Concrete dam flood risk evaluation index system construction method

Abstract

The invention relates to the technical field of risk evaluation index systems, in particular to a concrete dam flood risk evaluation index system construction method. The method comprises the following steps: carrying out flow velocity gradient analysis on river channel data of an upstream area of an earth dam to obtain river channel rainy season flow velocity gradient data; performing water depth increment rate calculation on the flow and flow velocity gradient data in the rainy season of the riverway, and performing stratified pressure water flow impact simulation calculation among different water depths to obtain stratified pressure water flow impact data; performing fatigue limit load strength estimation on the concrete dam structure design data according to the layered pressure water flow impact data to obtain fatigue limit load strength estimation data; performing flood risk level prediction on the fatigue limit load intensity estimation data to obtain flood risk level data; and carrying out risk evaluation system construction on the flood risk level data to obtain a flood risk evaluation system. According to the method, the risk evaluation index system technology is optimized, so that the risk evaluation index system technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment index systems, and particularly to a method for constructing a flood risk assessment index system for concrete dams. Background Art

[0002] As an important water conservancy infrastructure, concrete dams are widely used in the fields of water resource regulation, flood control, power generation, etc. Their safety is directly related to the life and property safety of downstream areas. Especially during floods, the stability and flood resistance ability of concrete dams are crucial. The flood risk assessment system for concrete dams should cover multiple dimensions, including aspects such as the safety of the dam structure, the impact of floods and rainstorms, the geological environment of the dam area, monitoring data, and early warning capabilities. First of all, the health status of the dam structure is the basis for evaluating risks, mainly including monitoring data such as dam materials, structures, stress distributions, and cracks, which directly affect the performance of the dam under extreme conditions such as floods. Secondly, the intensity and duration of floods and rainstorms are also key factors. Different hydrological conditions lead to different flood risks. Therefore, it is necessary to predict the scale and flow rate of floods through hydrological models and evaluate them in combination with the actual situation of the dam. In addition, the geological conditions of the dam area play a decisive role in the stability of the dam. Geological disasters such as landslides and settlements increase the pressure on the dam or trigger secondary disasters. Therefore, the assessment of the geological environment cannot be ignored. However, there are problems in a traditional method for constructing a flood risk assessment index system for concrete dams, such as inaccurate analysis of the ultimate load intensity of the dam and large errors in predicting flood risk levels. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for constructing a flood risk assessment index system for concrete dams to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a flood risk assessment index system for concrete dams, the method includes the following steps: Step S1: Obtain the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam; perform a velocity gradient analysis on the river channel data in the upstream area of the earth dam to obtain the river channel rainy season flow velocity gradient data; Step S2: Calculate the water depth increment rate for the river channel rainy season flow velocity gradient data to obtain the river channel water depth increment rate data; perform a layered pressure water flow impact simulation calculation between different water depths on the structural design data of the concrete dam based on the river channel rainy season flow velocity gradient data to obtain the layered pressure water flow impact data; Step S3: Estimate the fatigue ultimate load intensity for the structural design data of the concrete dam according to the layered pressure water flow impact data to obtain the fatigue ultimate load intensity estimation data; Step S4: Predict the flood risk level for the fatigue limit load intensity estimation data to obtain flood risk level data; construct a risk assessment system for the flood risk level data to obtain a flood risk assessment system.

[0005] The present invention obtains the concrete dam structure design data and the river channel data in the upstream area of the earth dam, and conducts a velocity gradient analysis, which helps to deeply understand the hydrological characteristics of the upstream river channel during the rainy season, especially the change of the flow velocity gradient. By analyzing these data, the spatial distribution and change trend of the river channel velocity can be identified, providing accurate basic data for the subsequent water flow impact simulation to ensure that the impact on the dam body can be more realistically simulated. By calculating the water depth increment rate of the river channel rainy season flow velocity gradient data, the rate of change of the river channel water level under different flow conditions can be evaluated, and further based on this data, a layered pressure water flow impact simulation is carried out for different water depths of the concrete dam. This step can simulate the different pressures borne by the dam body under complex water flow conditions during the rainy season, and reveal the impact effect of the water flow at different depths of the dam body, thus providing key information for the structural safety assessment of the dam body. Using the layered pressure water flow impact data to estimate the fatigue limit load intensity can reveal the fatigue performance of the concrete dam under long-term water flow impact and predict its fatigue failure risk under extreme conditions. This step provides data support for the health management and maintenance cycle planning of the concrete dam, helps to identify potential structural weaknesses in advance, and enhances the long-term stability and safety of the dam body. By predicting the flood risk level for the fatigue limit load intensity data, the safety of the dam body under different flood conditions can be evaluated, and a scientific risk prevention and control strategy can be formulated. Combining the construction of the risk assessment system, the state of the dam body with different risk levels can be quantitatively evaluated, providing clear risk level judgments and countermeasures for decision-makers to ensure the operation safety of water conservancy facilities and reduce the potential hazards brought by floods. Therefore, the present invention is an optimized treatment of the traditional method for constructing a flood risk assessment index system for a concrete dam, solving the problems existing in the traditional method for constructing a flood risk assessment index system for a concrete dam, such as inaccurate analysis of the limit load intensity of the dam body and large prediction errors for the flood risk level, improving the accuracy of the analysis of the limit load intensity of the dam body, and reducing the large prediction errors for the flood risk level.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain the concrete dam structure design data and the river channel data in the upstream area of the earth dam; Step S12: Conduct a rainy season flow statistics on the river channel data in the upstream area of the earth dam to obtain the upstream river channel rainy season flow data; Step S13: Conduct a velocity gradient analysis on the upstream river channel rainy season flow data to obtain the river channel rainy season flow velocity gradient data.

[0007] The present invention obtains the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam, which is the basis for subsequent analysis. The structural design data of the concrete dam includes the dimensions of the dam body, material properties, load calculation, etc., which can provide important parameters for the stability assessment of the dam body; while the upstream river channel data (such as river channel width, depth, hydro-meteorological conditions, etc.) is the key to analyzing the changes in flow rate and velocity. This step lays a solid data support for the whole process by collecting accurate original data, ensuring the reliability of subsequent analysis. Conducting a rainy season flow statistics on the river channel data in the upstream area of the earth dam to obtain the upstream river channel rainy season flow data can identify the flow characteristics of the river channel during different rainy seasons. The work in this stage helps to understand the changing trend of water flow during the rainy season, especially the patterns of peak flow and flow fluctuations. Through accurate flow data statistics, it can provide the necessary basis for subsequent velocity gradient analysis, especially for a clearer prediction basis for the response of the river channel under extreme climate conditions and the potential impact on the dam body. Conducting a velocity gradient analysis on the upstream river channel rainy season flow data to obtain the river channel rainy season flow velocity gradient data can reveal the velocity distribution and gradient changes of the river channel water flow during the rainy season. The velocity gradient is an important indicator for evaluating the interaction between the water flow and the dam body, directly affecting the water flow impact force, friction force, and the erosion degree of the dam body surface. Through precise velocity gradient analysis, the changes in water flow at different positions can be evaluated, providing key data for subsequent water flow impact simulation and dam body safety assessment, and helping to determine which areas are facing greater water flow impact and potential risks.

[0008] Preferably, step S2 includes the following steps: Step S21: Evaluating the sediment-carrying capacity of the river channel data in the upstream area of the earth dam according to the river channel rainy season flow velocity gradient data to obtain the sediment-carrying capacity evaluation data; Step S22: Calculating the water depth increment rate of the river channel rainy season flow velocity gradient data to obtain the river channel water depth increment rate data; Step S23: Evaluating the sediment density between different water depths of the sediment-carrying capacity evaluation data according to the river channel water depth increment rate data to obtain the sediment density data between different water depths; Step S24: Conducting a layered pressure water flow impact simulation calculation on the structural design data of the concrete dam based on the sediment density data between different water depths and the river channel rainy season flow velocity gradient data to obtain the layered pressure water flow impact data.

[0009] The present invention evaluates the sediment concentration carried by the river for the river channel data in the upstream area of the earth dam based on the river channel flow velocity and flow rate gradient data during the rainy season, and obtains the sediment concentration evaluation data. This step evaluates the amount of sediment carried in the water flow by analyzing the flow velocity and flow rate characteristics of the river channel during the rainy season. The concentration of sediment directly affects the scouring effect of the river channel flow, especially the erosion risk of the dam body. Through accurate sediment concentration evaluation, important parameters can be provided for subsequent water flow impact simulation, ensuring a true reflection of the interaction between the water flow and the dam body, and helping to identify areas that face greater scouring and structural damage risks due to high sediment content. Calculate the water depth increment rate for the river channel flow velocity and flow rate gradient data during the rainy season to obtain the river channel water depth increment rate data. The calculation of the water depth increment rate can reveal the rate of rise or fall of the river channel water level under different flow conditions. The change in water depth has an important impact on the stability of the dam body. Especially during the rainy season when the water flow is large, the rapid change in water depth may cause the dam body to bear different degrees of pressure impact. Through this calculation, data support for the dynamic change of water flow can be provided for subsequent structural simulation, helping to evaluate the water flow impact on the dam body at different water depths. Evaluate the sediment density between different water depths for the sediment concentration evaluation data based on the river channel water depth increment rate data to obtain the sediment density data between different water depths. The sediment-carrying capacity of the water flow is different at different water depths. The deeper the water depth, the higher the sediment density carried by the water flow. The evaluation at this stage helps to clarify the distribution characteristics of sediment in the water flow under different water depth conditions. Understanding these data helps to predict the degree of erosion or wear suffered by different depths of the dam body when the water level is high. Especially when the sediment content is high, the scouring effect of the water flow on the dam body will be more significant, thus affecting the long-term stability of the dam body. Perform a layered pressure water flow impact simulation calculation for the concrete dam structure design data based on the sediment density data between different water depths and the river channel flow velocity and flow rate gradient data during the rainy season to obtain the layered pressure water flow impact data. This step combines the changes in the flow velocity, flow rate, sediment concentration, and water depth of the water flow to perform a pressure water flow impact simulation for the concrete dam body at different water depths, and evaluate the impact of different water depths on the dam body structure. The simulation can reveal how the water flow generates different levels of impact force on the dam body under actual flow and flow velocity conditions, helping designers to identify potential weak links of the dam body and strengthen them specifically during the design or maintenance process to enhance the anti-scouring and anti-fatigue capabilities of the dam body under complex water flow conditions.

