Concrete gravity dam long-term failure probability prediction and optimization analysis method
By constructing a material degradation and load calculation model, defining failure mode criteria, and using big data simulation to calculate failure probability, the problem of multi-factor coupling in the long-term safety assessment of concrete gravity dams was solved, achieving accurate prediction and optimization analysis, and improving the accuracy and quantitative effect of safety assessment.
Patent Information
- Application Number
- CN202511624609.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-07
- Publication Date
- 2026-02-06
AI Technical Summary
Existing technologies cannot accurately reflect the long-term safety risks of concrete gravity dams, fail to systematically analyze the synergistic effects of multiple failure modes, and lack quantitative assessment of the effectiveness of optimization schemes, resulting in large deviations in safety assessment results and making it difficult to meet actual engineering needs.
We constructed a material degradation model and a load calculation model, defined logical judgment criteria for three failure modes: anti-slip, anti-overturning, and roof fall, simulated and calculated the failure probability through big data sampling, and conducted quantitative analysis on optimization measures.
It enables accurate prediction of the long-term failure probability of concrete gravity dams, provides multi-factor coupled modeling, improves prediction accuracy and quantitative evaluation of optimization schemes, and is applicable to safety assessment in multiple fields.
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Figure CN121479897A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of water conservancy project safety monitoring technology, specifically relating to a method for predicting and optimizing the long-term failure probability of concrete gravity dams. Background Technology
[0002] Concrete gravity dams, as core structures in the field of water conservancy engineering, are widely used in key scenarios such as power generation, flood control, navigation, and reservoir impoundment, and are an important basic structure to ensure the function of water conservancy engineering systems.
[0003] As the service life of concrete gravity dams extends, the dam body and foundation materials are gradually degraded due to environmental erosion (such as seepage, chemical corrosion, and freeze-thaw cycles), cumulative loads (such as long-term hydrostatic pressure and periodic water level changes), and construction defects. Simultaneously, the sudden onset of unpredictable loads from extreme hydrological events (such as catastrophic floods) and earthquakes further exacerbates the risk of dam failure. Therefore, accurately predicting the failure probability of concrete gravity dams throughout their entire lifespan and proposing scientific optimization measures to address potential risks has become a core requirement in the field of water conservancy project safety monitoring.
[0004] However, current safety assessment technologies for concrete gravity dams still have significant shortcomings, making it difficult to meet the actual needs of engineering projects for long-term safety risk management. These shortcomings are specifically reflected in the following three aspects: (1) Failure to reflect the coupling effect of multiple factors: Traditional dam safety assessments mostly adopt deterministic analysis methods or static reliability theory, and conduct assessments based only on initial design parameters or fixed performance indicators. They ignore the time-varying degradation law of material properties (such as concrete strength, friction coefficient and cohesion of dam foundation contact surface) over time, and do not consider the coupling effect of material degradation with extreme hydrological and seismic events. This results in a large deviation between the assessment results and the actual service status, and fails to accurately reflect the long-term safety risks of the dam.
[0005] (2) Lack of analysis of complex failure mechanisms: Existing studies mostly focus on single failure modes (such as only focusing on anti-slip failure or overtopping failure), without systematically analyzing the synergistic mechanism of the three failure modes of "anti-slip-anti-overtopping-overtopping" in the core of concrete gravity dams. In actual engineering, dam failure is often the result of multiple modes being triggered or interacting. Single-mode assessment is difficult to fully cover safety risks and may lead to the omission of safety hazards.
[0006] (3) Lack of quantitative basis for optimization schemes: In the stage of comparing and selecting dam maintenance and reinforcement schemes, existing technologies mostly rely on experience judgment or qualitative analysis, and lack technical means to quantitatively evaluate the long-term effects of different engineering measures (such as drainage system optimization, foundation reinforcement, and dam crest heightening). This makes it difficult for engineers to accurately judge the risk reduction and economic efficiency of different optimization schemes, and makes it impossible to efficiently select the optimal scheme that suits the characteristics of the project.
