High earth and rockfill dam multi-dimensional risk assessment method and system

Abstract

The invention discloses a multi-dimensional risk assessment method and system for a high earth and rockfill dam, comprehensively and systematically assesses the risk of the earth and rockfill dam and ensures the safety of the dam body, and the method comprises the following steps: collecting the operation data of the earth and rockfill dam, covering the dam body structure, geological conditions and hydro meteorological information; risk sources are identified and classified, and a comprehensive risk assessment index system is constructed; constructing a model by adopting an analytic hierarchy process, and determining the weight of each risk factor by utilizing an entropy weight method; calculating a comprehensive risk value through a risk matrix method, and performing dam break risk analysis in combination with a Bayesian network method; and corresponding grading prevention and control measures are made according to the risk grades so as to optimize management and maintenance of the dam body. According to the invention, a scientific and accurate risk assessment result can be provided, and the efficiency and effectiveness of dam body safety management are improved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project safety management technology, and in particular to a multi-dimensional risk assessment method and system for high earth-rock dams. Background Technology

[0002] Earth-rock dams, as large-scale water conservancy infrastructure, play a vital role in flood control and disaster reduction, agricultural irrigation, hydropower generation, and water resource allocation. Due to their massive size, complex structure, and variable operating environment, the safety management of earth-rock dams has always been a crucial issue in the engineering field. Especially during the construction and operation of high earth-rock dams, the stability of the dam structure, the safety of the dam foundation, and changes in hydrological and meteorological conditions all have a significant impact on the safe operation of the dam.

[0003] During the operation of high earth-rock dams, common risks include flooding, dam seepage, and dam foundation landslides. With the increasing frequency of extreme weather and natural disasters, the probability of floods, earthquakes, and other natural disasters is rising, making the risks faced by earth-rock dams more complex and unpredictable. Traditional safety management methods often focus on the analysis of single factors, neglecting the interaction of multiple risk factors. As demands evolve, research on dam safety risks both domestically and internationally is gradually shifting from single-factor analysis to multi-dimensional, comprehensive assessments.

[0004] Existing risk assessment technologies have limitations and are difficult to fully adapt to the complex operational scenarios of high earth-rock dams. First, they lack the ability to integrate multi-dimensional risks. Although most methods incorporate multiple indicators, they do not fully address the coupling mechanisms between structural, geological, and hydrological risks, and easily overlook the cascading disasters caused by overlapping risks. Second, they have weak dynamic response capabilities. Traditional assessments are mostly based on historical data or periodic monitoring results, and cannot integrate online monitoring data during dam operation in real time, making it difficult to capture the real-time evolution of risks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-dimensional risk assessment method and system for high earth-rock dams, solving the problems of multi-dimensional risk coupling and weak dynamic response in high earth-rock dams, supporting dam break risk path analysis, providing a basis for comprehensive risk assessment, and meeting the needs of complex scenarios.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by the present invention includes two aspects, as detailed below:

[0007] Firstly, a multi-dimensional risk assessment method for high earth-rock dams includes the following steps:

[0008] Step 1, Data Collection and Analysis:

[0009] Collect operational data on earth-rock dams, including dam structure, geological conditions, and hydrological and meteorological data.

[0010] Step 2, Risk Identification and Classification:

[0011] Identify and classify risk sources.

[0012] Step 3, Determining the Risk Assessment Indicator System and Weights:

[0013] A risk assessment indicator system was constructed, and a risk evaluation model was built using the analytic hierarchy process (AHP) to initially determine the weights. The weights of each risk factor were then recalculated using the entropy weight method. By combining subjective and objective methods, the credibility of the weight results was improved.

[0014] Step 4, Multi-dimensional Risk Assessment and Analysis:

[0015] By progressively analyzing dam failure risks through probability analysis, comprehensive assessment, and path deduction, the probability characteristics of different risk types are clarified; a comprehensive risk value is calculated using the risk matrix method to conduct a comprehensive risk assessment; and dam failure risk analysis is carried out in conjunction with the Bayesian network method.

[0016] Step 5, Dynamic Risk Monitoring and Feedback:

[0017] Real-time monitoring data is input into the risk assessment model, and tiered prevention and control measures are formulated based on the risk assessment results and risk levels.