[0010] Preferably, step S24 includes the following steps: Step S241: Calculate the high and low drop of the concrete dam structure design data to obtain the high and low drop data of the concrete dam; Step S242: Calculate the average difference of the earth dam drop thick wall for the concrete dam structure design data based on the high and low drop data of the concrete dam to obtain the average difference data of the earth dam drop thick wall; Step S243: Evaluate the hydrodynamic differences at different water depths based on the sediment concentration density data and the river channel rainy season flow velocity gradient data between different water depths, and obtain the hydrodynamic difference data at different water depths; Step S244: Calculate the sand grain friction degree between different water depths according to the sediment concentration density data between different water depths and the hydrodynamic difference data at different water depths, and obtain the sand grain friction degree data between different water depths; Step S245: Calculate the force load of the stratified wall pressure flow field at different drop depths for the soil dam drop thick wall average difference data according to the sand grain friction degree data between different water depths and the hydrodynamic difference data at different water depths, and obtain the force load data of the stratified wall pressure flow field; Step S246: Conduct a simulation calculation of the stratified pressure water flow impact between different water depths based on the force load data of the stratified wall pressure flow field, and obtain the stratified pressure water flow impact data.

[0011] The present invention calculates the high and low drop of the concrete dam structure design data to obtain the high and low drop data of the concrete dam. By calculating the high and low drop of the dam body, the water level change under the action of water flow and the pressure distribution at different positions inside the dam body can be accurately identified. The high and low drop is one of the key factors for evaluating the force of water flow on the dam body, which can help determine the impact intensity of water flow on the dam body during the process from upstream to downstream, and provide an important basis for subsequent structural design and water flow impact simulation. In addition, the high and low drop of the dam body has a direct impact on the stability and anti-scouring ability of the dam body. Therefore, this step helps to comprehensively understand the pressure risk faced by the dam body. According to the high and low drop data of the concrete dam, the earth dam drop thick wall average difference calculation is carried out on the concrete dam structure design data to obtain the earth dam drop thick wall average difference data. This calculation takes into account the dam body thickness, the structure of the earth dam, and the stress distribution under different drop conditions, and can provide a more refined load distribution prediction for the dam body. The earth dam drop thick wall average difference reflects the thickness change of the dam body under different depth drop conditions and the corresponding structural strength, helps to identify the weak areas of the dam body, optimize the dam body design and protection measures, and improve the anti-water pressure impact ability and stability of the dam body. Based on the sediment concentration density data between different water depths and the river channel rainy season flow velocity gradient data, the hydrodynamic difference evaluation at different water depths is carried out to obtain the hydrodynamic difference data at different water depths. The hydrodynamic difference evaluation can reveal the velocity, pressure, and dynamic changes of water flow at different water depths, and further help to understand the different action mechanisms of water flow at different depths of the dam body. By evaluating the dynamic characteristics of water flow at different water depths, the impact intensity of water flow on each part of the dam body can be predicted under different flow velocity and flow rate conditions, thus providing more accurate parameter support for water flow impact simulation. According to the sediment concentration density data between different water depths and the hydrodynamic difference data at different water depths, the sand grain friction degree calculation between different water depths is carried out to obtain the sand grain friction degree data between different water depths. The calculation of the sand grain friction degree can reflect the movement state of sediment in water flow at different water depths, as well as the friction and impact effects of sediment on the dam body surface. The scouring effect of high sediment concentration water flow on the dam body is particularly significant. Understanding the friction force size between sand grains and the dam body under different water depth conditions helps to predict the wear and structural fatigue of the dam body under long-term water flow scouring, and further guides the anti-wear design and maintenance strategy of the dam body. According to the sand grain friction degree data between different water depths and the hydrodynamic difference data at different water depths, the layered wall pressure flow field force load calculation between different drop depths is carried out on the earth dam drop thick wall average difference data to obtain the layered wall pressure flow field force load data. Through this calculation, how water flow generates different force patterns on the dam body surface under different water depth and drop conditions can be analyzed. The layered wall pressure flow field force load data can provide the pressure and stress distribution borne by each layer structure of the dam body under different water depths and different sediment concentrations, which helps to more accurately evaluate the safety of the dam body under extreme water flow conditions and provides a basis for subsequent structural reinforcement and risk assessment.Based on the force load data of the stratified wall pressure flow field, the simulation calculation of the stratified pressure water flow impact between different water depths is carried out to obtain the stratified pressure water flow impact data. By simulating the water flow impact at different water depths, the influence degree of the water flow on different depths of the dam body during the rainy season or flood can be accurately evaluated. This simulation takes into account factors such as the pressure, velocity, sediment concentration, and friction of the water flow, and can provide the structural response data of the dam body under different water flow conditions, helping to optimize the dam design and ensure its safety under various hydrological conditions. At the same time, the results of this step can provide quantitative support for the formulation of dam maintenance and reinforcement strategies, reducing the potential damage risk caused by the water flow.

[0012] Preferably, the calculation of the sand grain friction degree between different water depths is carried out according to the sediment concentration density data between different water depths and the hydrodynamic difference data of different water depths through the sand grain friction degree calculation formula, and the sand grain friction degree calculation formula is as follows: ; In the formula, represents the result value of the sand grain friction degree, represents the sediment concentration between different water depth depths, represents the pressure coefficient between different water depth depths, represents the water flow velocity between different water depth depths, represents the time coefficient, represents the density mean difference of the sediment concentration density data between different water depths, represents the dynamic difference mean difference of the hydrodynamic difference data of different water depths, represents the error correction value of the sand grain friction degree calculation formula.

[0013] The present invention constructs a sand grain friction degree calculation formula, which can effectively predict the sand grain friction degree under different water depth conditions by comprehensively considering various factors such as sediment concentration, pressure, and water flow velocity. This has important application value in the fields of water conservancy projects, river regulation, and ecological environment protection. The formula fully considers the sediment concentration between different water depth depths , and the sediment concentration is a key factor affecting the sand grain friction degree. It directly reflects the concentration of sand grains in the water body. A higher sediment concentration usually increases the friction force, thus affecting the deposition and erosion processes. By accurately measuring the sediment concentration at different water depths, more accurate data can be provided for the calculation, thereby improving the prediction ability of the model. The pressure coefficient between different water depth depths , and the pressure coefficient is used to describe the pressure change generated by the water body at different depths. It is proportional to the water depth. As the depth increases, the pressure increases, thus affecting the force of the water flow on the sand grains. Using this parameter helps to understand the influence of the water flow on the movement of sand grains and provides a more accurate physical model for the deposition and movement of sand grains. The water flow velocity between different water depth depths , the water flow velocity reflects the dynamic state of the water body and directly affects the movement and friction of sand grains. A higher flow velocity makes it easier for sand grains to be carried by the water flow, thereby reducing the degree of friction. By considering the flow velocity changes at different water depths, the behavior of sand grains in the water flow can be analyzed more comprehensively, providing a scientific basis for soil and water conservation and river regulation. The time coefficient , is used to describe the dynamic process of sand grain friction changing over time. It can help analyze the variation law of sand grains in the water flow over time. Especially in the case of drastic water flow changes, the introduction of the time factor can better reflect the instantaneous friction situation. This is of great significance for evaluating the deposition and erosion behavior of sand grains within a specific time. The density average difference of the sediment concentration density data between different water depths , the density average difference measures the degree of change in sediment concentration at different water depths and can reflect the non-uniformity of sand grain distribution in the water body. By calculating the density average difference, the aggregation areas of sand grains in the water flow can be identified, providing data support for studying riverbed evolution and ecological impacts. The dynamic difference average difference of the hydrodynamic difference data at different water depths , the dynamic difference average difference is used to describe the change in hydrodynamic characteristics at different water depths and can reveal the impact of water flow on sand grain friction. The accuracy of this parameter is crucial for understanding how sand grains move at different water depths and flow velocities, and helps optimize river regulation and sand management strategies. The error correction value of the sand grain friction degree calculation formula , the error correction value is a parameter used to adjust the calculation results to ensure that the model can more accurately reflect the actual situation. Due to the uncertainty in the measurement process, the introduction of the correction value can improve the reliability of the model, making the final calculation results more in line with the actual environmental conditions.

[0014] Preferably, calculating the force load of the pressure flow field on the layered wall surface between different drop depths of the earth dam drop thick wall difference data based on the sand grain friction degree data between different water depths and the hydrodynamic difference data at different water depths includes the following steps: Evaluating the thick wall pressure profile of the earth dam drop thick wall difference data between different drop depths based on the hydrodynamic difference data at different water depths to obtain the thick wall pressure profile partition data; Deriving the flow direction vector of the partition pressure profile based on the sand grain friction degree data between different water depths for the thick wall pressure profile partition data to obtain the flow direction vector of the partition pressure profile; Conducting multi-angle non-linear interference analysis on the flow direction vector of the partition pressure profile to obtain the multi-angle interference data of the profile vector; Fitting the pressure density space gradient to the flow direction vector of the partition pressure profile according to the multi-angle interference data of the profile vector to obtain the pressure density space gradient fitting data; Based on the multi - angle interference data of the profile vector and the fitted data of the pressure - density spatial gradient, the force load calculation of the layered wall pressure flow field for the differential data of the drop - height and thick - wall of the earth dam is carried out between different drop - height depths, and the force load data of the layered wall pressure flow field are obtained.

[0015] The present invention can accurately identify the force distribution characteristics by evaluating the thick - wall pressure profiles at different drop - height depths. This process helps to understand the stability of the earth dam under different water flow conditions and ensure the structural safety of the dam body. During the design and maintenance process, the accurate evaluation of the thick - wall pressure profile can provide important decision - making basis for engineers. The derivation of the flow - direction vector based on the sand - grain friction degree data can provide the dynamic distribution of the water flow on the dam surface. This derivation reveals how the water flow interacts with the dam body, affects the force characteristics, and thus provides a basis for the structural design and anti - erosion measures of the dam body. The multi - angle non - linear interference analysis of the flow - direction vector can reveal the mutual influence of different flow patterns in the complex flow field. This analysis method helps to understand the non - linear effects in fluid dynamics, provides support for more accurate prediction of water flow behavior, and ensures the safety of the dam design. The fitting of the pressure - density spatial gradient is an important step in understanding the flow - field changes. Through fitting, the changing trend of the pressure distribution in the water flow can be identified, thus providing more detailed data support for the force analysis of the dam body. This helps to optimize the design of the dam body and reduce the potential risk of damage. Finally, by integrating the data of each step to calculate the force load of the layered wall pressure flow field, the force characteristics of the dam body under different water flow conditions can be directly reflected. The result of this calculation is crucial for engineering design and risk assessment, ensuring the stability and safety of the dam body under extreme conditions and avoiding potential disasters.

[0016] Preferably, step S3 includes the following steps: Step S31: Construct a stress - intensity thermal diagram for the concrete dam structural design data between different water depths based on the layered pressure water flow impact data, and obtain the water - flow impact stress - intensity thermal diagram; Step S32: Analyze the fatigue cumulative effect of the key areas of the concrete dam structural design data based on the water - flow impact stress - intensity thermal diagram, and obtain the fatigue cumulative effect data of the key areas; Step S33: Estimate the fatigue limit load intensity for the fatigue cumulative effect data of the key areas, and obtain the fatigue limit load intensity estimation data.