[0007] In view of this, the present invention is hereby proposed. Summary of the Invention
[0008] To address the aforementioned technical problems in existing technologies, this invention provides a method for predicting and optimizing the long-term failure probability of concrete gravity dams. This method solves the problem that existing technologies fail to integrate the multi-factor coupling of material time-varying degradation and load randomness, leading to biased risk predictions. Furthermore, it fails to systematically analyze the synergistic effects of three failure modes—anti-slip, anti-overturning, and overtopping—making it difficult to comprehensively cover risks.
[0009] To achieve the above objectives, the technical solution of the present invention is as follows: A method for predicting and optimizing the long-term failure probability of concrete gravity dams, comprising: S1. Construct a material degradation model and a load calculation model. The material degradation model describes the time-varying degradation law of the dam body and foundation material properties, and the load calculation model quantifies the randomness and time-varying nature of hydrological and seismic loads. S2. Define the logical judgment criteria for three failure modes: anti-slip failure, anti-overturning failure, and roof-overturning failure. S3. Based on the model in step S1 and the criteria in step S2, statistical methods are used to calculate the instantaneous failure probability in a single year and the cumulative failure probability over the entire lifespan. S4, Adopt Implement big data sampling simulation through programming to simulate and calculate the failure probability throughout the entire lifespan; S5. Quantitatively analyze the effects of three optimization measures: improving the drainage system, treating the foundation, and increasing the height of the free slab, and output the comparison results of the failure probability before and after optimization.
[0010] Furthermore, the material degradation model includes: a concrete compressive strength degradation model, an uplift pressure coefficient growth model, and a dam foundation contact surface friction coefficient and cohesion degradation model.
[0011] Furthermore, the concrete compressive strength degradation model The expression is:
[0012] in, Let be the compressive strength of the concrete at time t. This represents the average initial compressive strength of the concrete (MPa). The annual degradation rate of concrete compressive strength. This refers to the length of service.
[0013] Furthermore, the aforementioned uplift pressure coefficient growth model The expression is:
[0014] in, Let be the pressure coefficient at time t. This is the initial uplift pressure coefficient. To increase the annual growth rate of pressure; The friction coefficient and cohesion degradation model adopts an exponential degradation model, and the formula for the time-varying value of the friction coefficient is:
[0015] The formula for the time-varying value of cohesion is:
[0016] in, Let be the friction coefficient of the dam foundation contact surface at time t. The initial friction coefficient, The annual degradation rate of the friction coefficient. Let t be the cohesion at the dam foundation contact surface. The initial cohesion, The annual degradation rate of cohesion.
[0017] Furthermore, the load calculation model includes: a hydrostatic pressure calculation model, an extreme water level calculation model, and a seismic inertial force calculation model; In the hydrostatic pressure calculation model, the normal water level height follows a normal distribution; in the extreme water level calculation model, the extreme water level follows a Gumbel distribution; and in the seismic inertial force calculation model, the number of earthquakes follows a Poisson distribution and the peak ground acceleration follows a normal distribution.
[0018] Furthermore, the logical judgment criterion for the anti-slip failure is: failure is determined when the sliding force is greater than or equal to the anti-slip resistance. The sliding force is the superposition of hydrostatic pressure and horizontal seismic force, where hydrostatic pressure... The calculation formula is:
[0019] in, For hydrostatic pressure, The density of water (kN / kWh) ), g is the acceleration due to gravity (m / ), This represents the average normal water level (m). Standard deviation of normal water level (m); The anti-slip resistance The calculation formula is:
[0020] in, For the dam's own weight, To increase pressure, Let be the friction coefficient of the dam foundation contact surface at time t. Let t be the cohesion at the dam foundation contact surface. This refers to the contact area at the bottom of the dam per unit width.
