A gravity dam coupling analysis method, system, electronic device and storage medium

Abstract

This invention belongs to the field of hydraulic structure analysis technology, specifically disclosing a gravity dam coupling analysis method, system, electronic equipment, and storage medium, including: models describing water flow in concrete, the relationship between water pressure and stress, the dissolution reaction process, and the coupling of ion diffusion with water flow. By acquiring influencing variables, parameters of each model are correlated, and a solubility function considering performance parameters is established and dynamically updated. The solubility function is iteratively trained using experimental data to construct a finite element model, and simulations are performed based on the trained function. Early warnings are generated based on the simulation results to predict the concrete deterioration process and optimize maintenance decisions. It has the following advantages: by coupling multiple models such as water flow, chemical dissolution, and hydraulic fracturing, the long-term deterioration process of concrete under the action of water flow and dissolution is accurately simulated. The introduction of a solubility function and dynamic updating of material parameters, considering time-varying deterioration effects, comprehensively improves the accuracy of deterioration prediction and simulation results.

Description

Technical Field

[0001] This invention relates to the field of hydraulic structure analysis technology, and more specifically, to a gravity dam coupling analysis method, system, electronic device, and storage medium. Background Technology

[0002] Concrete gravity dams, as important hydraulic engineering structures, are widely used in large-scale hydraulic projects such as reservoirs and hydropower stations. With long-term operation, the dam concrete is subjected to the coupled effects of reservoir water seepage pressure, erosion, and enormous water loads, leading to gradual deterioration of its structural properties. Hydraulic fracturing and seepage erosion are two major mechanisms affecting the safety and durability of concrete gravity dams. Hydraulic fracturing occurs when water pressure penetrates micro-cracks in the concrete, generating fracturing stresses exceeding the tensile strength of the concrete, causing cracks to propagate and even penetrate. Seepage erosion refers to the process where, under the influence of water flow, seeping water dissolves hydration products such as calcium hydroxide in the concrete, increasing its porosity and reducing its strength and stiffness.

[0003] Existing research mainly focuses on single hydraulic fracturing or chemical dissolution effects, and lacks an effective coupled model to describe the interaction between the two; furthermore, existing numerical models fail to effectively consider the time-varying degradation effect of concrete materials, resulting in inaccurate or overly optimistic long-term predictions. Summary of the Invention

[0004] The present invention aims to provide a gravity dam coupling analysis method, system, electronic device and storage medium to solve or improve the problems mentioned above, such as the failure to effectively couple hydraulic fracturing and seepage erosion, and the lack of an accurate prediction model that considers the time-varying deterioration effect of concrete materials, resulting in inaccurate long-term safety assessment.

[0005] In view of this, the first aspect of the present invention is to provide a gravity dam coupling analysis method.

[0006] A second aspect of the present invention is to provide a system.

[0007] A third aspect of the present invention is to provide an electronic device.

[0008] A fourth aspect of the present invention is to provide a storage medium.

[0009] The first aspect of this invention provides a gravity dam coupling analysis method, comprising the following steps: constructing a first model describing water flow in concrete, a second model describing the relationship between water pressure generated by water flow and internal stress in concrete, a third model describing the concrete dissolution reaction process, and a fourth model describing the influence of ion diffusion and water flow coupling on the concrete dissolution process; obtaining the influence variables of concrete in hydraulic fracturing and seepage dissolution, and performing parameter correlation on the first model, the second model, the third model, and the fourth model according to the influence variables; establishing a dissolution degree function considering at least one performance parameter of concrete, adjusting the performance parameter to be updated through time-driven updates, and correlating it with the first model, the second model, the third model, and the fourth model; iteratively training the dissolution degree function using concrete experimental data, constructing a finite element model of the target dam body, and simulating it using the trained dissolution degree function; and determining whether to generate an early warning based on the simulation results.

[0010] A second aspect of the present invention provides a system comprising: a model building module for building a first model, a second model, a third model, and a fourth model respectively; a parameter association module for associating parameters of the first model, the second model, the third model, and the fourth model based on acquired influencing variables to establish a coupling effect between the models; a solubility function module for establishing a solubility function considering at least one performance parameter of concrete and updating the performance parameter through time-driven updates; associating the solubility function with the first model, the second model, the third model, and the fourth model; and a finite element analysis module for building a finite element model of a target dam body and performing simulation based on the trained solubility function; and determining whether to generate an early warning based on the simulation results.

[0011] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the coupling analysis method described above.

[0012] A fourth aspect of the present invention provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described coupling analysis method.

[0013] The beneficial effects of this invention compared to the prior art are as follows:

[0014] By coupling multiple physical processes such as water flow, chemical dissolution, and hydraulic fracturing, a multi-model coupled analysis method was constructed, which can accurately simulate the long-term deterioration process of concrete under the influence of water flow and chemical dissolution. Compared with traditional single-factor analysis methods, it can comprehensively describe the interaction between water flow and dissolution, thereby improving the prediction accuracy of the deterioration process.

[0015] A solubility function was introduced into the simulation process, and iterative training was performed using experimental data to enable dynamic updates of material parameters over time, taking into account the time-varying degradation effect of concrete materials. In this way, the solubility function not only reflects instantaneous changes in physical properties but also accurately describes the performance degradation trend of concrete during long-term use, avoiding the long-term prediction inaccuracies caused by existing models that do not consider time-varying degradation effects.

[0016] By coupling four models, the interaction between water flow, stress field, dissolution reaction and ion diffusion is simulated respectively. This allows for a comprehensive consideration of the deterioration of concrete in complex environments, improving the accuracy of the simulation results, especially the comprehensive reflection of the various physical effects that concrete may encounter in actual water conservancy projects.