[0017] The present invention constructs a stress intensity thermal diagram between different water depths for the concrete dam structure design data based on the impact data of stratified pressure water flow, and obtains the stress intensity thermal diagram of water flow impact. Through this step, the influence of water flow impact on the dam surface can be visualized, and the stress distribution of water flow on the concrete dam under different water depth conditions can be accurately presented. The stress intensity thermal diagram provides the maximum stress levels borne by each area of the dam at different depths and flow velocities, enabling designers to intuitively identify the areas of the dam that are most vulnerable to damage. This can provide accurate basis for subsequent fatigue analysis and reinforcement plan design, and effectively predict the failure areas of the dam under extreme water flow conditions, and take protective measures in advance to ensure the long-term safety of the dam. Based on the stress intensity thermal diagram of water flow impact, the fatigue cumulative effect analysis of the key areas of the concrete dam structure design data is carried out to obtain the fatigue cumulative effect data of the key areas. By analyzing the fatigue cumulative effect of the key areas of the dam, the fatigue damage of the dam under long-term water flow impact can be evaluated. Water flow impact will generate periodic stress on the dam material, resulting in structural fatigue damage such as cracks and spalling. By analyzing the cumulative effect of these fatigue damages, the fatigue life and potential failure points of the dam can be predicted, helping engineers identify key weaknesses and reinforce in advance, so as to avoid sudden structural failure. This step provides a scientific basis for the long-term stability assessment and maintenance plan formulation of the dam. The fatigue limit load intensity is estimated for the fatigue cumulative effect data of the key areas to obtain the fatigue limit load intensity estimation data. According to the fatigue cumulative effect data, the maximum load intensity borne by the key areas of the dam under extreme conditions and the fatigue limit under this load can be further estimated. This estimation result helps to clarify the failure limit of the dam in the face of continuous water flow impact or other external loads, and helps to judge the safety margin of the dam during long-term use. Through accurate fatigue limit estimation, specific guidance can be provided for the design improvement and maintenance of the dam, and catastrophic consequences caused by overloading of the dam can be avoided. This analysis is of great significance for the life prediction, repair cycle and safety assessment of the dam.

[0018] Preferably, step S32 includes the following steps: Step S321: Conduct regional block analysis on the stress intensity thermal diagram of water flow impact to obtain local impact stress distribution data; Step S322: Calculate the mean variance of the local impact stress distribution data to obtain local stress mean variance data; Step S323: Conduct layered analysis of the stress influence range on the local impact stress distribution data according to the local stress mean variance data to obtain layered data of the stress influence range; Step S324: Evaluate the failure probability of the key areas for the layered data of the stress influence range to obtain the fatigue failure probability data of the key areas; Step S325: Conduct fatigue cumulative effect analysis on the concrete dam structure design data based on the fatigue failure probability data of the key area and the mean variance data of the local stress to obtain the fatigue cumulative effect data of the key area.

[0019] The present invention conducts regional block analysis on the thermal stress intensity map of water flow impact to obtain local impact stress distribution data. By dividing the thermal stress intensity map of water flow impact into several regions for analysis, the local stress distribution borne by the dam surface at different positions can be accurately understood. The stress characteristics of each region help identify the weaknesses of the dam and the regions with high stress concentration, thereby providing basic data for further fatigue analysis and design optimization. This local analysis method enables a more detailed assessment of the stress state of the dam and can provide targeted guidance for subsequent structural reinforcement or maintenance measures. Calculate the mean and variance of the local impact stress distribution data to obtain the local stress mean and variance data. The calculation of the mean and variance helps quantify the stress fluctuation in the local area and reveals the stability of the stress distribution in each region. By calculating the mean and variance, the degree of stress concentration and the uniformity of the stress distribution can be determined. This step can help designers identify which regions have large stress changes and then predict the fatigue damage trend in these regions. By understanding the mean and variance of the local stress, it can provide a quantitative basis for further risk assessment and fatigue life prediction. Conduct stress influence range stratification analysis on the local impact stress distribution data according to the local stress mean and variance data to obtain stress influence range stratification data. Through the stratification analysis of the local stress distribution, the stress influence range of different levels can be accurately delimited, revealing the influence depth and range of water flow impact on the dam at different depths and in different regions. This step helps quantify the specific influence range of water flow on the dam and effectively identify the key levels with large surface stress and prone to damage on the dam. This stratification analysis can help decision-makers more accurately evaluate the potential failure risks in different regions of the dam and optimize the design and maintenance strategies. Conduct critical area failure probability assessment on the stress influence range stratification data to obtain critical area fatigue failure probability data. According to the results of the stratification analysis of the stress influence range, the fatigue failure probability of the critical areas of the dam under specific water flow conditions can be further evaluated. Through statistical and probability analysis methods, evaluate which regions of the dam are most likely to fail under long-term water flow impact. The evaluation results can reveal the potential risks in different regions, help engineers identify in advance the high-risk regions where cracks, spalling or other failure forms may occur, and provide a scientific basis for dam reinforcement or design adjustment, thereby ensuring the safety of the dam during long-term operation. Conduct critical area fatigue cumulative effect analysis on the concrete dam structure design data according to the critical area fatigue failure probability data and the local stress mean and variance data to obtain critical area fatigue cumulative effect data. By combining the fatigue failure probability data and the local stress mean and variance data, the critical areas of the dam can be analyzed for fatigue cumulative effect, quantifying the overall fatigue damage degree of the dam under long-term water flow impact. This analysis can reveal the fatigue accumulation of the dam at different usage stages, providing a basis for evaluating the service life of the dam, formulating maintenance plans and predicting the structural damage of critical areas.According to the fatigue cumulative effect data, optimization suggestions can be provided for the design of the dam structure to enhance the anti-fatigue performance of the dam and improve its safety.

[0020] Preferably, step S33 includes the following steps: Step S331: Simulate the dynamic microcrack propagation path of the concrete dam structure design data based on the fatigue cumulative effect data of the key area to obtain the predicted data of the microcrack propagation path; Step S332: Conduct a non-linear response analysis based on the predicted data of the microcrack propagation path to obtain the non-linear fatigue response data; Step S333: Identify the fatigue failure critical point of the non-linear fatigue response data to obtain the fatigue failure critical point data; Step S334: Estimate the fatigue limit load intensity based on the fatigue failure critical point data and the non-linear fatigue response data to obtain the estimated data of the fatigue limit load intensity.

[0021] The present invention simulates the dynamic microcrack propagation path of the concrete dam structure design data based on the fatigue cumulative effect data of the key area, and obtains the predicted data of the microcrack propagation path. By simulating the dynamic microcrack propagation path of the fatigue cumulative effect data of the dam body key area, the propagation path and development trend of microcracks under the long-term water flow impact can be predicted. This simulation can reveal the potential propagation direction, speed and cumulative area of microcracks, providing key early warning information for designers. By predicting the microcrack propagation path, the tiny cracks that cause structural damage can be identified early, and then targeted maintenance and reinforcement strategies can be formulated in advance, thus effectively avoiding structural failure caused by crack propagation. According to the predicted data of the microcrack propagation path, a nonlinear response analysis is carried out to obtain the nonlinear fatigue response data. The nonlinear response analysis can further consider the nonlinear behavior of the dam body material, such as the influence of factors such as crack propagation, plastic deformation, and stress concentration on fatigue damage. By combining the prediction of the microcrack propagation path with the nonlinear response, the stress and deformation conditions of the dam body under actual working conditions can be accurately simulated, and then more accurate fatigue response data can be obtained. This analysis helps to reveal the true stress state of the dam body under complex loads such as water flow impact, and identify the potential threats of nonlinear behavior to structural safety, providing a basis for subsequent structural evaluation and reinforcement. The fatigue failure critical point is identified from the nonlinear fatigue response data to obtain the fatigue failure critical point data. By analyzing the nonlinear fatigue response data, the critical points of fatigue failure of the dam body after experiencing multiple water flow impacts can be identified. These critical points are the areas or conditions where the dam body structure is most likely to fail under repeated loads. Identifying these key fatigue failure critical points helps engineers understand the weak links of the dam body and take reinforcement or repair measures in advance, thus avoiding sudden failure accidents. This step provides an important basis for the risk assessment, life prediction and maintenance plan formulation of the dam body. Based on the fatigue failure critical point data and the nonlinear fatigue response data, the fatigue limit load intensity is estimated to obtain the estimated data of the fatigue limit load intensity. By combining the fatigue failure critical point data and the nonlinear fatigue response data, the fatigue limit load of the dam body can be accurately estimated. The result of this estimation can clarify the maximum load intensity that the dam body can withstand during long-term use, and the critical conditions for the dam body to undergo fatigue failure at this intensity. By estimating the fatigue limit load intensity, a quantitative basis can be provided for the safety assessment of the dam body, helping engineers design more reasonable structural reinforcement schemes, extend the service life of the dam body and improve its fatigue resistance, thus ensuring the long-term stable operation of the dam body.

[0022] Preferably, step S4 includes the following steps: Step S41: Normalize the estimated data of the fatigue limit load intensity to obtain the normalized data of the fatigue limit load intensity; Step S42: Perform flood risk level prediction on the normalized data of the fatigue limit load intensity based on the policy gradient algorithm to obtain flood risk level data; Step S43: Construct a risk assessment system for the flood risk level data to obtain a flood risk assessment system.

[0023] The present invention performs normalization processing on the estimated data of the fatigue limit load intensity to obtain the normalized data of the fatigue limit load intensity. The normalization processing can convert the fatigue limit load intensity data with different dimensions into the same standard scale, making these data more comparable in subsequent analyses. Through normalization, the dimension differences and scale effects in the original data can be eliminated, enabling the fatigue limit load intensity data of different dams or regions to be compared at the same level. This step helps to uniformly measure the fatigue load capacity of each region, laying a foundation for subsequent risk assessment and model analysis, and ensuring the consistency and accuracy of data processing. Perform flood risk level prediction on the normalized data of the fatigue limit load intensity based on the policy gradient algorithm to obtain flood risk level data. The policy gradient algorithm is an optimization method based on reinforcement learning, which continuously adjusts the policy to minimize or maximize a certain objective function. In this step, the policy gradient algorithm can predict the risk level of the dam structure under different flood conditions according to the normalized fatigue limit load intensity data. Through this algorithm, the impacts of multiple factors (such as water flow, dam fatigue status, historical load, etc.) on the flood risk can be considered to obtain more accurate flood risk level data. This prediction can help identify potential risk areas, thus providing timely warnings for decision-makers and optimizing flood control strategies. Construct a risk assessment system for the flood risk level data to obtain a flood risk assessment system. By analyzing the flood risk level data, a comprehensive risk assessment system can be established to quantify the risk levels of different dams or regions under different flood conditions. The risk assessment system usually includes multiple dimensions, such as structural safety, flood frequency, dam fatigue status, etc., comprehensively considering various factors to form a multi-level and multi-dimensional evaluation framework. This system helps to systematically evaluate the risks of dams under different situations and provides a scientific basis for managers to help formulate targeted flood prevention and emergency response measures. Through the risk assessment system, the resource allocation and risk control can be effectively optimized to ensure the safety and long-term stability of the dam structure.