[0021] Furthermore, the logical judgment criterion for the overturning resistance failure is: failure is determined when the overturning moment is greater than or equal to the overturning resistance moment; The overturning moment The calculation formula is:
[0022] The anti-overturning moment The calculation formula is:
[0023] in, The height of the point of application of the resultant force of hydrostatic pressure. The height of the point of application of the resultant seismic force. For hydrostatic pressure, For horizontal seismic forces, For the dam's own weight, This refers to the width of the dam base.
[0024] Furthermore, the logical judgment criterion for the failure of the dam crest is: when the water level elevation combination is greater than or equal to the effective dam crest elevation, it is judged as a failure; The water level elevation combination The expression is:
[0025] in, This is the normal water level. This is the extreme water level. Random fluctuations caused by waves and wind congestion; The effective dam crest elevation The expression is:
[0026] in, Design elevation for dam crest, Increase the height of the free plate Furthermore, the formula for calculating the instantaneous failure probability in a single year is as follows:
[0027] in, The sample size is [number of samples]. Let represent the total failure state of the i-th sample in year t.
[0028] Furthermore, the sample size in the big data sampling simulation is on the order of hundreds of thousands, using... software The function implements a nonlinear least squares method to solve the problem and outputs a cumulative failure probability exponential model. Optimal parameters ,in This represents the average rate of failure occurring per unit time.
[0029] Compared with existing technologies, the present invention provides a method for predicting and optimizing the long-term failure probability of concrete gravity dams. The method includes: constructing a material degradation model and a load calculation model; defining three failure criteria: anti-sliding, anti-overturning, and overtopping; and then calculating the instantaneous failure probability in a single year and the cumulative failure probability over the entire lifespan. This invention achieves big data sampling simulation and then quantitatively analyzes the optimization effects of improving drainage systems, foundation treatment, and increasing the height of free slabs. It realizes multi-factor coupled modeling, improves prediction accuracy, quantifies optimization effects, and is applicable to safety assessments of concrete gravity dams in multiple fields. Attached Figure Description
[0030] Figure 1 A flowchart for sampling simulation programming provided in an embodiment of the present invention; Figure 2 A graph showing the variation of the cumulative failure probability of a concrete gravity dam provided in an embodiment of the present invention; Figure 3 The diagram shows the effect of the optimization measures provided in the embodiments of the present invention. Detailed Implementation
[0031] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.
[0032] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.
[0033] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.
[0034] Example 1 The present invention proposes a method for predicting and optimizing the long-term failure probability of concrete gravity dams, the specific steps of which may include: S1. Construct a material degradation model and a load calculation model. The material degradation model describes the time-varying degradation law of the dam body and foundation material properties, and the load calculation model quantifies the randomness and time-varying nature of hydrological and seismic loads. S11. Material Degradation Model: Used to describe the time-varying properties of dam body and foundation materials, focusing on key parameters affecting dam stability, and constructed in three sub-models: S111, Concrete Compressive Strength Degradation Model: This model describes the phenomenon where concrete's ability to resist axial compressive strength gradually decreases over time due to a combination of factors, including changes in the material's inherent properties, environmental effects, load effects, and construction / curing defects. Specifically, it includes: Basic assumptions: It is assumed that the material properties change continuously over time, following a continuous and gradual degradation (ignoring sudden damage such as instantaneous earthquake damage), an environment-dominated mechanism (degradation is caused by long-term environmental effects such as infiltration, chemical corrosion, and freeze-thaw cycles, and is independent of load history), and isotropic degradation (material properties decay spatially uniformly, ignoring local defects).
[0035] Theoretical basis: Employing an exponential degradation model, the carbonization depth formula is derived according to Fick's diffusion law, exhibiting exponential decay. Applicable to degradation related to the current state (e.g., the carbonation rate of concrete is related to the uncarbonated thickness), the general form is: .
[0036] Model equation: Using exponential decay form, the expression is as follows: , Let be the compressive strength of the concrete at time t. This represents the average initial compressive strength of the concrete (MPa). The annual degradation rate of concrete compressive strength is 0.003 (in this model). Service life; S112, Uplift Pressure Coefficient Growth Model: Uplift pressure is the upward water pressure exerted by seepage water on the dam surface. The coefficient growth originates from the deterioration of seepage conditions (such as blockage of the drainage system), which threatens the safety of the dam.