[0017] Additional aspects and advantages of embodiments of the invention will become apparent in the following description or may be learned by practice of embodiments of the invention. Attached Figure Description

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0019] Figure 1 This is a flowchart of the method steps of the present invention;

[0020] Figure 2 This is the parameter association and coupling mechanism of the present invention;

[0021] Figure 3 This is the process for constructing the solubility function of the present invention;

[0022] Figure 4 This is the early warning judgment and decision-making process of the present invention;

[0023] Figure 5 This is a system logic block diagram of the present invention;

[0024] Figure 6 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0025] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0027] Please see Figures 1-6 The following describes a gravity dam coupling analysis method, system, electronic device, and storage medium according to some embodiments of the present invention.

[0028] An embodiment of the first aspect of the present invention provides a gravity dam coupling analysis method. In some embodiments of the present invention, such as... Figures 1-4 As shown, this coupling analysis method includes the following steps:

[0029] S101, respectively construct a first model describing the flow of water in concrete, a second model describing the relationship between the water pressure generated by the water flow and the internal stress of the concrete, a third model describing the concrete dissolution reaction process, and a fourth model describing the influence of ion diffusion and water flow coupling on the concrete dissolution process.

[0030] Here, the first model describes the flow of water through concrete. By simulating the flow process, it focuses on the permeability of the water and the pore structure of the concrete. The flow is influenced by factors such as the porosity and density of the concrete, as well as the input of external water. Therefore, the first model first needs to calculate the change in water mass over time using the principle of mass conservation. Specifically, the porosity and density of the concrete are key parameters that determine the flow velocity and mass distribution of the water. The first model uses a seepage equation to describe the propagation of the water flow. Based on experimental data and numerical simulations, it simulates the changes in the flow velocity and flow rate of water through the concrete, providing fundamental data for the subsequent coupling of hydraulic fracturing and chemical dissolution.

[0031] The second model describes the relationship between water pressure generated by water flow and internal stress in concrete, simulating how this interaction occurs. Water pressure, caused by water flow, affects the stress state of concrete due to its permeability. Particularly in the presence of microcracks in the concrete, water pressure can trigger hydraulic splitting. Therefore, the second model establishes the relationship between water pressure and internal stress in concrete, simulating stress changes caused by water flow and calculating the conditions for hydraulic splitting. It uses stress balance equations to describe how water pressure affects the tensile strength of concrete and the crack propagation path.

[0032] The third model describes the dissolution reaction process in concrete, focusing on simulating this process. Under the long-term influence of water flow and chemicals, hydration products in concrete, such as calcium hydroxide, are dissolved by the chemical components in the water, leading to a decrease in the concrete's strength and stiffness. This third model establishes a dissolution kinetic equation to simulate how ions in the water flow react with the concrete and calculates the rate of the dissolution reaction. As the dissolution reaction proceeds, the porosity of the concrete increases, and its physical and chemical properties change. The third model considers the chemical composition of the water flow, the dissolution rate, and the diffusion characteristics of the dissolved products during the simulation, providing important input data for the hydraulic fracturing process and helping to predict the deterioration behavior of concrete under infiltration and dissolution.

[0033] The fourth model describes the effect of the coupling between ion diffusion and water flow on the concrete dissolution process. Specifically, it describes the coupling effect between water flow and ion diffusion, particularly how water flow accelerates ion diffusion during chemical dissolution. The velocity of the water flow not only propels ions along the concentration gradient but also influences the progress of the dissolution reaction. By coupling the ion diffusion equation with changes in water flow velocity, the fourth model simulates how ions migrate within the concrete during dissolution and assesses the impact of water flow velocity and ion concentration on the dissolution rate. The velocity and direction of the water flow, the progress of the dissolution reaction, and the diffusion rate of ions in the concrete are all comprehensively considered in the fourth model, thereby accurately predicting the deterioration process of concrete under long-term hydraulic and chemical erosion.

[0034] As described above, each model underwent detailed simulation and analysis of the specific action mechanisms of concrete gravity dams. Through the coupled analysis of these models, the long-term effects of hydraulic fracturing and seepage erosion on concrete structures can be comprehensively assessed, thus providing a scientific basis for the safety assessment and maintenance decisions of concrete gravity dams. Each model not only considers the individual effects of water flow, pressure, dissolution reaction, and ion diffusion, but also fully integrates the coupled effects of these factors.

[0035] Specifically, the first model includes the following formula:

[0036]

[0037] in, Porosity; For a point in time; The density of concrete; For fluid velocity; This refers to the quality source, specifically the input source of the seepage flow; This is the gradient operator.

[0038] As described above, the principle of mass conservation is used to describe the flow process of water in concrete, particularly the changes in water mass over time and space, as well as the input of external water sources. The first term in the formula captures the change in water mass over time, the second term describes the spatial expansion and transmission of water flow within the concrete, and the third term considers the contribution of external water sources to the water mass. Through the comprehensive calculation of these three parts, the dynamic changes of water flow in concrete under hydraulic and erosive effects can be fully simulated.

[0039] Specifically, the second model includes the following formula:

[0040]

[0041] in, The pressure of the water flow; It is the acceleration due to gravity; is the permeability coefficient.

[0042] As described above, the effects of water pressure and gravity on water flow velocity reflect the dynamic process of water flow within concrete. Water flow velocity is determined by the water pressure gradient, concrete density, and gravity. When water flows through concrete, changes in water pressure and density affect its velocity, while gravity further influences its flow behavior. The water pressure gradient describes the driving force of the water flow within the concrete; pressure differences cause water to flow from high-pressure areas to low-pressure areas. Gravity, along with water density, affects the flow, especially in cases of slope or strong vertical distribution of the water flow.