[0024] The beneficial effects of the present invention are as follows: obtaining the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam, and conducting a velocity gradient analysis, which helps to deeply understand the hydrological characteristics of the upstream river channel during the rainy season, especially the changes in the flow rate velocity gradient. By analyzing these data, the spatial distribution and change trend of the river channel velocity can be identified, providing accurate basic data for subsequent water flow impact simulation to ensure that the impact on the dam body can be more realistically simulated. By calculating the water depth increment rate of the river channel flow rate velocity gradient data during the rainy season, the rate of change of the river channel water level under different flow rates can be evaluated, and further, based on this data, a layered pressure water flow impact simulation of different water depths of the concrete dam can be carried out. This step can simulate the different pressures borne by the dam body under complex water flow conditions during the rainy season and reveal the impact effect of the water flow at different depths of the dam body, thus providing key information for the structural safety assessment of the dam body. Using the layered pressure water flow impact data to estimate the fatigue limit load strength can reveal the fatigue performance of the concrete dam under long-term water flow impact and predict its fatigue failure risk under extreme conditions. This step provides data support for the health management and maintenance cycle planning of the concrete dam, helps to identify potential structural weaknesses in advance, and enhances the long-term stability and safety of the dam body. By predicting the flood risk level based on the fatigue limit load strength data, the safety of the dam body under different flood conditions can be evaluated, and a scientific risk prevention and control strategy can be formulated. Combining the construction of the risk assessment system, the state of the dam body with different risk levels can be quantitatively evaluated, providing a clear risk level judgment and response measures for decision-makers to ensure the safe operation of water conservancy facilities and reduce the potential hazards brought by floods. Therefore, the present invention is an optimized treatment of the traditional method for constructing the flood risk assessment index system of a concrete dam, solving the problems of inaccurate analysis of the ultimate load strength of the dam body and large prediction errors of the flood risk level existing in the traditional method for constructing the flood risk assessment index system of a concrete dam, improving the accuracy of the analysis of the ultimate load strength of the dam body, and reducing the problem of large prediction errors of the flood risk level. Brief Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the step flow of a method for constructing a flood risk assessment index system of a concrete dam; Figure 2 is Figure 1 a detailed implementation step flow schematic diagram of step S2 in

[0026] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed Embodiments

[0027] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present invention without creative work belong to the scope of protection of the present invention.

[0028] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions of them will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0029] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0030] To achieve the above object, please refer to Figures 1 to 2 , a method for constructing an evaluation index system for the flood risk of a concrete dam, the method comprising the following steps: Step S1: Obtain the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam; perform a velocity gradient analysis on the river channel data in the upstream area of the earth dam to obtain the river channel rainy season flow velocity gradient data; Step S2: Calculate the water depth increment rate for the river channel rainy season flow velocity gradient data to obtain the river channel water depth increment rate data; perform a stratified pressure water flow impact simulation calculation between different water depths on the concrete dam structural design data based on the river channel rainy season flow velocity gradient data to obtain the stratified pressure water flow impact data; Step S3: Estimate the fatigue limit load strength of the concrete dam structural design data based on the stratified pressure water flow impact data to obtain the fatigue limit load strength estimation data; Step S4: Predict the flood risk level for the fatigue limit load strength estimation data to obtain the flood risk level data; construct a risk evaluation system for the flood risk level data to obtain the flood risk evaluation system.

[0031] In the embodiments of the present invention, refer to Figure 1As shown in the figure, it is a schematic diagram of the step flow of a method for constructing an index system for flood risk assessment of a concrete dam. In this example, the method for constructing an index system for flood risk assessment of a concrete dam includes the following steps: Step S1: Obtain the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam; perform a velocity gradient analysis on the river channel data in the upstream area of the earth dam to obtain the river channel rainy season flow velocity gradient data; In the embodiment of the present invention, the structural design data of the concrete dam is collected, including the geometric dimensions of the dam body (such as dam height, dam width, crest water level, etc.), the material properties of the dam body (such as concrete strength grade, elastic modulus, etc.), and the soil properties of the dam foundation (such as bearing capacity, soil type, etc.). These data can be obtained through construction drawings, design plans, and on-site surveys. Accordingly, the river channel data in the upstream area of the earth dam is obtained, including the cross-section of the river bed, the precipitation in the basin, the historical hydrological data of the river, etc. By docking with the data of local hydrological monitoring stations, basic data such as river channel flow, velocity, and rainfall are extracted. Then, a velocity gradient analysis is performed on the river channel data in the upstream area of the earth dam. The velocity gradient analysis is to calculate the change rate of the water flow velocity of the river cross-section under different water levels to obtain the change trend of the water flow in different river sections. Using empirical formulas or through on-site velocity measurement data, based on continuous hydrological stations or velocity detection devices, the water flow rates at different positions are measured section by section. Then, based on the measured velocity data, a numerical integration method is used to calculate the velocity gradient to obtain the river channel rainy season flow velocity gradient data. This data reflects the change and its gradient of the velocity under different water depth conditions in the river channel and can provide a basis for subsequent water flow simulation.

[0032] Step S2: Calculate the water depth increment rate for the river channel rainy season flow velocity gradient data to obtain the river channel water depth increment rate data; perform a stratified pressure water flow impact simulation calculation on the structural design data of the concrete dam based on the river channel rainy season flow velocity gradient data to obtain the stratified pressure water flow impact data; In the embodiments of the present invention, the water depth increment rate is calculated by using the river channel rainy season flow velocity gradient data obtained in step S1. The calculation of the water depth increment rate is based on the dynamic characteristics of the water flow and the cross-sectional shape of the river channel. First, according to the velocity gradient and flow rate, combined with the geometric parameters of the river channel, hydraulic formulas (such as Manning's formula, dimensional analysis method, etc.) are used to estimate the flow conditions of the water flow at different water levels. For each different water level, the flow rate change of the water flow at this water level is calculated, and then by comparing the water depth changes at different water levels, the water depth increment rate is obtained. Subsequently, the water depth increment of each cross-section in the river channel is combined with the velocity gradient to calculate the water depth increment rate. Specifically, by calculating section by section how the water flow velocity changes when the water level rises, the rate of change of the water flow velocity when the water depth increases is deduced. During the calculation process, the integral method needs to be used to accumulate each section of the water flow to obtain the growth rate information at each water depth. The obtained water depth increment rate data can be used to simulate the impact effect of the pressure water flow on the concrete dam, and further provide an accurate numerical basis for the subsequent steps. In this process, the core calculation method used is based on the relationship between the water flow rate and the water depth change in fluid mechanics, and the influence relationship of the velocity gradient on the water depth increment rate, and the accurate difference method or finite element method is used for numerical solution to ensure the accuracy and applicability of the results.

[0033] Step S3: Estimate the fatigue limit load strength of the concrete dam structure design data according to the stratified pressure water flow impact data to obtain the fatigue limit load strength estimation data; In the embodiments of the present invention, according to the river channel water depth increment rate data and the stratified pressure water flow impact data in step S2, the fatigue limit load strength of the concrete dam structure is estimated. First, the stratified pressure water flow impact data is used to analyze the pressure action on the dam body under different water depths and flow velocities. Specifically, according to different water flow depths and velocities, corresponding water flow pressures are applied at different positions of the concrete dam body (such as the bottom of the dam body, the dam slope surface, the dam top, etc.). Then, by inputting these pressure data into the structural analysis, the stress distribution of each part of the dam body when it is impacted by the water flow is calculated by using static and dynamic analysis methods. On this basis, the fatigue analysis theory is adopted to evaluate the influence of the repeated loads borne by the dam body under different water flow impact conditions. The fatigue strength estimation is to calculate the stress concentration of the key areas of the dam body (such as the bottom contact surface of the dam body, the dam slope surface, etc.) under repeated water flow impacts, and then deduce the fatigue life and limit load strength of this area. When specifically implemented, the mine fatigue theory or the S-N curve (stress-life curve) is used, combined with the stress state of each area for estimation. The fatigue strength estimation needs to adopt the fatigue life prediction formula according to the stress level, load cycle times and impact frequency of the water flow at each position, and finally obtain the fatigue limit load strength data of the concrete dam. Through these data, the fatigue failure of the dam body under specific conditions can be predicted, and potential risks can be identified in advance.

[0034] Step S4: Predict the flood risk level for the estimated data of the fatigue limit load intensity to obtain flood risk level data; construct a risk assessment system for the flood risk level data to obtain a flood risk assessment system.

[0035] In the embodiment of the present invention, after obtaining the estimated data of the fatigue limit load intensity of the concrete dam, the flood risk level is predicted. The flood risk level prediction is based on a multi-factor analysis method, comprehensively considering factors such as the fatigue limit load intensity of the dam body, historical water flow data, the changing trend of the rainy season flow rate, and the aging degree of the dam body for evaluation. Specifically, the analytic hierarchy process (AHP) or the weighted synthesis method is used to assign weights to different risk factors, and combined with historical hydrological data, basin precipitation conditions, and flood warning information, the risk level is divided. During the prediction process of the flood risk level, first, according to the weights of each influencing factor, the comprehensive risk index under different conditions is calculated. Then, the comprehensive risk index is compared with the predetermined risk level classification standard to determine the current flood risk level faced by the dam body. Through this process, the risk assessment result of the dam body under specific flood conditions can be obtained. Subsequently, based on the flood risk level data, a risk assessment system is constructed. The construction of the risk assessment system includes two aspects: on the one hand, reasonable assessment criteria and indicators are set according to the predicted risk level; on the other hand, through the analysis of historical flood events, a set of risk level classification systems supported by data is constructed. This system can effectively classify and manage the flood risk of the dam body and provide a basis for relevant safety prevention measures.

[0036] Preferably, step S1 includes the following steps: Step S11: Obtain the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam; Step S12: Conduct a statistical analysis of the rainy season flow rate for the river channel data in the upstream area of the earth dam to obtain the upstream river channel rainy season flow rate data; Step S13: Conduct a velocity gradient analysis of the upstream river channel rainy season flow rate data to obtain the river channel rainy season flow rate velocity gradient data.

