in that... Includes the following steps: Step 1: Based on the concrete structure area to be predicted, combined with the structural design drawings and on-site survey data, determine the boundary of the computational domain and construct a three-dimensional mesh structure model; mark key areas according to exposure conditions, divide the mesh refinement area based on the geometric characteristics of the concrete structure and the environmental interface, discretize the concrete body using tetrahedral elements, and locally refine the mesh in the key areas to generate an unstructured mesh. Step 2: Divide the unstructured grid into regions, use the moisture diffusion equation and environmental humidity time series data as boundary conditions to construct a wet-dry cycle model, use the multi-component transport equation to calculate the crystallization and dissolution of ion migration to construct a freshwater migration model, and couple the wet-dry cycle model and the freshwater migration model to construct an environmental load model. Step two includes: dividing the concrete structure area into extreme environment zone, alternating action zone and continuous erosion zone according to the unstructured mesh size range and the number of densification layers, and performing dynamic adaptive adjustment of mesh size; Based on the nonlinear diffusion of temperature and humidity, a moisture diffusion equation is constructed considering the evaporation and diffusion migration capabilities of water molecules. Boundary conditions for environmental humidity time-series data are set, including: Based on the nonlinear diffusion of temperature and humidity, and considering the evaporation and diffusion migration capabilities of water molecules, the water diffusion equation is as follows: in, Indicates the pore water saturation of concrete; This represents the amount of water evaporated per unit volume per unit time; t represents time; T represents the absolute temperature of the concrete. Indicates the ability of water to migrate within concrete; RH indicates the surface humidity of the concrete structure. in, Represents the baseline diffusion coefficient. R represents the energy threshold required for moisture migration, and R represents the gas constant. The calibration temperature for the diffusion coefficient; in, Indicates the evaporation coefficient. Indicates the relative humidity of the surface. Indicates balanced humidity; The boundary conditions driven by environmental humidity time-series data are: in, Indicates the average daily humidity. The period representing the humidity change, where t represents time; A multi-component transport equation involving diffusion, convection, crystallization, and dissolution was established to obtain the crystallization and dissolution rates of ion migration, and a saltwater migration model was constructed. The parameters obtained from the wet-dry cycle model are introduced into the freshwater migration model to correct the effective diffusion coefficient. The pore water flow velocity is considered to be affected by both humidity and ions, and coupled into an environmental load model. Step 3: Install various high-precision sensors to acquire real-time on-site monitoring data of key parts of the concrete structure, preprocess the on-site monitoring data; optimize the parameters of the environmental load model based on the preprocessed on-site monitoring data, correct the environmental load model, and predict the erosion and damage of the concrete structure through the concrete structure erosion and damage prediction model. Step 4: Integrate historical monitoring data with erosion damage prediction results to set critical thresholds, determine the risk of erosion damage to concrete structures, and use a 3D visualization platform to distinguish areas with different risk levels by color coding. The dynamic changes of risk are displayed in the form of time series animations to establish preventive maintenance plans.

2. The method for predicting erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 1, is characterized in that, Based on the concrete structure area to be predicted, combined with structural design drawings and on-site survey data, the boundary of the computational domain is determined, and a three-dimensional mesh structure model is constructed, including: Computer-aided design software is used to interpret structural design drawings, converting design information in the drawings into digital information. A 3D laser scanner is used to collect point cloud data, and point cloud registration and noise reduction are performed. By comparing and integrating the structural design drawings with the on-site survey data, the horizontal and vertical boundaries are delineated, and the boundaries of the calculation domain are determined. Based on the actual shape and size of the concrete structure, geometric models of each component are created using finite element analysis software, and Boolean operations are used to process the connection relationships between components and complex structural areas. Based on the preset mesh division rules and parameters, a preliminary three-dimensional mesh structure model of the concrete structure area to be predicted is generated.

3. The method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 1, is characterized in that, Key areas are marked according to exposure conditions. Based on the geometric features of the concrete structure and the environmental interface, mesh refinement zones are divided. The concrete body is discretized using tetrahedral elements, and local refinement is applied in key areas to generate an unstructured mesh, including: The exposure conditions refer to the external environmental characteristics of the concrete structure, including humidity, temperature, salinity, and pH. Key areas are identified and marked according to the exposure conditions. The geometric features of the concrete structure include cracks and the distribution of reinforcing bars, and the environmental interface includes the water level fluctuation zone; the geometric analysis algorithm and mesh quality assessment tool of the finite element analysis software are used to divide the mesh into a finer zone; The concrete body is discretized using tetrahedral elements. Local densification is performed in key areas according to preset densification parameters, and the mesh density in key areas is automatically adjusted to generate unstructured meshes.

4. The method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 1, characterized in that, Establishing multi-component transport equations involving diffusion, convection, crystallization, and dissolution; obtaining the crystallization-dissolution rates of ion migration; and constructing a saltwater migration model include: The multi-component transport equation is as follows: in, This represents the concentration of the i-th ion; Indicates the effective diffusion coefficient; v represents the pore water velocity. Indicates the rate of crystallization and dissolution.

5. The method for predicting erosion and damage to concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 1, characterized in that, In the brackish water migration model, parameters calculated using the wet-dry cycle model are introduced to correct the effective diffusion coefficient. Considering the combined influence of humidity and ions on pore water flow velocity, the model is coupled into an environmental load model, including: The correction to the effective diffusion coefficient is expressed as: in, Represents the reference diffusion coefficient. This represents the pore water saturation of concrete, imported from the wet-dry cycle model. R represents activation energy; T represents gas constant; T represents absolute temperature of concrete. The calibration temperature for the effective diffusion coefficient; This represents the saturation concentration of the j-th ion; This represents the concentration of the j-th ion; The pore water flow velocity v is affected by both humidity and ions: in, Indicates saturated permeability; Represents the osmotic pressure gradient; Indicates the dynamic viscosity of pore water; This indicates the thermal penetration effect induced by the ion concentration gradient; This represents the correction factor.

6. The method for predicting erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 1, is characterized in that, Step three includes: An optimization problem for predicting the erosion and damage of concrete structures was constructed by combining pre-processed field monitoring data. An optimization algorithm was used to determine the conditions for updating model parameters. The model parameters of the environmental load model were continuously adjusted and optimized through multiple iterations to correct the environmental load model. Based on the modified environmental load model, the damage evolution of the concrete structure area to be predicted is predicted, a concrete structure erosion and damage prediction model is constructed to predict the risk of concrete structure erosion and damage, and the risk level is classified.

7. The method for predicting erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 6, is characterized in that, The optimization problem for predicting erosion and damage in concrete structures is expressed as: in, express Monitor data in real time; Indicates the total monitoring time; Indicates the output parameters of the environmental load model ,in This represents the parameters to be optimized in the model. This represents the input parameters in the model; This represents the regularization coefficient.

8. The method for predicting erosion and damage of concrete structures under dual-cycle conditions of dryness, wetness, salinity, and freshness according to claim 5, is characterized in that, Based on the modified environmental load model, the damage evolution of the concrete structure region to be predicted is calculated. A concrete structure erosion failure prediction model is constructed to predict the risk of concrete structure erosion failure, and the risk level is classified, including: The output of the modified environmental load model is used as the input of the damage evolution model. Damage variables are defined to describe the generation and expansion of damage in concrete structures, and a damage evolution model is established. Numerical simulation using the finite difference method is employed to predict the damage evolution process of concrete structures. Based on the damage evolution model, the degree and distribution of damage are predicted, thereby forecasting the erosion and damage of concrete structures.

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