[0043] Specifically, the third model includes the following formula:

[0044]

[0045] in, This represents the concentration of calcium ions in the solution. This refers to the solubility concentration in concrete. This represents ion flux.

[0046] As described above, the principle of mass conservation is used to describe the dynamic changes in ion migration and dissolution reaction during the concrete dissolution process. The formula incorporates the time-dependent changes in ion concentration, the dissolution rate, and the ion diffusion flux, reflecting how the dissolution products are dynamically transferred between the concrete and the water flow during the interaction between the water flow and the concrete.

[0047] The dissolution reaction involves the release of hydration products from the concrete into the water through the dissolution action of the water flow. This process is described by ion concentration and dissolution rate.

[0048] Ion diffusion refers to the diffusion of ions in water flow into concrete. It is related to the speed and chemical composition of the water flow and affects the rate of the dissolution reaction.

[0049] Specifically, the fourth model includes the following formula:

[0050]

[0051]

[0052] in, The diffusion coefficient is denoted as . The effective diffusion coefficient; is the diffusion coefficient constant.

[0053] As described above, by combining ion diffusion with water flow coupling, the migration of ions in concrete during water flow and dissolution is accurately described. It not only considers the influence of water flow but also reflects the effect of ion concentration gradient on diffusion, thus more accurately simulating the chemical dissolution process in concrete. Ion diffusion in concrete is affected not only by the concentration gradient but also by water flow. Water flow propels ions along the flow direction, and the diffusion coefficient determines the ion's diffusion capacity. The coupling effect of water flow and ion diffusion is a key factor in the concrete dissolution reaction; increasing water flow velocity accelerates ion migration in concrete, thereby speeding up the dissolution reaction.

[0054] S102, as Figure 2 As shown, the influence variables of concrete in hydraulic fracturing and infiltration erosion are obtained, and the parameters of the first model, the second model, the third model and the fourth model are correlated based on the influence variables.

[0055] Here, during the hydraulic fracturing process, the variables affecting concrete crack propagation mainly include water flow pressure, concrete tensile strength, porosity, initial defects in the concrete such as microcracks or pores, water flow velocity, and permeability coefficient. Water flow pressure is the primary factor leading to hydraulic fracturing; when the water flow pressure reaches the tensile strength of the concrete, cracks will initiate and propagate. To obtain accurate analytical results, it is necessary to extract and quantify the aforementioned influencing variables from experimental data. Specifically, water flow pressure is generated during the infiltration process of water through the concrete. Increased water pressure causes changes in internal stress within the concrete, leading to crack propagation. The magnitude of water flow pressure is a key factor affecting hydraulic fracturing. Tensile strength is affected by factors such as the composition, age, and hydration reaction of the concrete; porosity directly affects the flow path and infiltration velocity of the water. Furthermore, higher porosity allows water to more easily penetrate the concrete interior, accelerating the occurrence of hydraulic fracturing.

[0056] During the infiltration and dissolution process, the variables affecting the dissolution rate of concrete mainly include the chemical composition of the water flow, ion concentration, hydration products of the concrete, pore structure, dissolution rate, and permeability coefficient. Concrete reacts with the chemical components in the water, causing the dissolution of hydration products and increasing the porosity of the concrete. The reaction between the dissolved chemical components in the water flow and the hydration products in the concrete is a key factor in the dissolution reaction. The chemical concentration in the water flow determines the rate of dissolution; ion concentration has a direct impact on the dissolution reaction. Higher ion concentrations lead to a more rapid dissolution reaction. The dissolution rate determines the rate of chemical erosion of concrete by the water flow and is an important factor in assessing concrete durability. The dissolution rate is usually closely related to the water flow velocity, chemical composition, and structural characteristics of the concrete; the pore structure of the concrete has a significant impact on the permeability of the water flow and the dissolution rate. A more porous structure allows for a faster dissolution reaction because the water flow can more easily penetrate into the concrete and react with the hydration products.

[0057] After obtaining the aforementioned influencing variables, the key parameters in the first, second, third, and fourth models are correlated using these variables to achieve the coupling effect between the models. Key parameters in each model, such as water flow velocity, porosity, density, permeability coefficient, tensile strength, and ion concentration, are affected by the dynamic changes of these variables, leading to variations in concrete permeability, crack propagation, dissolution, and erosion effects. Specifically, these variations include:

[0058] In the first model, water flow velocity, permeability coefficient, and concrete porosity will vary with the effects of hydraulic fracturing and dissolution. Water pressure, as a key variable influencing water flow within concrete, directly affects the velocity field, while porosity and permeability coefficient also change with the progress of dissolution. Therefore, the parameters of the first model need to be dynamically adjusted based on water pressure and the degree of concrete dissolution.

[0059] The second model involves the relationship between water flow pressure and internal stress in concrete. Changes in water flow pressure not only affect hydraulic splitting of concrete but may also have a coupling effect with physical properties such as tensile strength and porosity. The chemical composition and ion concentration of the water flow can also affect the stress state of concrete, as the dissolution reaction may lead to a decrease in material strength. Therefore, the calculation of water flow pressure and stress in the second model needs to take into account the influence variables of hydraulic splitting and chemical dissolution.