[0037] In the embodiments of the present invention, the structural design data of the concrete dam and the river channel data in the upstream area of the earth dam are obtained, which provide basic data for subsequent analysis. First, the structural design data of the concrete dam include information such as the geometric dimensions of the dam body, the construction materials of the dam body, and the soil and rock formation structures of the dam foundation. The geometric dimensions include indicators such as the dam height, the water level at the dam crest, and the width of the dam foundation. These data can usually be obtained from construction drawings or design documents. The material properties of concrete, such as the strength grade, elastic modulus, and density, need to refer to design specifications and actual test data. For the river channel data in the upstream area of the earth dam, first, the topographic information of the river channel is collected, including the cross-section of the riverbed, the longitudinal slope of the river channel, and the precipitation and historical water flow data of the basin. These information can be obtained through on-site surveys, data from historical hydrological monitoring stations, and satellite remote sensing images. By combining on-site measurement data, the dynamic information such as the flow velocity and flow rate of the river channel is obtained, providing basic data on the flow state of the river channel for subsequent steps. When obtaining these data, a standardized data acquisition process is usually adopted to ensure the accuracy and integrity of the data. Statistical analysis of the rainy season flow rate data of the river channel in the upstream area of the earth dam is carried out to obtain the flow rate data of the upstream river channel during the rainy season. First, based on the river channel data collected in step S11, a time range is selected, usually the rainy seasons in the past three to five years (for example, from June to September each year). Through the flow rate data provided by hydrological monitoring stations, combined with information such as precipitation and meteorological records, data cleaning and screening are carried out. Then, statistical analysis methods are used to process these rainy season flow rate data. Specifically, by calculating the flow rate data of different time periods in each basin, the changing trend of the flow rate during the rainy season is obtained. In order to analyze the volatility and regularity of the flow rate, time series analysis methods are used to gradually count the daily and monthly flow rates to determine indicators such as the maximum value, minimum value, average value, and standard deviation of the flow rate. These flow rate statistical data can reflect the flood peak and low water level conditions of the river during the rainy season. By calculating the rainy season flow rate data of the upstream river channel, the extreme flow rate changes during the rainy season are focused on, and the peak and trough periods of the flow rate are marked. These data provide specific flow rate data support for subsequent water flow gradient analysis, dam body impact simulation, etc. During the process of flow rate statistics, the main statistical methods adopted include mean calculation, variance analysis, and trend prediction to ensure the accuracy and representativeness of the results. Velocity gradient analysis of the rainy season flow rate data of the upstream river channel is carried out to obtain the velocity gradient data of the rainy season flow rate of the river channel. Velocity gradient analysis is to calculate the gradient relationship between different water depths and water flow velocities according to the cross-sectional shape of the river channel, the changes in flow rate and flow velocity. In the previous step, the rainy season flow rate data of the upstream river channel have been obtained. Therefore, further analysis needs to be carried out through physical formulas and the principles of hydrodynamics. According to the known river channel cross-section data (such as the width, depth, and slope of the river channel), basic formulas of fluid mechanics (such as Manning's formula, velocity distribution formula, etc.) are used for calculation.These formulas can provide the quantitative relationship between the water flow velocity and the flow rate, especially the velocity changes under different water levels. Secondly, by combining the flow rate data and the cross-section of the river channel, the differential method or the finite difference method is used to analyze the velocity gradient. Specifically, first, several typical flow rate values are selected, and then, according to the variation of the water flow velocity at different water depths, the velocity gradient is calculated. This process can be carried out through the simulation of the water flow model or directly by calculating the difference of the measured flow velocities, calculating the variation relationship between the flow velocity and the water depth, so as to obtain the velocity gradient data. The velocity gradient refers to the change in the flow velocity caused by the change in the unit water depth, and it is an important index of the hydrodynamic characteristics of the river channel flow. Through the velocity gradient data, the acceleration or deceleration process of the water flow under different water levels can be analyzed. These data can not only reflect the impact of the water flow on the dam structure, but also provide a basis for the subsequent simulation of the water flow impact and the structural analysis. In this process, the numerical solution method is used to accurately calculate the velocity gradient. Especially considering the non-uniformity of the river channel and the volatility of the flow rate, the velocity gradient analysis must cover the diversity of different time periods, different water levels, and different flow rates to ensure the comprehensiveness and accuracy of the analysis results.

[0038] Preferably, step S2 includes the following steps: Step S21: Evaluate the sediment concentration carried by the river for the river channel data in the upstream area of the earth dam according to the velocity gradient data of the river channel during the rainy season flow rate, and obtain the sediment concentration evaluation data carried by the river; Step S22: Calculate the water depth increment rate for the velocity gradient data of the river channel during the rainy season flow rate, and obtain the river channel water depth increment rate data; Step S23: Evaluate the sediment density between different water depths for the sediment concentration evaluation data carried by the river according to the river channel water depth increment rate data, and obtain the sediment density data between different water depths; Step S24: Based on the sediment density data between different water depths and the velocity gradient data of the river channel during the rainy season flow rate, perform a layered pressure water flow impact simulation calculation on the concrete dam structure design data, and obtain the layered pressure water flow impact data.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Evaluate the sediment concentration carried by the river for the river channel data in the upstream area of the earth dam according to the velocity gradient data of the river channel during the rainy season flow rate, and obtain the sediment concentration evaluation data carried by the river; In the embodiments of the present invention, the flow rate and velocity gradient data of the river channel during the rainy season are used to evaluate the sediment-carrying capacity of the river in the upstream area of the earth dam. First, based on the river channel flow rate and velocity gradient data obtained in steps S12 and S13, combined with the cross-sectional terrain data of the river channel, the water flow conditions of the river during the rainy season are simulated and analyzed. The evaluation process is based on hydraulics and sediment transport theories, especially the incipient velocity of sediment and the scouring ability of water flow. The incipient velocity of sediment refers to the flow velocity at which the water flow has sufficient energy to start scouring and moving the sediment. This formula calculates the minimum flow velocity required for sediment incipience through the velocity of the water flow and the density difference of the sediment particles. The specific formula is: incipient velocity of sediment = (the ratio of the density of sediment particles minus the density of water to the density of water) multiplied by the square root of (the ratio of the diameter of sediment particles to the flow velocity). This formula indicates the flow velocity at which sediment particles can start to move under the scouring of the water flow. The greater the flow velocity, the more sediment the water flow can carry. Through this formula, the conditions for sediment particles to start moving in the water flow when the water flow reaches a certain velocity can be calculated.

[0040] Step S22: Calculate the water depth increment rate for the flow rate and velocity gradient data of the river channel during the rainy season to obtain the water depth increment rate data of the river channel; In the embodiments of the present invention, the increment rate of the water depth of the river channel is calculated based on the flow rate and velocity gradient data of the river channel during the rainy season. In actual operation, it is necessary to first use the cross-sectional data of the river channel and the flow rate data during the rainy season to deduce the rate of change of water depth over time based on fluid mechanics theory. The specific calculation method can adopt the continuity equation and the hydrodynamic equation, combined with local water flow conditions for deduction. First, based on the known velocity gradient data, the change in water depth under different flow velocity conditions can be determined through the flow rate-water depth relationship formula. For example, by simulating the water flow using the Manning formula, the change in the water depth of the river channel under different flow rates and flow velocities can be obtained. The relationship between the flow rate and water depth calculated by this formula can be further used to deduce the increment rate of water depth over time, that is, the change in water depth per unit time. By comparing the water depth change rates under different flow rate conditions, the water depth increment rate data of the river channel are obtained. These data provide a basis for subsequent water flow impact simulation.

[0041] Step S23: Evaluate the sediment concentration density between different water depths for the river sediment-carrying capacity evaluation data based on the water depth increment rate data of the river channel to obtain the sediment concentration density data between different water depths; In the embodiments of the present invention, based on the river channel water depth increment rate data, the sediment concentration density under different water depth conditions is evaluated. First, using the river channel water depth increment rate data obtained in step S22 and combining with the river sediment carrying capacity evaluation data obtained in step S21, a detailed analysis of the sediment transport capacity at different water depth levels is carried out. The river channel needs to be divided into several different water depth levels, and each level represents a specific water flow depth range. Then, according to the hydrodynamic characteristics of each level, the sediment concentration in that level is evaluated. Generally, the sediment concentration is proportional to the water depth, and in the area with higher water flow velocity, the sediment concentration will also increase accordingly. Therefore, according to the velocity gradient and the water depth increment rate, the sediment concentration under different water depth conditions can be calculated. Through the aforementioned velocity-water depth relationship, the water flow velocity of each water depth level is determined; then, combining with the relationship between the sediment concentration and the water flow velocity, the sediment concentration density of each water depth layer is obtained through experiments or historical data. For example, in the shallower water depth area, due to the faster water flow velocity, the sediment concentration is higher; while in the deeper water depth area, the water flow velocity is slower and the sediment concentration is lower. In this way, the sediment concentration density of different water depth levels is accurately evaluated and integrated into the "sediment concentration density data between different water depths".

[0042] Step S24: Based on the sediment concentration density data between different water depths and the river channel rainy season flow velocity gradient data, a stratified pressure water flow impact simulation calculation is carried out on the concrete dam structure design data to obtain stratified pressure water flow impact data.

[0043] In the embodiments of the present invention, according to the sediment concentration density data between different water depths and the river channel rainy season flow velocity gradient data, a stratified pressure water flow impact simulation calculation of the concrete dam structure is carried out. The key to this step is to evaluate the impact of the water flow and sediment on the dam structure by simulating the water pressure distribution under different water depth conditions. According to the sediment concentration density data of different water depth levels obtained in step S23 and combining with the velocity gradient data of the river channel, a stratified water flow simulation is carried out using a hydrodynamic model. The model needs to consider the interaction between the water flow and the sediment, especially the impact pressure of the water flow on the dam surface. Generally, the used water flow impact models include two-dimensional or three-dimensional flow field calculations based on the Navier-Stokes equation, combined with the simulation method of unsteady water flow. During the simulation process, according to different water depth levels, the impact force and sediment content of each layer of water flow are allocated. Through these parameters, the pressure distribution of each water depth level on the dam surface is calculated, and the wear and impact effects of the sediment on the dam during the flow process are evaluated. Through such simulation calculations, "stratified pressure water flow impact data" can be obtained, and further analyze the potential impact of the water flow on the dam under different water depths, flow velocities and sediment concentrations. This simulation calculation process relies on the basic theories and numerical simulation techniques in fluid mechanics to ensure that it can accurately reflect the dynamic changes of the river under different water depths and water flow conditions, and finally evaluate its impact on the concrete dam.