[0018] Furthermore, the operational data also includes historical flood data, dam seepage pressure, dam foundation deformation monitoring data, and population and property distribution data in downstream areas.

[0019] Furthermore, the identification of risk sources includes historical data comparison and analysis, and real-time monitoring data analysis; the identified risk sources are classified according to their causes, including natural risks, human risks, and structural risks, and a specific list of risk sources is compiled.

[0020] Furthermore, it also includes classifying the identified risk sources according to their intermediate transmission routes to construct dam failure causal chains and identify risk evolution paths.

[0021] Furthermore, in the probability analysis, the probability calculation of natural risks uses statistical models to analyze the frequency of occurrence of natural risks of floods and rainstorms, and combines historical data and climate predictions to estimate the probability of future risks; hydrological models are applied to simulate rainfall and runoff within the watershed to predict flood risks.

[0022] Structural risk analysis involves analyzing the key components of the dam, including the dam foundation, dam body, and spillway, to assess their safety under earthquakes or other stresses; and using the finite element method to calculate the stress and strain of the dam body to assess its resistance to sliding, cracking, and seepage.

[0023] Human risk probability assessment analyzes the human risks that occur in the daily maintenance, operation and management of dams, including management negligence and equipment aging, and assesses their probability of occurrence in combination with historical data.

[0024] Furthermore, the risk matrix method for calculating the comprehensive risk value includes constructing a risk matrix based on the probability and potential impact of various risks, and classifying the risk levels.

[0025] The comprehensive risk level of the dam is calculated by weighting each risk source using a linear weighting method.

[0026] Based on weighted comprehensive calculations, risk factors, risk elements, and assessment objects are classified into risk levels, and corresponding response measures and action plans are formulated for each level.

[0027] Furthermore, the dam failure risk analysis using the Bayesian network method specifically includes:

[0028] Construct a Bayesian network model to define the relationships between various risk factors and form a causal chain for dam failure; determine the contribution of each factor to dam failure and analyze the probability of dam failure.

[0029] Sensitivity analysis of the model was performed using the Bayesian network method to identify key risk factors affecting dam failure.

[0030] By using reverse reasoning analysis, we can identify the effective types and key points of early prevention measures, reduce the risk of dam failure, and optimize emergency response plans.

[0031] Furthermore, the sensitivity analysis identifies key risk factors affecting dam failure by changing the probability table of the selected node SN and examining the changes in the probability table of the target node TN. The importance index I is calculated using the following formula:

[0032]

[0033] Where P(TN) is the probability of the target node, It is the conditional probability of the target node under the change of the selected node; the importance index represents the degree of influence of the node on the danger or failure of the dam, and reflects the negative impact of the node under the condition of not meeting the requirements and the frequency of its occurrence.

[0034] Furthermore, the aforementioned tiered prevention and control measures based on risk levels specifically include:

[0035] Automatic monitoring equipment is installed at key locations of the dam to obtain real-time dam operation data;

[0036] Regularly input real-time monitoring data into the risk assessment model to update the risk assessment results and identify risk change trends;

[0037] Risk control measures should be adjusted promptly based on monitoring results. If the risk level increases, the emergency plan should be activated immediately and additional prevention and control measures should be taken.

[0038] Preventive measures include routine monitoring, reinforcement projects, and emergency response plans.

[0039] Secondly, a system for multidimensional risk assessment of high earth-rock dams includes:

[0040] Data collection and processing module: including dam structure data collection, geological condition survey, hydrological and meteorological data collection, and social and environmental data collection;

[0041] Risk identification and classification module: includes historical data analysis, real-time monitoring data analysis, and risk source classification;

[0042] Risk assessment indicator system module: including indicator selection and setting, assessment model construction and weight calculation;

[0043] Risk occurrence probability analysis module: includes natural risk probability calculation, structural risk analysis, and human risk assessment;

[0044] Comprehensive risk assessment module: includes risk matrix construction, weighted comprehensive calculation, and risk level classification;

[0045] The Bayesian network-based dam failure risk analysis module includes Bayesian network modeling, sensitivity analysis, and reverse reasoning and emergency optimization.