[0037] Theoretical basis: A linear growth model is adopted, with the general form as follows: It is suitable for externally driven degradation (the uplift pressure gradually increases as the drainage system becomes clogged).
[0038] Model equations: , Let be the pressure coefficient at time t. This is the initial uplift pressure coefficient. The annual growth rate of uplift pressure is given by the reference value of 0.1% to 0.3% for the proportion of uplift pressure caused by drainage system blockage. The value in this model is 0.002.
[0039] S113, Friction Coefficient and Cohesion Degradation Model: Physical mechanisms: Cohesion degradation stems from chemical erosion at the cement-rock interface of the dam body, while friction coefficient degradation stems from reduced roughness at the dam body-bedrock contact surface due to particle migration.
[0040] Model equations: All models adopt an exponential degradation model, and the formula for the time-varying value of the friction coefficient is:
[0041] The formula for the time-varying value of cohesion is:
[0042] in, Let be the friction coefficient of the dam foundation contact surface at time t. The initial friction coefficient, The annual degradation rate of the friction coefficient. Let t be the cohesion at the dam foundation contact surface. The initial cohesion, The annual degradation rate of cohesion.
[0043] S12. Load Calculation Model: This model quantifies the randomness and time-varying nature of hydrological and seismic loads, providing load input for failure assessment. It is constructed using three sub-models: S121. Hydrostatic pressure calculation model: conventional water level height Follows a normal distribution ,in The average normal water level is taken as 0.7H in this model, where H is the dam height. Standard deviation of normal water level (3m in this model); hydrostatic pressure The specific formula is:
[0044] in, For hydrostatic pressure, The density of water is 9.81 kN / m³. ), g is the acceleration due to gravity (9.81 m / s²). ), The average normal water level (m, taken as 0.7H in this model). The standard deviation of the normal water level (m, taken as 3m in this model); S122. Extreme Water Level Calculation Model: The maximum value of independent and identically distributed samples asymptotically follows an extreme value distribution. The peak flood level conforms to a Gumbel distribution (Type I extreme value distribution). By combining extreme value statistical theory with engineering boundary conditions, this model quantifies extreme flood events. This model achieves scientific quantification of extreme flood events through the combination of extreme value statistical theory and engineering boundary conditions, providing reliable input for gravity dam risk analysis. The extreme water level follows a Gumbel distribution, and the distribution expression is:
[0045] in, The location parameter (reflecting typical flood levels, taken as 0.4H in this model) H is the scaling parameter (which controls the dispersion of extreme events; in this model, it is set to 0.15H). The result is a uniformly random number in the range [0,1]. S123. Seismic inertial force calculation model: Through probabilistic and modular design, a seismic inertial force simulation model suitable for long-term reliability assessment of dams is established to balance computational efficiency and engineering accuracy requirements.
[0046] Mathematical model: ,in Let be the number of earthquakes that occur within time t. The annual incidence rate is 0.03 in this model. Peak ground acceleration (PGA): The peak ground acceleration (PGA) follows a normal distribution. ,in (Peak ground acceleration average (corresponding to an intensity of approximately 7-8 degrees)) 0.12g (standard deviation of peak ground acceleration).
[0047] Horizontal seismic force calculation: ,in For dam quality W represents the dam's self-weight (in kN).
[0048] S2. Define the logical judgment criteria for three failure modes: anti-sliding failure, anti-overturning failure, and overtopping failure; based on common instability forms of dams, define the judgment criteria for these three failure modes, clarifying the mechanical principles and calculation methods, specifically including: S21. Sliding failure mode: Shear sliding occurs along the dam foundation surface. Failure occurs when the sliding force (water pressure + seismic force) exceeds the sliding resistance (friction + cohesion). Specific formulas include: Sliding force: , Hydrostatic pressure, superimposed when seismic force is involved. ; Anti-slip resistance: , For the dam's own weight, To increase pressure, Let be the friction coefficient of the dam foundation contact surface at time t. Let t be the cohesion at the dam foundation contact surface. This refers to the contact area at the base of the dam per unit width. Among them, the dam body's self-weight , The unit weight of concrete ( ), The volume of the dam body per unit span ( ), lifting pressure .