[0044] Preferably, step S24 includes the following steps: Step S241: Calculate the high and low drop of the concrete dam structure design data to obtain the high and low drop data of the concrete dam; Step S242: Calculate the earth dam drop thick wall average difference of the concrete dam structure design data according to the high and low drop data of the concrete dam to obtain the earth dam drop thick wall average difference data; Step S243: Evaluate the hydrodynamic differences at different water depths based on the sediment concentration density data and the river channel rainy season flow velocity gradient data between different water depths to obtain the hydrodynamic difference data at different water depths; Step S244: Calculate the sand grain friction degree between different water depths according to the sediment concentration density data and the hydrodynamic difference data between different water depths to obtain the sand grain friction degree data between different water depths; Step S245: Calculate the force load of the stratified wall pressure flow field between different drop depths on the earth dam drop thick wall average difference data according to the sand grain friction degree data between different water depths and the hydrodynamic difference data at different water depths to obtain the force load data of the stratified wall pressure flow field; Step S246: Conduct a simulation calculation of the stratified pressure water flow impact between different water depths based on the force load data of the stratified wall pressure flow field to obtain the stratified pressure water flow impact data.

[0045] In the embodiments of the present invention, first, it is necessary to obtain the basic structure design data of the concrete dam, especially the elevation data of the dam body and the relative height information of the bottom, top, and different positions of the dam body. Through these data, the height difference of the dam body can be calculated. The specific operations are as follows: Select key positions of the dam body, such as the starting end, center point, and end of the dam body, and extract the corresponding height data. Calculate the height differences between these points and further determine the height difference of the dam body. Usually, the differential calculation method is used to determine the elevation difference between any two points of the dam body. In this step, the thick-wall average difference calculation is performed on the earth dam part of the dam body using the height difference data of the concrete dam. First, the actual thickness data of the earth dam area needs to be obtained, which can be determined through the dam body design drawings and topographic exploration data. By combining the height difference data with the structural characteristics of the earth dam, the thick-wall average difference of the earth dam part of the dam body is calculated. Through the river channel rainy season flow velocity gradient data and the sediment concentration density data between different water depths, the differences in hydrodynamic forces at different water depths are evaluated, the flow rate and flow velocity data at different water depth levels are extracted, and combined with the river channel rainy season flow velocity gradient data, the hydrodynamic force differences at each water depth level are calculated. The calculated hydrodynamic force difference data is combined with the sediment concentration density data to analyze the influence of water flow on sediment concentration at different water depths, so as to evaluate the scouring characteristics of water flow at different water depths. The movement state of sand grains is calculated by analyzing the friction degree between the hydrodynamic forces and sediment at different water depth levels. The friction degree of sand grains depends on factors such as water flow velocity, flow rate, and sediment concentration density. The friction degree of sand grains is directly affected by the hydrodynamic force differences. The stronger the water flow, the greater the friction degree of sand grains, and as the water depth increases, the friction force also changes. By calculating the pressure load of the water flow on the surface of the earth dam, the influence of different water depths and sand grain friction on the force on the dam body surface is evaluated. Using the sand grain friction degree and hydrodynamic force difference data at different water depths, combined with the thick-wall average difference data of the earth dam, the stratified pressure between different drop depths of the earth dam is calculated. The stratified wall pressure flow field force load can be calculated through fluid mechanics. By combining the sand grain friction force and the hydrodynamic force difference, the force distribution of each layer of the dam body is calculated, so as to obtain the pressure load data at different depths of the dam body. Using the force load data of the stratified wall pressure flow field, combined with the dynamic characteristics of the water flow, a water flow impact simulation is carried out. The simulation calculation process takes into account multiple factors such as the non-uniformity of the water flow, flow velocity, and friction characteristics of sand grains, and a mechanical model is used to calculate the impact force generated by the water flow on the surface of the dam body. Through dynamic simulation, the impact data of the water flow on the surface of the dam body under different water depth conditions are obtained. These data will be further used to evaluate the flood risk of the dam body and guide subsequent structural optimization and safety assessment.

[0046] Preferably, the calculation of the sand grain friction degree between different water depths is performed according to the sediment concentration density data between different water depths and the hydrodynamic force difference data at different water depths through the sand grain friction degree calculation formula, and the sand grain friction degree calculation formula is as follows: In the formula, represents the result value of the sand grain friction degree, represents the sediment concentration between different water depths, represents the pressure coefficient between different water depths, represents the water flow velocity between different water depths, represents the time coefficient, represents the density mean difference of the sediment concentration density data between different water depths, represents the dynamic difference mean difference of the hydrodynamic difference data between different water depths, represents the error correction value of the calculation formula for the sand grain friction degree.

[0047] The present invention constructs a calculation formula for the sand grain friction degree. By comprehensively considering various factors such as sediment concentration, pressure, and water flow velocity, it can effectively predict the sand grain friction degree under different water depth conditions. This has important application value in the fields of water conservancy projects, river regulation, and ecological environment protection. The formula fully considers the sediment concentration , and the sediment concentration is a key factor affecting the sand grain friction degree. It directly reflects the concentration of sand grains in the water body. A higher sediment concentration usually increases the frictional force, thus affecting the deposition and erosion processes. By accurately measuring the sediment concentration at different water depths, more accurate data can be provided for the calculation, thereby improving the prediction ability of the model. The pressure coefficient between different water depths , and the pressure coefficient is used to describe the pressure change generated by the water body at different depths. It is directly proportional to the water depth. As the depth increases, the pressure increases, thus affecting the force exerted by the water flow on the sand grains. Using this parameter helps to understand the influence of the water flow on the movement of sand grains and provides a more accurate physical model for the deposition and movement of sand grains. The water flow velocity between different water depths , and the water flow velocity reflects the dynamic state of the water body and directly affects the movement and friction of sand grains. A higher flow velocity causes the sand grains to be more easily carried by the water flow, thus reducing the friction degree. By considering the flow velocity changes at different water depths, the behavior of sand grains in the water flow can be analyzed more comprehensively, providing a scientific basis for soil and water conservation and river regulation. The time coefficient , which is used to describe the dynamic process of the sand grain friction changing with time. It can help analyze the variation law of the sand grains in the water flow over time. Especially in the case of drastic water flow changes, the introduction of the time factor can better reflect the instantaneous friction situation. This is of great significance for evaluating the deposition and erosion behavior of sand grains within a specific time. The density mean difference of the sediment concentration density data between different water depths , The density mean difference measures the degree of change in sediment concentration at different water depths and can reflect the non-uniformity of sediment particle distribution in the water body. By calculating the density mean difference, the aggregation areas of sediment particles in the water flow can be identified, providing data support for the study of riverbed evolution and ecological impacts. The dynamic difference mean difference of hydrodynamic difference data at different water depths , The dynamic difference mean difference is used to describe the changes in hydrodynamic characteristics at different water depths and can reveal the influence of water flow on sediment particle friction. The accuracy of this parameter is crucial for understanding how sediment particles move at different water depths and flow velocities and helps optimize river regulation and sediment management strategies. The error correction value of the sediment particle friction degree calculation formula , The error correction value is a parameter used to adjust the calculation results to ensure that the model can more accurately reflect the actual situation. Due to the uncertainty in the measurement process, the introduction of the correction value can improve the reliability of the model, making the final calculation results more in line with the actual environmental conditions.

[0048] Preferably, calculating the force load of the stratified wall pressure flow field between different drop depths for the thick-wall difference data of the earth dam drop according to the sediment particle friction degree data between different water depths and the hydrodynamic difference data at different water depths includes the following steps: Evaluating the thick-wall pressure profile of the thick-wall difference data of the earth dam drop between different drop depths according to the hydrodynamic difference data at different water depths to obtain the thick-wall pressure profile partition data; Deriving the flow direction vector of the partition pressure profile based on the sediment particle friction degree data between different water depths for the thick-wall pressure profile partition data to obtain the flow direction vector of the partition pressure profile; Conducting multi-angle non-linear interference analysis on the flow direction vector of the partition pressure profile to obtain the multi-angle interference data of the profile vector; Fitting the pressure density space gradient to the flow direction vector of the partition pressure profile according to the multi-angle interference data of the profile vector to obtain the pressure density space gradient fitting data; Calculating the force load of the stratified wall pressure flow field between different drop depths for the thick-wall difference data of the earth dam drop according to the multi-angle interference data of the profile vector and the pressure density space gradient fitting data to obtain the force load data of the stratified wall pressure flow field.

[0049] In the embodiments of the present invention, first, it is necessary to combine the hydrodynamic difference data of different water depths and the average difference data of the thick walls of the dam drop to evaluate the thick wall pressure profile at different drop depths of the earth dam. First, analyze the water flow forces at different depths of the dam body according to the hydrodynamic difference data. Through indicators such as water flow rate and flow rate, combined with the theory of hydrodynamics, determine the pressure changes generated by the water flow at different water depths. For each water depth layer, use the following method to calculate the pressure profile, combine the structural information at different depths of the dam body, and evaluate the pressure profile at different depths of the dam body. By calculating the pressure data at different water depth levels multiple times, finally divide these data into multiple pressure zones to form the thick wall pressure profile zone data. Based on the sand grain friction degree data and the pressure profile zone data at different water depths, deduce the flow vector of the zonal pressure profile. First, deduce the flow direction of each water depth layer using the sand grain friction degree data. The sand grain friction force is closely related to the flow velocity and direction of the water flow. The area with a large friction force causes the water flow to deflect, thus affecting the deduction of the flow vector. Based on the sand grain friction degree data, calculate the friction force distribution of each water depth layer, combine the friction force of each water depth layer with the corresponding pressure profile data, deduce the flow direction of the water flow through the theory of fluid mechanics, and further analyze the flow vector of the zonal pressure profile using multi-angle non-linear interference analysis. By analyzing the changes in the flow vector at different angles, evaluate the interference effect of the water flow at different depths of the dam body. The specific operation steps are as follows: For each zone, select multiple different angles (such as 30°, 45°, 60°, etc.), rotate and interfere analyze the flow vector, calculate the interference effect of the flow vector at different angles based on the non-linear dynamics theory, and obtain the interference data. This process involves the interaction of the flow vector at different angles, considering factors such as the deflection and vortex effect of the water flow, and uses numerical calculation methods (such as the finite difference method or the finite element method) to perform interference calculations on the flow vector, and finally obtain the multi-angle interference data of the profile vector. Fit the multi-angle interference data of the profile vector to the spatial gradient of the pressure density. First, determine the spatial gradient of the pressure change by analyzing the multi-angle interference data of the profile vector, perform spatial analysis on the pressure data of each zone, consider the direction and change degree of the water flow, and calculate the pressure gradient. The fitting process uses the non-linear least squares method or other appropriate numerical optimization algorithms to approximate the pressure gradient to obtain more accurate fitting data. Use the spatial gradient data of the pressure density and the multi-angle interference data of the profile vector obtained in the previous steps, combined with the average difference data of the thick walls of the dam drop, to calculate the force on the earth dam structure at different depths. For each layer, combine the pressure gradient and flow vector of the water flow to calculate the pressure load generated by the water flow on the dam surface. Finally, the obtained stratified wall surface pressure flow field force load data provides an accurate basis for the subsequent dam body stability analysis and risk assessment.