[0060] The third model describes the dissolution reaction process. In this model, the chemical composition of the water flow (such as ion concentration) and factors such as flow velocity and porosity directly affect the dissolution rate. Therefore, the parameters of the third model also need to be dynamically correlated based on the chemical properties of the water flow, the dissolution rate, and the pore structure of the concrete.

[0061] The fourth model involves the coupling effect of water flow and ion diffusion. The velocity of the water flow and the ion concentration in the water flow have a significant impact on the ion diffusion rate and the dissolution reaction rate. The ion diffusion process is affected not only by the water flow velocity but also by the combined effects of the chemical composition of the water flow and the dissolution rate. Therefore, the calculation of the fourth model requires establishing an accurate parameter correlation between the water flow and the dissolution process.

[0062] As described above, by obtaining the key influencing variables of concrete during hydraulic fracturing and seepage dissolution, and using these variables to correlate the parameters in the four models, the coupling effect between the models was achieved. This method allows for a comprehensive simulation of the concrete deterioration process, taking into account multiple factors such as water flow, pressure, ion concentration, and dissolution rate.

[0063] Specifically, the steps for associating the parameters of the first, second, third, and fourth models based on the influencing variables include:

[0064] The parameters of the first model are correlated with those of the second and fourth models based on the water flow velocity.

[0065] The parameters of the third and fourth models were correlated based on the ion concentration and ion flux in the concrete.

[0066] Based on the specific descriptions above, the parameters of the four models are correlated through influencing variables to ensure that the models are coupled and collectively reflect the combined effects of water flow and dissolution on concrete. This correlation enables interaction between the models, ensuring consistency in simulating the long-term deterioration of concrete. In the first model, the water flow velocity directly affects the distribution of water mass and the water transfer process within the concrete. Therefore, the water flow velocity needs to be correlated with parameters such as the porosity and density of the concrete to simulate the flow characteristics of water within it. Increased water flow velocity generally means faster water propagation within the concrete and a larger water flow rate in the pores, thus affecting the water mass distribution. In the second model, the water flow velocity directly affects changes in water pressure, which in turn affects changes in internal stress within the concrete. Water pressure is a key factor leading to hydraulic splitting; higher water flow velocities result in a larger water pressure gradient, increasing the likelihood of hydraulic splitting. Water flow velocity is closely related to the tensile strength and crack propagation path of concrete. In the fourth model, the water flow velocity affects the ion diffusion process. Increased water flow velocity promotes ion migration because the water flow not only propels ions to diffuse along the concentration gradient, but also directly accelerates the ion diffusion process.

[0067] The third model describes the concrete dissolution process, where the chemical composition of the water flow, especially the ion concentration, directly affects the dissolution rate. Higher ion concentrations accelerate the dissolution reaction; therefore, it is essential to correlate ion concentration with the concrete dissolution rate. The dissolution rate varies with the ion concentration in the water flow, thus changes in ion concentration directly influence the dissolution kinetics equations in the third model. In the fourth model, ion flux is not only related to the water flow velocity but also closely correlated with ion concentration and diffusion coefficient. By correlating ion flux with ion concentration and diffusion coefficient, the dissolution reaction of concrete and the influence of water flow on ion migration can be simulated more accurately.

[0068] Specifically, water flow velocity, ion concentration, and ion flux are all derived from experimental measurements or numerical inversions of seepage and chemical fields in concrete gravity dams. Water flow velocity can be obtained through indoor seepage experiments, seepage pressure monitoring, or numerical calculations of seepage. The measured object is the seepage flow within the pores and microcracks of the concrete, characterizing the rate of water movement in the porous medium per unit time. This directly controls the seepage intensity distribution in the first model and the water pressure gradient within cracks in the second model, thus affecting the internal stress response of the concrete. It also serves as the input to the flow field driving ion migration in the fourth model. Ion concentration is obtained by collecting exudate or pore water samples and performing ion composition analysis. The measured object is the concrete pore solution or... Solute components in the seepage fluid, such as calcium ions and hydrogen ions, are used to characterize the extent of the dissolution reaction and the chemical potential gradient. They are core parameters controlling the dissolution rate and solid-liquid mass exchange in the third model, and determine the concentration gradient for ion migration in the fourth model. Ion flux, on the other hand, is obtained from experimental data or coupled calculations based on the amount of ions transported per unit cross-section per unit time. Its measurement object is the actual migration process of ions in the concrete pore network. It is used to quantify the ion transport intensity under the combined action of diffusion and convection. It is a characterization quantity that links the chemical reaction rate in the third model with the migration process in the fourth model. Through parameter correlation, multi-field coupled characterization between the seepage field, stress field, and chemical field is realized.

[0069] As described above, by correlating water flow velocity with the first, second, and fourth models, and ion concentration and ion flux with the third and fourth models, it can be ensured that the parameters of all models are dynamically updated and accurately reflect the coupling effect of water flow and dissolution reaction in concrete. Through the above correlations, the deterioration process of concrete can be accurately predicted in the coupled analysis of water flow and dissolution.

[0070] S103, such as Figure 3 As shown, a solubility function considering at least one performance parameter of concrete is established, the performance parameter is adjusted to be updated by time-driven update, and associated with the first model, the second model, the third model and the fourth model.