[0049] Failure detection: ,when > If the dam body fails to slide once, the system will determine and record that the dam body has failed once; otherwise, it will determine that the dam body has not failed.
[0050] Referring to Table 1, the meanings of the key parameters are as follows: Table 1
[0051] S22. Overturning Failure Mode: The dam body rotates and overturns around the downstream toe. Failure occurs when the overturning moment (water pressure + seismic moment) exceeds the overturning moment (self-weight stabilizing moment). Specific formulas include: Overturning moment: ; Anti-overturning moment: ; in, The height of the point of application of the resultant force of hydrostatic pressure. The height of the point of application of the resultant seismic force. For hydrostatic pressure, For horizontal seismic forces, This refers to the width of the dam base; Failure detection: ,when > If the dam fails once, the system determines and records that the dam body has experienced one overturning failure; otherwise, it determines that the dam body has not failed.
[0052] Referring to Table 2, the meaning of the key parameters is as follows: Table 2
[0053] S23. Overtopping Failure Mode: The mechanical principle is that when the reservoir water level exceeds the dam crest elevation, water overflow leads to dam erosion or structural failure. Specific formulas include: Effective dam crest elevation: , For the effective dam crest elevation, Design elevation for dam crest, Increase the height of the free plate.
[0054] Failure detection:
[0055]
[0056] in, This is the normal water level. This is the extreme water level. These are random fluctuations caused by waves and wind congestion. The water level combination is the sum of the larger value of the normal design water level and the extreme water level, plus random fluctuations. Random fluctuations caused by waves and wind congestion.
[0057] Referring to Table 3, the meanings of the key parameters are as follows: Table 3
[0058] S3. Based on the model in step S1 and the criteria in step S2, statistical methods are used to calculate the instantaneous failure probability in a single year and the cumulative failure probability over the entire lifespan; specifically including: S31. Failure Probability Calculation: Failure modes are converted into quantified probabilities using statistical methods, and the results are curve-fitted to enhance their engineering guidance value. S311. Single-Year Failure Mode Determination: For each sample i=1, ..., Nsim, and for each year t=0, ..., 100, three failure modes are obtained based on the load-resistance comparison: Slip failure: ; Overturning failure: ; Top view unavailable: ; The total number of failure events in a single year is calculated by summing the three in parallel: .
[0059] S312, Failure Probability Statistics: The instantaneous failure probability in a single year is the percentage of total failure events that occur within a given year. The specific calculation formula is as follows:
[0060] in, The sample size is [number of samples]. Let represent the total failure state of the i-th sample in year t. For each sample, an absorption memory is created; if a sample fails in any year, its cumulative state thereafter is considered failed.
[0061] The cumulative failure probability is the percentage of samples that experience at least one failure within a certain year from the start of service. The formula is:
[0062] in, Let be the cumulative probability of loss in year t. The sample size is [number of samples]. The i-th sample has failed at least once in year t.
[0063] S313, Failure Probability Curve Fitting: In failure probability statistical theory, failure determination is independent year by year, leading to a cumulative failure probability. The statistical results are a jumpy broken line, which lacks guidance for engineering. This patent uses an exponential model to simplify the fitting. The curve makes the optimized The curve is an exponential curve, which makes it easy to intuitively show the year-by-year changes in the cumulative failure probability of concrete gravity dams, and is of greater guiding significance for engineering.
[0064] Referring to Table 4, when establishing the model, the following reasonable assumptions are made to balance computational efficiency and engineering accuracy, so that the simulation of the failure probability of concrete gravity dams can take into account both scientificity and feasibility. Table 4
[0065] Refer to Table 5 for the initial values of key parameters in the model and their corresponding physical meanings, covering soil parameters and environmental parameters. All parameters are based on common engineering values and theoretical derivations.