[0050] Preferably, step S3 includes the following steps: Step S31: Construct a stress intensity thermal diagram between different water depths for the concrete dam structure design data based on the stratified pressure water flow impact data to obtain the water flow impact stress intensity thermal diagram; Step S32: Analyze the fatigue cumulative effect of the key areas of the concrete dam structure design data based on the water flow impact stress intensity thermal diagram to obtain the fatigue cumulative effect data of the key areas; Step S33: Estimate the fatigue limit load intensity for the fatigue cumulative effect data of the key areas to obtain the fatigue limit load intensity estimation data.

[0051] In the embodiments of the present invention, by using the impact data of the stratified pressure water flow obtained in the previous step, the impact force generated by each layer of water flow at different depths and regions is obtained, and the structural design data of the concrete dam are acquired, including information such as the geometric shape, material, and thickness of the dam body. Using the principles of fluid dynamics, combined with the velocity, flow rate, and hydrodynamic characteristics of the water flow, the stress generated by the water flow impact force on the dam body is calculated. Considering the water flow dynamics at different water depths, based on the changes in water flow velocity and direction, the stress value is further weighted by depth to ensure that the calculation result reflects the stress differences at different levels of the dam body. According to the stress distribution, the surface of the dam body is divided into multiple regions, and the stress values within each region are marked according to the color gradient. The specific approach is as follows: The calculated stress values are converted into a thermal map through a thermodynamic model, and the stress intensity is represented by the color depth of the thermal map. Each depth layer is processed separately, and finally, the stress intensity thermal map for each water depth level is obtained. Finally, these thermal maps are combined to form a multi-layer thermal map, representing the stress distribution of the water flow impact at different water depths. According to the regions with relatively high stress intensity in the stress intensity thermal map of the water flow impact, the key regions that will be subjected to greater stress impact are selected, usually including the downstream surface of the dam body, the toe region of the dam, and the joints in the middle of the dam body, etc. The selection of the key regions is based on the regions with relatively high stress values in the stress intensity thermal map of the water flow impact, or the weak parts that are prone to fatigue in the dam design. For the selected key regions, according to the stress intensity borne by each region at different water depths, the rain flow counting method is used to calculate the fatigue effect. This method calculates the influence of different stress levels on material fatigue by counting the number of occurrences at different stress levels. In this step, combined with the stress-strain relationship curve, the stress history of each region is analyzed, and its fatigue cumulative effect under different water flow impacts is calculated. By comparing the fatigue effects of different regions, it is determined which parts have relatively serious fatigue effects, thereby guiding the dam design and maintenance. Standard methods such as the Goodman linear fatigue criterion or the Miner linear fatigue law are used to estimate the fatigue limit load intensity. According to the stress history and cumulative fatigue values of different regions, a suitable fatigue estimation model is selected for calculation. According to the Miner's law, the proportions of each fatigue cycle are accumulated to obtain the fatigue limit load intensity of the key regions. According to the fatigue limit strength formula, the cumulative fatigue effect of each region is compared with the maximum load intensity to obtain the fatigue limit load intensity value. The calculated fatigue limit load intensity value is compared with the strength of the actual dam body material to ensure that the fatigue limit load intensity estimation result conforms to the actual situation. In case of non-conformity with the actual situation, appropriate corrections are made according to the anti-fatigue ability of the actual material. The calculated fatigue limit load intensity estimation data will be used for subsequent risk assessment and structural safety verification.

[0052] Preferably, step S32 includes the following steps: Step S321: Conduct regional block analysis on the thermal stress intensity map of water flow impact to obtain local impact stress distribution data; Step S322: Calculate the mean and variance of the local impact stress distribution data to obtain local stress mean and variance data; Step S323: Conduct stress influence range stratification analysis on the local impact stress distribution data according to the local stress mean and variance data to obtain stress influence range stratification data; Step S324: Evaluate the failure probability of the key area for the stress influence range stratification data to obtain key area fatigue failure probability data; Step S325: Conduct fatigue cumulative effect analysis on the key areas of the concrete dam structure design data according to the key area fatigue failure probability data and the local stress mean and variance data to obtain key area fatigue cumulative effect data.

[0053] In the embodiment of the present invention, obtain the thermal stress intensity map of water flow impact constructed in the previous step. Each area in the map represents the water flow impact stress distribution at different depths and positions. According to the geometric shape of the dam body and the changing trend of the water flow impact stress, select a suitable partitioning method to divide the thermal stress intensity map. Usually, the uniform grid partitioning method is adopted to divide the thermal stress intensity map into several small areas, or non-uniform partitioning is carried out according to the stress intensity gradient of the water flow impact. Each partitioned area represents a small range on the dam body. The specific partitioning method can refer to the changing characteristics of the water flow impact force. For example, the area with higher stress can be divided into smaller blocks. For each partitioned area, calculate the water flow impact stress value within this area. According to the stress intensity value of each block in the thermal stress intensity map, the local impact stress distribution within this area can be calculated. The stress intensity within each block can be weighted and calculated by the average value of all points within this area to obtain the overall impact stress of this partitioned area. The obtained local impact stress distribution data will provide a basis for subsequent analysis, including the mean variance calculation and stress influence range analysis in the subsequent steps. Using the local impact stress distribution data, through the calculation of the mean and variance, obtain the mean and variance of the stress intensity of each partitioned area. This step is to quantify the stress fluctuation within each area, further analyze the influence of the stress on each part of the dam body. Obtain the local impact stress distribution data obtained in step S321. These data contain the stress intensity information within each partitioned area. Calculate the mean value of the stress data for each partitioned area. After obtaining the mean value, calculate the variance to measure the stress volatility within this area. Obtain the local stress mean variance data calculated in step S322, which contains the stress mean and variance of each partitioned area. According to the stress mean variance of each area, formulate a hierarchical standard for stress influence. Usually, the area with a larger variance is divided into a higher influence level, and the area with a smaller variance is divided into a lower influence level. For example, set a certain threshold. If the variance is greater than a certain value, it is considered that the stress influence in this area is larger and belongs to the high influence level; if the variance is less than a certain value, it is considered that the stress influence in this area is smaller and belongs to the low influence level. For each partitioned area, perform hierarchical classification according to its stress variance to obtain hierarchical data of different stress influence ranges. For example, according to different stress distribution levels, the area can be divided into multiple levels, such as the strong influence area, the medium influence area, and the weak influence area. The obtained hierarchical data of the stress influence range will be used as the basis for subsequent analysis to help determine the priority of the key area in the fatigue assessment. Obtain the hierarchical data of the stress influence range obtained in step S323, and combine the historical failure data or theoretical failure model of the local area to conduct the fatigue failure probability assessment of the key area. Use the cumulative damage theory or the fatigue life prediction model constructed based on the stress-strain relationship to calculate the fatigue failure probability under different stress influence levels, and combine the hierarchical classification of the stress influence range to calculate the fatigue failure probability of each area.The failure probability in the high-stress influence area is relatively high, while that in the low-stress influence area is relatively low. Obtain the fatigue failure probability data of the key area in step S324 and the local stress mean variance data in step S322. According to the fatigue failure probabilities and stress variances of different areas, use the cumulative damage model to calculate the cumulative fatigue effect. Usually, the Miner's rule or other stress-strain-based models are used to calculate the cumulative fatigue effect. According to the fatigue effect analysis results, output the cumulative fatigue effect data of the key area for subsequent fatigue limit estimation.

[0054] Preferably, step S33 includes the following steps: Step S331: Simulate the dynamic microcrack propagation path of the concrete dam structure design data based on the cumulative fatigue effect data of the key area to obtain the microcrack propagation path prediction data; Step S332: Conduct a nonlinear response analysis based on the microcrack propagation path prediction data to obtain the nonlinear fatigue response data; Step S333: Identify the fatigue failure critical point of the nonlinear fatigue response data to obtain the fatigue failure critical point data; Step S334: Estimate the fatigue limit load intensity based on the fatigue failure critical point data and the nonlinear fatigue response data to obtain the fatigue limit load intensity estimation data.

[0055] In an embodiment of the present invention, based on the fatigue cumulative effect data of the key area, the extension path of the microcracks in the concrete dam structure is simulated. The implementation steps are as follows: obtain the fatigue cumulative effect data of the key area obtained in step S325, including the fatigue damage accumulation of each area of ​​the dam body, and select an appropriate microcrack extension model. Common models include the Paris formula or a numerical simulation method based on the law of crack extension. Based on the law of crack extension, the extension path of the microcracks under different loading cycles is simulated. The specific operation is to use the accumulated information of fatigue damage (such as stress intensity factor) to infer the extension direction and position of the crack in the dam structure according to the crack extension rate. The obtained microcrack extension path prediction data reflects the development direction and extension speed of microcracks in different areas, providing a basis for subsequent nonlinear response analysis. Obtain the microcrack extension path prediction data obtained in step S331, combined with the concrete dam structure design data, especially the geometric shape, material properties and existing crack information of the dam body. Select an analysis method suitable for simulating the nonlinear response of the structure. Commonly used models include nonlinear analysis methods based on the finite element method (FEM), or fatigue response simulation based on the incremental damage model. The nonlinear response analysis takes into account the local stiffness changes and nonlinear material behavior caused by crack propagation. According to the simulation data of the microcrack propagation path, the nonlinear response calculation of the concrete dam structure is performed by numerical methods such as finite element analysis. The development of cracks in different loading cycles and their influence on the overall stiffness and deformation of the dam body need to be considered in the calculation. For different fatigue loading conditions, the changes in stress and strain in each cycle are simulated to evaluate the influence of microcracks on the overall response of the dam body. The obtained nonlinear fatigue response data, including the stress, strain, displacement and other response quantities of the dam body during the microcrack propagation process, provide a basis for the subsequent identification of the critical point of fatigue failure. The nonlinear fatigue response data obtained in step S332 are obtained, including the response of the dam body under different stress cycles. Based on the fatigue failure criterion of the material, a critical point identification method is selected. Commonly used methods include that when the fatigue damage accumulates to a certain critical value, the structure will fail. Critical point identification methods based on energy method, stress amplitude method or fracture mechanics theory are usually used. For example, the critical point can be identified by the following criteria: when the fatigue damage at a certain position (such as the cyclic accumulation of stress-strain) exceeds the set critical value, the position is regarded as the critical point of fatigue failure. According to the nonlinear fatigue response data, the fatigue damage of each monitoring point is analyzed to identify the critical area where fatigue failure occurs in the dam body. This process takes into account the influence of crack propagation and evaluates the fatigue limit based on the accumulated damage and energy dissipation. The obtained fatigue failure critical point data indicates the area where fatigue failure occurs most in the dam structure. These data will provide an important basis for the subsequent fatigue limit load strength estimation. Obtain the fatigue failure critical point data obtained in step S333 and the nonlinear fatigue response data in step S332.The data includes the responses of the dam body under different stress loadings, the crack propagation conditions, and the identified critical points of fatigue failure. Based on the positions of the critical points of fatigue failure and the non-linear response data, the damage accumulation method or the life prediction model is used to estimate the fatigue limit load intensity. Usually, a method based on the fatigue characteristics of the material (such as the S-N curve) is adopted, combined with crack propagation analysis, to calculate the limit load intensity under different loadings. Using the above methods, combined with the fatigue damage conditions in each key area, the overall fatigue limit load intensity of the dam body is deduced. This process needs to comprehensively consider factors such as the crack propagation path, fatigue response, and fatigue characteristics of the material. The estimated data of the fatigue limit load intensity provides the critical strength for the dam body to fail under different load conditions, providing a basis for the safety assessment and design optimization of the dam body.