[0071] Here, a solubility function is established to reflect the changes in concrete performance parameters over time. The solubility function is a mathematical expression describing the decay process of concrete performance parameters as the degree of deterioration changes under the influence of water flow and chemical erosion. Typically, the solubility function is closely related to the physical and chemical properties of concrete, dynamically updating over time with changes in dissolution reactions and crack propagation. The solubility function simulates the deterioration process of concrete under the influence of water flow and dissolution reactions, usually manifested as a gradual decay of performance parameters such as tensile strength, elastic modulus, and permeability coefficient. As the solubility increases, these performance parameters of concrete gradually decrease. The solubility function defines a decay function for each performance parameter, which may typically be expressed using exponential decay, linear decay, or other empirical function forms. The specific form and coefficients of the solubility function usually need to be calibrated using experimental data. Specifically, long-term water erosion experiments can be conducted to measure parameters such as tensile strength, elastic modulus, and permeability coefficient of concrete at different time points, and then a decay function related to solubility can be fitted.

[0072] After obtaining the solubility function, the concrete performance parameters are correlated with the time-driven, updated solubility function. As the solubility changes over time, the various performance parameters of the concrete also change over time. The decay of performance parameters can be dynamically updated through the solubility function. The performance parameters of concrete gradually deteriorate over time. During the simulation, the solubility function adjusts the values ​​of the performance parameters according to specific time steps. Specifically, within each calculation time step, the solubility function updates the concrete performance parameters according to the current degree of solubility. The time-driven update mechanism ensures that the concrete performance accurately reflects its actual deterioration under long-term exposure to water flow and a corrosive environment. After each update, the performance parameters will affect subsequent model calculations, ensuring that the coupled analysis reflects the true deterioration process.

[0073] After establishing the dissolution function and updating the performance parameters in a time-driven manner, the next step is to associate the dissolution function with the first, second, third, and fourth models. The calculations for each model will dynamically adjust its parameters based on the updated dissolution function results, specifically including:

[0074] In the first model, the flow of water through concrete is influenced by parameters such as porosity and permeability. The solubility function can dynamically adjust the porosity and permeability of the concrete, thereby altering the flow characteristics of the water. By correlating the solubility function with these parameters, it is possible to simulate how the flow of water through concrete changes with increasing solubility in the concrete.

[0075] The second model simulates the relationship between water flow pressure and internal stress in concrete. As the dissolution reaction proceeds, the tensile strength of the concrete decreases over time. The degree of dissolution affects the tensile strength of the concrete, which in turn affects the effect of water flow pressure on the internal stress of the concrete. Through this correlation, the process of hydraulic fracturing induced by water flow pressure can be dynamically predicted.

[0076] In the third model, the rate of dissolution is influenced by the chemical composition of the water flow and the physical properties of the concrete. The solubility function is closely related to the dissolution rate and can adjust the progress and rate of the dissolution reaction according to the degree of concrete erosion.

[0077] The fourth model describes the coupling effect of water flow and ion diffusion. The ion diffusion rate is affected by the water flow velocity and ion concentration. The solubility function can adjust the ion diffusion coefficient and ion concentration, thus affecting the dissolution process of concrete. By associating the solubility function with the fourth model, the influence of the interaction between water flow and ion diffusion on the dissolution rate can be simulated.

[0078] As described above, by establishing the solubility function, the deterioration process of concrete under the influence of water flow and chemical erosion was accurately simulated. The solubility function not only dynamically updates the performance parameters of the concrete but also ensures that changes in performance parameters are synchronized with time through a time-driven mechanism. By associating the solubility function with the four models, the coupled analysis process can be made more accurate, precisely reflecting the deterioration behavior of concrete under long-term water flow and chemical erosion.

[0079] Specifically, the performance parameters include the tensile strength, elastic modulus, and permeability coefficient of concrete; and the solubility function includes:

[0080]

[0081] in, Let be the tensile strength among the performance parameters at time t. This refers to the initial state value of the tensile strength in the performance parameters; This is a material degradation function calibrated through experiments, and the structure of the material degradation function is set according to the type of performance parameter.

[0082] Regarding the specific description above, the tensile strength of concrete changes over time. Initially, the tensile strength of concrete is a baseline value, representing the tensile strength of concrete without exposure to water flow or chemical corrosion. Over time, water flow and dissolution reactions gradually alter the pore structure and physical properties of the concrete, leading to a gradual decrease in tensile strength. This formula dynamically adjusts the tensile strength using a solubility function to simulate the deterioration process of concrete under water flow and chemical corrosion conditions.

[0083] The solubility function, calibrated using experimental data, reflects the impact of the degree of concrete dissolution on tensile strength. The dissolution reaction intensifies with the action of water flow, and the chemical composition of the water plays a crucial role in the dissolution rate of concrete hydration products. The solubility function quantifies this effect and, by multiplying it by the initial tensile strength, accurately calculates the tensile strength of concrete under different dissolution environments.

[0084] Initial tensile strength represents the strength of concrete when it is not subjected to any chemical erosion or hydraulic fracturing. It provides a baseline value for subsequent calculations and is the basis for assessing concrete durability. Under the influence of water flow and dissolution, the tensile strength of concrete gradually decreases over time, while the solubility function dynamically adjusts the tensile strength according to environmental changes.

[0085] As described above, by introducing the initial tensile strength and the solubility function, the process of concrete tensile strength changing over time is accurately described. The solubility function, by reflecting the influence of water flow and chemical erosion on concrete properties, quantifies the decay of tensile strength.

[0086] Besides tensile strength, elastic modulus, and permeability coefficient, other performance parameters of concrete, such as compressive strength, frost resistance, and alkali resistance, also deteriorate over time and with environmental changes. Based on the characteristics of each performance parameter, a corresponding degradation function can be set individually. Specifically, frost resistance may be affected by temperature changes and water flow, while alkali resistance may be related to chemical corrosion. The degradation functions for these parameters also need to be calibrated based on experimental data and described using appropriate attenuation models.