[0066] Table 5
[0067] S4, see reference Figure 1 ,use The program implements a big data sampling simulation with a sample size in the hundreds of thousands, simulating the failure probability over the entire lifespan; the specific fitting steps are as follows: S41. Based on the principle of data sampling simulation, the following is adopted: The program simulates the degradation of the failure probability of a concrete gravity dam over time.
[0068] S42. Simulation Result Analysis: See [link / reference] Figure 2 ,run After using the software, a graph showing the cumulative failure probability of concrete gravity dams was obtained. Analysis of the simulation results led to the following conclusions: S421, Trend: In the early stage (0-30 years): the failure probability rises rapidly from 0 to about 0.8, indicating that in the early stage of structural service, the deterioration mechanism (material aging, environmental corrosion, load accumulation, etc.) has a strong effect and the reliability decreases significantly.
[0069] Mid-term (30-60 years): The failure probability increases from about 0.8 to 0.95, and the growth rate slows down, indicating that some structures are approaching their limit and have limited remaining lifespan.
[0070] Later stage (60-100 years): The failure probability gradually approaches 1, indicating that most structures have failed or require large-scale maintenance after the design life.
[0071] S422, Engineering Recommendations: Early stage: Improve the grade of materials (high-performance concrete, anti-corrosion steel bars, etc.) during the design and construction stages to extend the plateau period with high reliability in the early stage.
[0072] Mid-term: Establish a regular inspection system in about 20 to 30 years, and use sensors to monitor indicators such as crack width, steel corrosion, and deformation.
[0073] Later stage: Replace or remove parts that cannot be economically reinforced to prevent disaster risks when the failure probability is close to 1.
[0074] In summary, the specific process for this step is as follows: exist Input T simulated time points into the code. and their corresponding failure probability values ; exist In the code, through The function is solved using the nonlinear least squares method, and the optimal parameter k is output, where k represents the average rate of failure occurring per unit time. By using the failure probability index model The original failure probability curve is then subjected to exponential fitting.
[0075] S5. Quantitatively analyze the effects of three optimization measures: improving the drainage system, treating the foundation, and increasing the height of the free slab, and output a comparison of the failure probabilities before and after optimization. Specifically, this includes: S51. Based on the preceding research and analysis, three optimization measures are proposed; refer to Table 6 for the specific measures' names, working principles, and operating methods: Table 6
[0076] S52. Verification of Optimization Measures: For each optimization measure, modify the parameters and rerun the program to simulate the failure probability of the concrete gravity dam. Refer to [link to relevant documentation]. Figure 3 , for the effect diagram of the optimization measures.
[0077] To evaluate the optimization effect, refer to Table 7, which is an analysis table of the three optimization measures. Engineering professionals can select appropriate optimization measures and formulate reasonable post-maintenance plans based on the characteristics of the project.
[0078] Table 7
[0079] In summary, the present invention has the following advantages: 1. Improved simulation accuracy through multi-factor coupled modeling, encompassing multiple sources of uncertainty such as material degradation, hydrology, and earthquakes. Programmatically perform 100,000 rapid simulations of large datasets to improve prediction accuracy; 2. Wide range of applications: Concrete gravity dams in various fields such as hydropower generation, flood control and disaster reduction, and navigation can be used to simulate the failure probability within the design cycle by adjusting the code according to the dam body and environmental parameters. 3. The effectiveness of optimization schemes can be quantified, providing a quantitative display of the simulation effects of various optimization measures, which makes it easier for researchers to select more scientific and efficient optimization schemes based on the characteristics of the project.