[0056] Preferably, step S4 includes the following steps: Step S41: Normalize the estimated data of the fatigue limit load intensity to obtain the normalized data of the fatigue limit load intensity; Step S42: Predict the flood risk level based on the normalized data of the fatigue limit load intensity using the policy gradient algorithm to obtain the flood risk level data; Step S43: Construct a risk assessment system for the flood risk level data to obtain the flood risk assessment system.

[0057] In the embodiments of the present invention, the estimated data of the fatigue limit load intensity is normalized. The purpose of normalization is to adjust the data range to a unified scale to make it more comparable. In this step, the maximum-minimum normalization method is adopted, that is, for each estimated value of the fatigue limit load intensity, after normalization, the data range will be scaled to between 0 and 1, ensuring that the data will not deviate due to magnitude differences in the subsequent analysis process. Based on the policy gradient algorithm, the flood risk level is predicted for the normalized fatigue limit load intensity data. The policy gradient algorithm is a reinforcement learning method that maximizes the cumulative reward by optimizing the policy function. In this step, the prediction model of the flood risk level can be matched with the fatigue limit load intensity data by defining a reward function. Specifically, first, a prediction model of the risk level is determined, different load intensities are used as state inputs, and the policy function is trained in combination with historical data and risk level labels. The model is optimized so that under the given input, a reasonable risk level can be output. This process iteratively adjusts the policy parameters to minimize the risk prediction error and ensure that the finally obtained risk level accurately reflects the stress situation of the dam under different water flow conditions. Based on the obtained flood risk level data, a complete risk assessment system is further developed. This system not only considers the assessment of the impact of the fatigue limit load intensity on the dam safety, but also needs to comprehensively consider multiple factors such as water flow impact, dam structure design, and geographical environment. The risk assessment system usually includes multiple levels, and corresponding response measures are set according to different risk levels. For example, in the high-risk level, a more rigorous dam inspection and maintenance plan needs to be implemented, while in the low-risk level, the regular inspection frequency can be maintained. The ultimate goal of this step is to conduct a comprehensive risk assessment of the dam based on the risk level data and form a set of scientific, reasonable, and highly operable risk response strategies.

[0058] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0059] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a concrete dam flood risk assessment index system, characterized in that: The following steps are involved: Step S1: Acquire the concrete dam structure design data and the river channel data in the upstream area of ​​the earth dam; Conduct velocity gradient analysis on river data in the upstream area of ​​the earth dam to obtain the flow velocity gradient data of the river in the rainy season; Step S2: Calculate the water depth increment rate for the flow velocity gradient data of the river in rainy season to obtain the water depth increment rate data of the river; perform a stratified pressure water flow impact simulation calculation at different water depths on the concrete dam structure design data based on the flow velocity gradient data of the river in rainy season to obtain the stratified pressure water flow impact data; Step S3: Estimating the fatigue limit load strength of the concrete dam structure design data according to the layered pressure water flow impact data to obtain fatigue limit load strength estimation data; Step S4: predicting the flood risk level of the fatigue limit load strength estimation data to obtain flood risk level data; The risk assessment system is constructed based on the flood risk level data to obtain the flood risk assessment system.

2. The method for constructing a concrete dam flood risk assessment index system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the concrete dam structure design data and the river channel data in the upstream area of ​​the earth dam; Step S12: Counting the flow data of the river channel in the upstream area of ​​the earth dam in the rainy season to obtain the flow data of the upstream river channel in the rainy season; Step S13: Perform velocity gradient analysis on the upstream river flow data in the rainy season to obtain the river flow velocity gradient data in the rainy season.

3. The method for constructing a concrete dam flood risk assessment index system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: evaluating the river sediment content according to the river flow velocity gradient data in the rainy season on the river data in the upstream area of ​​the earth dam, and obtaining the river sediment content evaluation data; Step S22: Calculating the water depth increment rate of the flow velocity gradient data of the river in the rainy season to obtain the water depth increment rate data of the river; Step S23: evaluating the sediment density between different water depths for the river sediment content assessment data according to the river depth increment rate data, and obtaining sediment density data between different water depths; Step S24: Based on the sediment density data at different water depths and the flow velocity gradient data of the river in rainy season, the stratified pressure water flow impact simulation calculation at different water depths is performed on the concrete dam structure design data to obtain the stratified pressure water flow impact data.

4. The method for constructing a concrete dam flood risk assessment index system according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: Calculate the height difference of the concrete dam structure design data to obtain the height difference data of the concrete dam; Step S242: calculating the average difference of earth dam height difference and wall thickness on the concrete dam structure design data according to the concrete dam height difference data, and obtaining the average difference of earth dam height difference and wall thickness data; Step S243: evaluating the hydrodynamic difference of different water depths based on the sediment density data of different water depths and the flow velocity gradient data of the river in the rainy season, and obtaining the hydrodynamic difference data of different water depths; Step S244: calculating the degree of friction of sand particles at different water depths according to the sand density data at different water depths and the hydrodynamic difference data at different water depths, and obtaining the degree of friction of sand particles at different water depths; Step S245: Calculate the stratified wall pressure flow field load between different drop depths based on the sand friction degree data between different water depths and the hydrodynamic difference data between different water depths for the earth dam drop thickness average difference data to obtain the stratified wall pressure flow field load data; Step S246: performing a simulation calculation of the stratified pressure water flow impact between different water depths based on the stratified wall pressure flow field force load data to obtain the stratified pressure water flow impact data.

5. The method for constructing a concrete dam flood risk assessment index system according to claim 4, characterized in that: The calculation of the degree of friction of sand particles at different water depths is performed based on the sand density data at different water depths and the hydrodynamic difference data at different water depths. This is done using the calculation formula for the degree of friction of sand particles, where the calculation formula for the degree of friction of sand particles is as follows: ; In the formula, Indicates the result value of the degree of sand friction. Indicates the sediment content at different water depths. Indicates the pressure coefficient between different water depths, Indicates the water flow speed between different water depths. represents the time coefficient, It represents the average density difference of sediment density data at different water depths. The dynamic difference of the hydrodynamic difference data at different water depths is expressed as the average difference, Indicates the error correction value of the sand friction degree calculation formula.

6. The method for constructing a concrete dam flood risk assessment index system according to claim 4, characterized in that: According to the sand friction degree data at different water depths and the hydrodynamic difference data at different water depths, the calculation of the layered wall pressure flow field force load at different drop depths is carried out based on the average difference data of the earth dam drop thickness wall, including the following steps: According to the hydrodynamic difference data of different water depths, the thick wall pressure profiles of different drop depths are evaluated for the average difference data of the earth dam drop thickness, and the thick wall pressure profile partition data are obtained; Based on the sand friction degree data between different water depths, the partition pressure profile flow direction vector is derived from the partition pressure profile data of the thick wall, and the partition pressure profile flow direction vector is obtained; Perform multi-angle nonlinear interference analysis on the flow direction vector of the partitioned pressure profile to obtain multi-angle interference data of the profile vector; According to the multi-angle interference data of the profile vector, the pressure density space gradient fitting is performed on the flow direction vector of the partitioned pressure profile to obtain the pressure density space gradient fitting data; According to the multi-angle interference data of profile vector and the spatial gradient fitting data of pressure density, the stress load of stratified wall pressure flow field between different drop depths is calculated for the average difference data of earth dam drop thickness, and the stress load data of stratified wall pressure flow field is obtained.

7. The method for constructing a concrete dam flood risk assessment index system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: constructing a stress intensity thermodynamic map between different water depths for the concrete dam structure design data according to the layered pressure water flow impact data, and obtaining a water flow impact stress intensity thermodynamic map; Step S32: performing fatigue cumulative effect analysis on the key areas of the dam body on the concrete dam structure design data based on the water flow impact stress intensity thermodynamic map to obtain fatigue cumulative effect data of the key areas; Step S33: performing fatigue limit load strength estimation on the fatigue cumulative effect data of the key area to obtain fatigue limit load strength estimation data.

8. The method for constructing a concrete dam flood risk assessment index system according to claim 7, characterized in that: Step S32 includes the following steps: Step S321: performing regional block analysis on the water flow impact stress intensity thermodynamic map to obtain local impact stress distribution data; Step S322: performing mean variance calculation on the local impact stress distribution data to obtain local stress mean variance data; Step S323: performing stress influence range hierarchical analysis on the local impact stress distribution data according to the local stress mean variance data to obtain stress influence range hierarchical data; Step S324: performing a critical area failure probability assessment on the stress influence range layered data to obtain critical area fatigue failure probability data; Step S325: performing fatigue cumulative effect analysis of the key area of ​​the dam body on the concrete dam structure design data according to the key area fatigue failure probability data and the local stress mean variance data to obtain the key area fatigue cumulative effect data.

9. The method for constructing a concrete dam flood risk assessment index system according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: performing dynamic microcrack propagation path simulation on the concrete dam structure design data according to the fatigue cumulative effect data of the key area to obtain microcrack propagation path prediction data; Step S332: performing nonlinear response analysis based on the microcrack propagation path prediction data to obtain nonlinear fatigue response data; Step S333: performing fatigue failure critical point identification on the nonlinear fatigue response data to obtain fatigue failure critical point data; Step S334: performing fatigue limit load strength estimation based on the fatigue failure critical point data and the nonlinear fatigue response data to obtain fatigue limit load strength estimation data.

10. The method for constructing a concrete dam flood risk assessment index system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: normalizing the fatigue limit load strength estimation data to obtain fatigue limit load strength normalized data; Step S42: predicting the flood risk level of the fatigue limit load strength normalized data based on the policy gradient algorithm to obtain flood risk level data; Step S43: constructing a risk assessment system for the flood risk level data to obtain a flood risk assessment system.

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