[0087] S104, such as Figure 4 As shown, the dissolution rate function is iteratively trained using concrete experimental data, a finite element model of the target dam body is constructed, and the trained dissolution rate function is used for simulation; the simulation results are used to determine whether to generate an early warning.

[0088] Here, the first step is to iteratively train the solubility function using experimental data to accurately describe the degradation process of concrete performance parameters over time. Experimental data typically comes from concrete exposure tests under different conditions, simulating the behavior of concrete under long-term water flow and chemical erosion. The experimental data includes changes in tensile strength, elastic modulus, permeability coefficient, and other performance parameters of concrete under different erosion environments. By setting up different experimental groups, the changes in the physical properties of concrete under different environmental conditions can be obtained. Using the experimental data, the solubility function will be continuously adjusted to optimize its fit, enabling it to accurately reflect changes in concrete performance parameters. Specifically, the experimental results will show the change in tensile strength of concrete at a certain time point, and the solubility function will be adjusted based on this data until the function can accurately predict the concrete performance at other time points.

[0089] Once the optimized dissolution function is obtained through training with experimental data, the next step is to apply this function to the finite element model of the target dam. The finite element model (FEM) is a computational simulation tool used to decompose complex structures into smaller elements, facilitating numerical calculations. When constructing the FEM, detailed consideration must be given to the concrete dam's geometry, material zoning, construction techniques, and geological structure. Different regions of concrete may have different physical properties; therefore, the FEM must accurately set the corresponding properties for each region. By modeling the geometry and material properties of the target dam, a representative and reliable FEM can be established. The completed FEM is then coupled with the trained dissolution function. The dissolution function dynamically updates the concrete's performance parameters based on the real-time solubility of the concrete under hydraulic fracturing and dissolution. In this way, the FEM can reflect the stress, deformation, and damage state of the concrete under different degrees of dissolution in real time.

[0090] After constructing the finite element model and correlating the solubility function, the next step is to dynamically simulate the concrete gravity dam using the trained solubility function. Finite element analysis can predict the deterioration process of concrete under long-term water flow and dissolution, yielding results such as stress distribution, crack propagation, and changes in physical properties. Simulations can also reveal the trends in tensile strength, elastic modulus, and permeability coefficient of the concrete dam over future time periods. These simulation results can help engineers predict the long-term stability of the dam, providing data support for dam maintenance and reinforcement. The simulation results will be output graphically and numerically, including information such as changes in the dam's safety factor, crack propagation paths, and solubility distribution. This data helps identify the most vulnerable areas of the dam, providing a basis for subsequent early warning systems and maintenance strategies.

[0091] The simulation results will be used to determine whether an early warning needs to be generated. By analyzing the simulation results, the system can assess the long-term safety of the dam, determine whether it has reached a predetermined safety threshold, and automatically generate an early warning based on the results. If the simulation results show that the safety of the dam is close to or below the set threshold, the system will automatically generate an early warning, notifying relevant personnel to take appropriate maintenance or repair measures to avoid potential structural failure or catastrophic consequences.

[0092] As described above, by combining it with the finite element model, the trained dissolution function can dynamically simulate the concrete dam body and predict its deterioration trend over a future period. Finally, based on the simulation results, the system will automatically determine whether to generate an early warning.

[0093] Specifically, the steps for iteratively training the dissolution function using concrete experimental data include:

[0094] To obtain experimental values ​​of the influence variables of concrete during the experiment until failure.

[0095] Experimental values ​​were input into the first, second, third, and fourth models, and simulations were performed to obtain characterization variables related to performance parameters.

[0096] The characterization variables are input into the dissolution function for iterative training.

[0097] Regarding the specific description above, the main goal of the iterative training step using concrete experimental data to improve the solubility function is to accurately describe the deterioration process of concrete under the influence of water flow and chemical dissolution. First, key influencing variables of concrete as it is exposed to water flow and dissolution reaction environments until failure are obtained through experimental data. These variables include water flow velocity, concrete porosity, density, ion concentration, and chemical reaction rate, which are the main driving factors of concrete deterioration. By controlling different experimental conditions, data on the deterioration state and performance changes of concrete at different time points can be obtained.

[0098] After obtaining the aforementioned influencing variables, the second step is to input the experimental data into the first, second, third, and fourth models for simulation. These four models respectively describe the flow of water in concrete, the hydraulic fracturing effect of water on concrete, the dissolution reaction process, and the coupling effect of ion diffusion. By inputting variables from the experimental data, such as water velocity, porosity, density, and ion concentration, the changes in concrete under different environmental conditions can be simulated. Specifically, in the first model, factors such as water velocity and porosity affect the flow of water in concrete, thereby affecting the occurrence of hydraulic fracturing; in the third model, the ion concentration in the water flow affects the dissolution reaction rate of concrete.

[0099] Through simulation using the above model, characteristic variables related to concrete performance can be obtained. These variables may include tensile strength, elastic modulus, and permeability coefficient of concrete. By repeatedly calculating these characteristic variables, the combined effects of water flow and dissolution on concrete performance can be quantified, thus providing necessary input data for optimizing the solubility function.

[0100] The third step involves inputting the simulated characterization variables into the solubility function and continuously optimizing the function through iterative training. The solubility function is a mathematical expression describing the relationship between the degree of concrete dissolution and its performance degradation; it is typically a function related to solubility. Experimental data reveals the changes in concrete performance under different solubilities, and the solubility function dynamically adjusts accordingly. The training of the solubility function employs an iterative optimization approach, gradually reducing errors by comparing it with experimental data. Each time, the simulated results are compared with experimental results, and the parameters of the solubility function are adjusted in reverse based on the error until the solubility function can accurately predict the performance changes of concrete under different dissolution environments.