[0046] Dynamic risk monitoring and feedback module: This includes the construction of a real-time monitoring system, data analysis and updates, and feedback and response adjustments.

[0047] The beneficial effects of this invention are:

[0048] 1. Enhanced multi-dimensional coupling assessment: In response to the problem of insufficient integration of multi-dimensional risks in traditional methods, this invention systematically integrates dimensions such as dam structure, geological conditions, hydrology and meteorology, and social environment, deeply analyzes the coupling mechanism between structural, geological and hydrological risks, accurately identifies the chain disasters caused by cross risks, and provides a comprehensive risk perspective.

[0049] 2. Improve the accuracy of assessment results: Through the prior systematic data collection, risk identification and classification, and the construction of a scientific indicator system, combined with the subjective and objective weighting verification of the analytic hierarchy process and the entropy weight method, the shortcomings of traditional assessments, such as the lack of consideration for coupled risks and the single determination of weights, are made up for, and more accurate risk assessment results are output, helping decision-makers to quickly locate core risk points.

[0050] 3. Enhance dynamic early warning and response: Overcome the limitations of traditional methods in dynamic response, integrate online real-time monitoring data to conduct dynamic risk assessment, promptly capture dam anomalies and real-time risk evolution, provide accurate early warnings, and improve emergency response efficiency.

[0051] 4. Optimize decision support: Based on the dam failure causal chain constructed by Bayesian network, combined with risk matrix construction and weighted comprehensive calculation for comprehensive risk assessment, it provides scientific decision support with both logical support and data basis for dam management and maintenance, and helps to formulate more targeted risk control and emergency measures.

[0052] 5. Enhance risk management: By comprehensively utilizing advanced methods such as the Analytic Hierarchy Process (AHP), Entropy Weight Method (IHP), and Bayesian Network Analysis, we can achieve a scientific approach to the entire process of risk weighting, causal analysis, and evolution tracking, significantly improving the scientific, systematic, and rigorous nature of risk management.

[0053] 6. Effective preventive measures: Through backward reasoning and sensitivity analysis using Bayesian networks, the method can identify key risk factors and optimize preventive measures, effectively reducing the risk of dam failure.

[0054] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0055] Figure 1 This is a flowchart of the multidimensional risk assessment method for high earth-rock dams in Embodiment 1 of the present invention;

[0056] Figure 2 This is a structural diagram of the list of risk sources for the operation of a hydropower station dam in Embodiment 1 of the present invention;

[0057] Figure 3 This is a flowchart of the risk level assessment process in Embodiment 1 of the present invention;

[0058] Figure 4 This is a Bayesian network flowchart analysis diagram of the overtopping dam failure in Embodiment 1 of the present invention;

[0059] Figure 5 This is the risk level chart in step 5 of embodiment 1 of the present invention;

[0060] Figure 6 This is a chart of important indicators for the overtopping and dam failure of a hydropower station in step 6 of embodiment 1 of the present invention;

[0061] Figure 7 This is a schematic diagram of the hierarchical structure model in step 3 of embodiment 1 of the present invention. Detailed Implementation

[0062] Example 1:

[0063] This embodiment presents a multidimensional risk assessment method for high earth-rock dams, such as... Figure 1 As shown, it includes the following steps:

[0064] Step 1, Data Collection and Analysis:

[0065] 1.1 Dam structure data acquisition:

[0066] Investigate the dam's design drawings to understand its building materials, structural form, construction quality, and other parameters.

[0067] The internal structural model of the dam was reconstructed using 3D modeling technology, and its strength, stability and other characteristics were analyzed.

[0068] 1.2 Geological Conditions Survey:

[0069] Conduct geological surveys of the dam area and its surrounding areas to identify potential geological risks such as landslides and faults.

[0070] Key monitoring should be conducted on the dam foundation, reservoir area, and near-dam slope to assess potential geological hazards.

[0071] 1.3 Hydrological and meteorological data collection:

[0072] Collect historical rainfall and flood data to analyze rainfall and runoff patterns within the watershed.