[0080] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for predicting and optimizing the long-term failure probability of concrete gravity dams, characterized in that, include: S1. Construct a material degradation model and a load calculation model. The material degradation model describes the time-varying degradation law of the dam body and foundation material properties, and the load calculation model quantifies the randomness and time-varying nature of hydrological and seismic loads. S2. Define the logical judgment criteria for three failure modes: anti-slip failure, anti-overturning failure, and roof-overturning failure. S3. Based on the model in step S1 and the criteria in step S2, statistical methods are used to calculate the instantaneous failure probability in a single year and the cumulative failure probability over the entire lifespan. S4, Adopt Implement big data sampling simulation through programming to simulate and calculate the failure probability throughout the entire lifespan; S5. Quantitatively analyze the effects of three optimization measures: improving the drainage system, treating the foundation, and increasing the height of the free slab, and output the comparison results of the failure probability before and after optimization.
2. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The material degradation models include: a concrete compressive strength degradation model, an uplift pressure coefficient growth model, and a dam foundation contact surface friction coefficient and cohesion degradation model.
3. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 2, characterized in that, The concrete compressive strength degradation model The expression is: in, Let be the compressive strength of the concrete at time t. This represents the average initial compressive strength of the concrete (MPa). The annual degradation rate of concrete compressive strength. This refers to the length of service.
4. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 2, characterized in that, The uplift pressure coefficient growth model The expression is: in, Let be the pressure coefficient at time t. This is the initial uplift pressure coefficient. To increase the annual growth rate of pressure; The friction coefficient and cohesion degradation model adopts an exponential degradation model, and the formula for the time-varying value of the friction coefficient is: The formula for the time-varying value of cohesion is: in, Let be the friction coefficient of the dam foundation contact surface at time t. The initial friction coefficient, The annual degradation rate of the friction coefficient. Let t be the cohesion at the dam foundation contact surface. The initial cohesion, This represents the annual degradation rate of cohesion.
5. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The load calculation models include: hydrostatic pressure calculation model, extreme water level calculation model, and seismic inertial force calculation model; In the hydrostatic pressure calculation model, the normal water level height follows a normal distribution; in the extreme water level calculation model, the extreme water level follows a Gumbel distribution; and in the seismic inertial force calculation model, the number of earthquakes follows a Poisson distribution and the peak ground acceleration follows a normal distribution.
6. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The logical judgment criterion for anti-slip failure is: failure is determined when the sliding force is greater than or equal to the anti-slip resistance. The sliding force is the superposition of hydrostatic pressure and horizontal seismic force, where hydrostatic pressure... The calculation formula is: in, For hydrostatic pressure, The density of water (kN / kWh) ), g is the acceleration due to gravity (m / ), This represents the average normal water level (m). Standard deviation of normal water level (m); The anti-slip resistance The calculation formula is: in, For the dam's own weight, To increase pressure, Let be the friction coefficient of the dam foundation contact surface at time t. Let t be the cohesion at the dam foundation contact surface. This refers to the contact area at the bottom of the dam per unit width.
7. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The logical judgment criterion for anti-overturning failure is: failure is determined when the overturning moment is greater than or equal to the anti-overturning moment; The overturning moment The calculation formula is: The anti-overturning moment The calculation formula is: in, The height of the point of application of the resultant force of hydrostatic pressure. The height of the point of application of the resultant seismic force. For hydrostatic pressure, For horizontal seismic forces, For the dam's own weight, This refers to the width of the dam base.
8. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The logical judgment criterion for the failure of the overtopping is: when the water level elevation combination is greater than or equal to the effective dam crest elevation, it is judged as a failure; The water level elevation combination The expression is: in, This is the normal water level. This is the extreme water level. Random fluctuations caused by waves and wind congestion; The effective dam crest elevation The expression is: in, Design elevation for dam crest, Increase the height of the free plate.
9. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The formula for calculating the instantaneous failure probability in a single year is as follows: in, The sample size is [number of samples]. Let represent the total failure state of the i-th sample in year t.
10. The method for predicting and optimizing the long-term failure probability of concrete gravity dams according to claim 1, characterized in that, The sample size of the big data sampling simulation is on the order of hundreds of thousands. software The function implements the nonlinear least squares method to solve the problem and outputs the optimal parameters of the cumulative failure probability exponential model.