[0101] As described above, after multiple iterations of training, the erosion degree function can accurately reflect the deterioration process of concrete under the influence of water flow and dissolution reactions. This optimized erosion degree function can not only be used to predict the long-term deterioration trend of concrete, but also provide a scientific basis for concrete safety assessment and maintenance decisions. By combining it with the finite element model, the erosion degree function can be further used to simulate the performance of concrete under actual working conditions, and generate early warnings based on the simulation results to detect potential deterioration risks of concrete in advance.

[0102] Specifically, the steps of inputting the characterization variables into the dissolution function for iterative training include:

[0103] Extract the critical values ​​corresponding to the concrete failure from the characterization variables, and input the critical values ​​into the dissolution degree function.

[0104] Based on the specific description above, critical values ​​are extracted from the characterization variables obtained through model simulation and experiments. These values ​​represent the critical points or turning points in concrete performance before failure. Critical values ​​refer to the threshold values ​​at which concrete performance changes under the influence of water flow and chemical erosion. The definition of critical values ​​may differ for different performance parameters; typically, it refers to the value at which concrete performance reaches a predetermined lower limit.

[0105] The extracted critical values ​​are input into the solubility function for training. The solubility function mainly describes the change in concrete performance with the degree of dissolution reaction, and is usually adjusted and optimized based on experimental data or simulation results. In this process, the goal of the solubility function is to adjust its parameters through critical values ​​so that the function can accurately reflect the performance degradation of concrete under the action of dissolution reaction.

[0106] Specifically, the steps of iteratively training the dissolution function using concrete experimental data also include:

[0107] The boundary conditions for determining performance parameters are established by using critical values.

[0108] Regarding the specific description above, the step of iteratively training the dissolution function using concrete experimental data also includes determining the judgment boundary conditions for performance parameters through critical values. This step first extracts key performance parameter data obtained from the experiment, especially the critical values ​​at which concrete approaches or reaches failure under the action of water flow and chemical dissolution. Critical values ​​refer to certain performance parameters of concrete, such as tensile strength, elastic modulus, and permeability coefficient. When these values ​​reach a certain set threshold, it indicates that the structural performance of the concrete has approached its limit, which may lead to structural damage or failure. The experiment identifies the key critical points of the performance parameters by monitoring changes in these performance parameters.

[0109] The aforementioned critical values ​​are input into the solubility function to define the boundary conditions for concrete performance degradation. These boundary conditions are used to determine the deterioration process of concrete under the influence of water flow and chemical erosion. When performance parameters approach the critical values, the solubility function is used to predict the degree of concrete degradation, and its parameters are dynamically adjusted by fitting experimental data. During each training iteration, the solubility function is used to correct the model based on the critical values ​​to ensure that the model can accurately simulate the deterioration process of concrete under different environmental conditions.

[0110] Specifically, the steps for determining whether to generate an early warning based on the simulation results include:

[0111] Determine whether the simulated performance parameters of the target dam body at each time step are similar to the judgment boundary conditions. If so, generate an early warning and output the corresponding time step.

[0112] Based on the specific description above, the simulation results of the target dam will be used to evaluate the performance parameters at each time step. By performing time-series simulations of the concrete performance parameters, the simulation results at each time step are compared with previously set judgment boundary conditions. These judgment boundary conditions are typically threshold values ​​set based on critical values, representing the performance limits of concrete under specific conditions. When the simulation results show that the performance parameters are close to or have reached the aforementioned critical values, it indicates that the concrete performance may be approaching a critical point of failure.

[0113] If the simulated performance parameters are close to the judgment boundary conditions, the system will generate an early warning and indicate the corresponding time in the output. This process will dynamically monitor each moment and continuously evaluate changes in concrete performance. When the simulated data reaches or exceeds the critical value, the early warning will be triggered, and the specific time of the problem will be output in a timely manner, providing relevant personnel with timely handling information. The above early warning information can provide a basis for decisions on the safe maintenance, repair, or reinforcement of the dam, avoiding structural failures caused by the degradation of concrete performance.

[0114] This invention provides a coupled analysis method for gravity dams, which accurately simulates the performance changes of concrete under long-term water flow and chemical erosion by coupling multiple physical processes such as water flow, dissolution reaction, and hydraulic fracturing. It not only considers the influence of water flow on the concrete structure but also incorporates the effects of dissolution reaction and ion diffusion, thus providing a more comprehensive description of the deterioration process of concrete under different environmental conditions.

[0115] By coupling multiple scenarios such as hydraulics, chemistry, and dissolution, the complexity of the interaction between water flow and chemical erosion can be simulated, thereby improving the accuracy of predicting concrete performance degradation. Compared with traditional single-factor analysis methods, it can more realistically reflect the performance of concrete in actual environments.

[0116] A system 2 is provided in an embodiment of the second aspect of the present invention. In some embodiments of the present invention, such as... Figure 5 As shown, system 2 includes:

[0117] Model building module 201 is used to build the first model, the second model, the third model and the fourth model respectively.

[0118] The parameter association module 202 is used to associate the parameters of the first model, the second model, the third model and the fourth model based on the obtained influencing variables, so as to establish the coupling effect between the models.

[0119] The solubility function module 203 is used to establish a solubility function that considers at least one performance parameter of concrete and to update the performance parameter through time-driven updates; and to associate the solubility function with the first model, the second model, the third model and the fourth model.