[0073] Monitor the current meteorological conditions in the dam area, paying particular attention to changes during the flood season and rainy season.

[0074] 1.4 Social and Environmental Data Collection:

[0075] Collect information on population distribution, economic activities, and land use in downstream areas to assess the impact of dam accidents on downstream society and the environment.

[0076] Assess the sensitivity of ecosystems, such as downstream wetlands and habitats of rare species.

[0077] Step 2, Risk Identification and Classification:

[0078] 2.1 Historical Data Comparison and Analysis:

[0079] By utilizing the dam's historical operation records and maintenance data, potential hidden dangers and problems in the dam body can be identified; by comparing similar earth-rock dam failure cases, lessons can be learned from their experience in identifying and preventing risk points.

[0080] 2.2 Real-time monitoring data analysis:

[0081] Use sensors and monitoring systems to acquire real-time data on the dam body, such as seepage pressure, displacement, and deformation of the dam foundation and dam surface; compare with historical monitoring data to analyze the changing trends of key parameters and identify potential risks.

[0082] 2.3 Risk Source Classification:

[0083] To identify risk evolution paths, quantify risk levels, and construct dam failure causal chains, risk sources are classified according to risk triggers, intermediate transmission, and harmful consequences, supporting risk factor correlation analysis and dam failure path tracing.

[0084] According to the risk causes, they are classified as natural causes (such as floods, earthquakes, rainstorms, landslides, debris flows, strong wind erosion, etc.), human causes (such as construction defects, improper maintenance, illegal water storage exceeding standards, disturbance from surrounding projects, etc.), and material and structural causes (such as deterioration of dam fill material, damage to seepage prevention material, leakage of dam foundation, blockage of drainage system, etc.).

[0085] According to the intermediate transmission, the risks include: dam body risks (such as dam slope instability, cracks, seepage, piping, and soil erosion), dam foundation risks (such as foundation settlement, seepage, and deformation), and risks of auxiliary structures (such as spillway blockage, sluice gate failure, and slope damage).

[0086] Classified by hazard outcome: including core disaster risk (dam failure risk), functional failure risk (such as loss of flood discharge capacity, failure of water retention function, etc.), and secondary disaster risk (such as flood overflow caused by dam failure, chain reaction of geological disasters, etc.).

[0087] Based on the actual situation, a specific list of risk sources is compiled according to their carriers and causes. A risk source list structure diagram for a certain power station is shown below. Figure 2 As shown, each item will be analyzed in detail, as follows:

[0088] (1) Risks associated with water-retaining structures:

[0089] Water-retaining structures are the primary line of defense for a hydropower station, bearing the crucial responsibility of blocking water flow and ensuring the safety of reservoir storage. After detailed analysis and comprehensive assessment, issues such as calcium leaching in the dam foundation gallery and transverse cracks in the dam top were identified, and these potential risks were meticulously recorded in the risk source list.

[0090] (2) Risks associated with spillway structures:

[0091] Spillway structures play a crucial role in regulating water flow and preventing floods in a certain hydropower station. During the analysis, attention was paid to issues that arose during the long-term operation of the spillway structures, including seepage in the emptying tunnel, leakage in the No. 1 spillway, the design of the spillway, cracks in the gate chamber, and erosion of the downstream slope. These risk sources were included in the risk source list.

[0092] (3) Risks to reservoir banks and slopes:

[0093] The geographical environment of a certain hydropower station is complex and variable, and the stability of the reservoir bank and slopes is crucial to the safety of the entire hydropower station. A comprehensive assessment was conducted on the risks of natural disasters such as near-dam reservoir bank landslides and slope instability in the key area, and these risk sources were listed in detail in a risk source inventory.

[0094] (4) Risks related to gates and hoists:

[0095] Gates and hoists are critical components in the daily operation of hydropower stations. Potential risks, such as gate mechanical failures and power supply issues, were analyzed and detailed in a risk source list.

[0096] (5) Operational and management risks:

[0097] Operation and management are also important safeguards for the safety of hydropower stations. This paper analyzes the potential risks at the management level and includes these issues in the risk source list.

[0098] (6) Natural disasters:

[0099] Natural disaster risks include factors such as floods exceeding standard levels, upstream cascade dam failures or abnormal water discharges, torrential rains, and earthquakes exceeding standard levels.

[0100] Step 3, Determining the Risk Assessment Indicator System and Weights:

[0101] 3.1 Selecting risk assessment indicators:

[0102] Risk assessment indicators are defined according to the classification of risk sources (risk carrier, risk trigger, intermediate transmission, and harmful consequences). Risk trigger and intermediate transmission indicators clarify the causal starting point and trigger probability of dam failure, build a process bridge from trigger to dam failure, and serve the purpose of path analysis design. Harmful consequences indicators quantify the final consequences of dam failure and provide a result reference for path analysis and comprehensive risk assessment. Risk carrier indicators locate the objects to which the risk is attached and their risk resistance capabilities, and support the purpose of comprehensive risk assessment design.

[0103] Trigger-related indicators include the probability of floods exceeding standard levels, the incidence of construction quality defects, and the probability of earthquakes / rainstorms triggering events.

[0104] Intermediate transmission indicators include flood level and rise rate, dam deformation rate, seepage pressure value, foundation stability safety factor, and discharge capacity attenuation rate.

[0105] Outcome-related indicators include dam failures and functional failures.

[0106] The carrier-related indicators include the integrity rate of water-retaining structures, the soundness rate of water-discharging structures, the stability level of slopes / reservoir banks, the integrity rate of gates and hoists, and the soundness rate of the operation and maintenance system;

[0107] Select key risk assessment indicators as evaluation indicators based on the actual situation.

[0108] 3.2 Setting Indicator Standards:

[0109] Threshold standards for each risk indicator are set based on relevant regulations, standards, and expert opinions. For example, flood levels exceeding design flood standards and seepage pressure exceeding safety limits.

[0110] 3.3 Evaluation Model Construction and Weight Calculation Based on Analytic Hierarchy Process (AHP):

[0111] By employing the Analytic Hierarchy Process (AHP), a judgment matrix can be established for each risk factor (criteria level) and risk factor (option level), or other adaptation methods can be selected to construct a multi-level risk assessment model.

[0112] The sub-steps for determining weights using the Analytic Hierarchy Process (AHP) are as follows:

[0113] 3.3.1. Constructing a hierarchical model:

[0114] Based on the complexity of the risks being assessed, risk assessment issues are categorized into different levels, such as... Figure 7 As shown, it is divided into the target layer (such as comprehensive risk assessment), the criterion layer (i.e. risk factors, such as flood risk, structural risk, geological risk, etc.) and the scheme layer (i.e. risk factors, such as flood level, dam deformation, etc.).

[0115] Each risk factor is decomposed into a structured hierarchical model, from the overall to the specific.

[0116] 3.3.2. Constructing the judgment matrix:

[0117] For each risk factor at the same level, pairwise comparisons are made to assess their relative importance. A 9-point scale is used, where 1 indicates that both are equally important and 9 indicates that one is extremely important.

[0118] Construct a judgment matrix, compare each risk factor with other factors in pairs, and fill in the corresponding relative importance values.

[0119] 3.3.3. Calculate the eigenvectors and weights:

[0120] The relative weight of each risk factor is calculated using a judgment matrix, with specific methods including the eigenvector method or the geometric mean method.

[0121] The calculated eigenvectors are the weight vectors of each risk factor.

[0122] 3.3.4. Consistency check:

[0123] To ensure the rationality of the judgment matrix construction, the consistency ratio (CR) is calculated. The formula for calculating the consistency ratio is as follows:

[0124] (1)

[0125] Where CI is the consistency index and RI is the random consistency index.

[0126] If the consistency ratio CR < 0.1, the judgment matrix passes the consistency test; if CR > 0.1, the judgment matrix needs to be adjusted.

[0127] 3.3.5. Final weight allocation:

[0128] Based on the calculated weight vector, the final weights of each risk factor are assigned.

[0129] The weight allocation was confirmed to be in line with the actual situation, and further adjustments and verifications were made based on expert opinions.