[0120] The finite element analysis module 204 is used to construct a finite element model of the target dam body and simulate it based on the trained dissolution function; it determines whether to generate an early warning based on the simulation results in order to predict the deterioration process of concrete under long-term water flow and dissolution.

[0121] This invention provides a system that integrates multiple modules to achieve precise coupled analysis of concrete gravity dams under the influence of water flow and chemical erosion. The system accurately simulates changes in concrete performance by constructing multiple physical process models and correlating their parameters, and dynamically updates the simulation using a solubility function. Through the finite element analysis module, the system can predict the deterioration process of concrete under long-term action, providing a scientific basis for dam safety assessment, maintenance decisions, and early warning generation, thereby improving the durability, reliability, and maintenance efficiency of concrete structures.

[0122] An embodiment of the third aspect of the present invention provides an electronic device. In some embodiments of the present invention, such as... Figure 6As shown, an electronic device is provided, which may include: a desktop computer, a laptop, a handheld computer, and a cloud server, etc. The electronic device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or different components.

[0123] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0124] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 3. The memory 302 can also include both internal and external storage units of the electronic device 3. The memory 302 is used to store the computer program 303 and other programs and data required by the electronic device.

[0125] An embodiment of the fourth aspect of the present invention provides a storage medium. In some embodiments of the present invention, a storage medium is provided that, when executed by processor 301, implements the steps of the above-described method. Therefore, the storage medium provided in the fourth aspect of the present invention has all the technical effects of the above-described steps, which will not be repeated here.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether the above functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

Claims

1. A gravity dam coupling analysis method, characterized in that, Includes the following steps: A first model describing water flow in concrete, a second model describing the relationship between water pressure generated by water flow and internal stress in concrete, a third model describing the concrete dissolution reaction process, and a fourth model describing the influence of ion diffusion and water flow coupling on the concrete dissolution process are constructed respectively. The first model includes the following formulas: In the formula, Porosity; For a point in time; The density of concrete; For fluid velocity; This refers to the quality source, specifically the input source of the seepage flow; Gradient operator; The second model includes the following formulas: In the formula, The pressure of the water flow; It is the acceleration due to gravity; Permeability coefficient; The third model includes the following formula: In the formula, This represents the concentration of calcium ions in the solution. This refers to the solubility concentration in concrete. This refers to ion flux; The fourth model includes the following formulas: In the formula, The diffusion coefficient is denoted as . The effective diffusion coefficient; The diffusion coefficient is a constant; The influence variables of concrete in hydraulic fracturing and seepage erosion are obtained, and the parameters of the first model, the second model, the third model and the fourth model are correlated based on the influence variables; Establish a solubility function that considers at least one performance parameter of concrete, adjust the performance parameter to be updated through time-driven updates, and associate it with the first model, the second model, the third model, and the fourth model; The solubility function is iteratively trained using concrete experimental data, a finite element model of the target dam body is constructed, and the trained solubility function is used for simulation. Whether to generate an early warning is determined based on the simulation results.

2. The gravity dam coupling analysis method according to claim 1, characterized in that, The step of performing parameter correlation on the first model, the second model, the third model, and the fourth model based on the influencing variables includes: The first model is associated with the parameters of the second model and the fourth model respectively according to the water flow velocity; The parameters of the third model and the fourth model are correlated based on the ion concentration and ion flux in the concrete.

3. The gravity dam coupling analysis method according to claim 1, characterized in that, The step of iteratively training the dissolution function using concrete experimental data includes: To obtain experimental values ​​of the influence variables of concrete during the experiment until failure; The experimental values ​​are input into the first model, the second model, the third model, and the fourth model, and simulations are performed to obtain characterization variables related to the performance parameters. The characterization variables are input into the dissolution function for iterative training.

4. The gravity dam coupling analysis method according to claim 3, characterized in that, The step of inputting the characterization variable into the dissolution function for iterative training includes: Extract the critical values ​​corresponding to the concrete failure from the characterization variables, and input the critical values ​​into the dissolution degree function.

5. The gravity dam coupling analysis method according to claim 4, characterized in that, The step of iteratively training the dissolution function using concrete experimental data further includes: The critical value is used to determine the boundary conditions for judging the performance parameter.

6. The gravity dam coupling analysis method according to claim 5, characterized in that, The step of determining whether to generate an early warning based on the simulation results includes: Determine whether the simulated performance parameters of the target dam body at each time point are similar to the judgment boundary conditions. If so, generate an early warning and output the corresponding time point.

7. The gravity dam coupling analysis method according to claim 1, characterized in that, The performance parameters include the tensile strength, elastic modulus, and permeability coefficient of the concrete. And the solubility function includes: Among them, the Let be the tensile strength among the performance parameters at time t. The initial state value of the tensile strength in the performance parameters; This is a material degradation function calibrated through experiments, and the structure of the material degradation function is set according to the types of performance parameters.

8. A system for performing the gravity dam coupling analysis method according to any one of claims 1-7, characterized in that, include: The model building module is used to build the first model, the second model, the third model, and the fourth model, respectively. The parameter association module is used to perform parameter association on the first model, the second model, the third model and the fourth model based on the acquired influencing variables, so as to establish the coupling effect between the models; The solubility function module is used to establish a solubility function that considers at least one performance parameter of concrete and to update the performance parameter in a time-driven manner. The solubility function is associated with the first model, the second model, the third model, and the fourth model; The finite element analysis module is used to construct a finite element model of the target dam body and perform simulation based on the trained dissolution function; Whether to generate an early warning is determined based on the simulation results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the gravity dam coupling analysis method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the gravity dam coupling analysis method as described in any one of claims 1 to 7.

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