[0130] 3.4 Weight Calculation Based on Entropy Weight Method:

[0131] Through expert evaluation and entropy weighting, the importance weights of each risk factor are verified or revised again, taking into account both subjective experience and objective data support, to ensure that the weight allocation is scientific and reasonable.

[0132] Entropy weighting is an objective weighting method that reduces the subjectivity of weight determination. This method assigns weights by measuring the degree of information orderliness in the evaluation indicators. The better the information orderliness of an indicator, the greater its relative weight. Entropy value is the core basis for judging this degree of orderliness; the smaller the entropy value of an indicator, the stronger its information orderliness and the more effective information it contains, and the greater its corresponding weight. Based on the idea of ​​entropy value, the specific analysis method of objective weighting using entropy weighting is as follows.

[0133] Suppose a certain evaluation index system has There are 10 evaluation indicators, and each evaluation indicator has 100 evaluation indicators. If there are original data, then an initial matrix can be used. To represent, the matrix obtained after standardizing the initial matrix is... , No. The formula for calculating the information entropy of each evaluation indicator is as follows:

[0134] (2)

[0135] (3)

[0136] in, To standardize the weighting of data, For the standardized matrix The element in, when the first If the information content of each evaluation indicator is completely disordered, then the information entropy... Maximum, that is Therefore, the first The weight of each evaluation indicator depends on the coefficient of variation. ,Right now:

[0137] (4)

[0138] in, For the first The coefficient of difference for each evaluation indicator will After standardization, we can obtain the first... The corresponding objective weight values ​​for each evaluation indicator are:

[0139] (5)

[0140] Step 4, Multi-dimensional Risk Assessment and Analysis:

[0141] 4.1 Risk Occurrence Probability Analysis:

[0142] 4.1.1 Probability calculation of natural risks:

[0143] Statistical models are used to analyze the frequency of natural risks such as floods and rainstorms, and historical data and climate predictions are combined to estimate the probability of future risks.

[0144] Hydrological models are used to simulate rainfall and runoff within the watershed to predict flood risk.

[0145] 4.1.2 Structural Risk Analysis:

[0146] Structural analysis is conducted on key components of the dam, such as the dam foundation, dam body, and spillway, to assess their safety under earthquakes or other stresses.

[0147] The finite element method was used to calculate the stress and strain of the dam body and to evaluate its anti-sliding, anti-cracking, and anti-seepage capabilities.

[0148] 4.1.3 Human-caused risk probability assessment:

[0149] Analyze potential human risks in the daily maintenance, operation and management of the dam, such as management negligence and equipment aging, and assess their probability of occurrence based on historical data.

[0150] 4.2 Comprehensive Risk Assessment:

[0151] 4.2.1 Constructing a risk matrix:

[0152] A risk matrix is ​​constructed based on the probability of occurrence and the magnitude of potential impact of various risks, and risk levels are classified. The risk level table is as follows: Figure 5 As shown:

[0153] This embodiment describes the establishment of a risk matrix and analysis method for the safe operation of a hydropower station dam. The risk matrix method is based on the specific circumstances of the project. Different risk matrices need to be established when conducting risk assessment and analysis for different projects.

[0154] 4.2.2 Weighted composite calculation:

[0155] The overall risk level of the dam is calculated by weighting each risk factor using a linear weighting method.

[0156] The linear weighted aggregation method uses a linear model to simply aggregate the weights and evaluation index values. The calculation formula is as follows:

[0157] (6)

[0158] Where y is the comprehensive evaluation value of the evaluated scheme after the evaluation information is aggregated by the linear weighted synthesis method; For the first Evaluation indicator values; The weight corresponding to the j-th indicator satisfies 0 ≤ ≤1 (j=1, 2, (m), and

[0159] 4.2.3 Risk Level Classification:

[0160] like Figure 3 As shown, based on weighted comprehensive calculation, risk factors, risk elements, and assessment objects are classified into risk levels, and corresponding response measures and action plans are formulated for each level.

[0161] 4.3 Dam Failure Risk Analysis Based on Bayesian Networks:

[0162] Bayesian network-based dam-break risk analysis uses a graphical probabilistic model framework to transform complex dam-break-related risk factors (flood level, dam deformation, seepage pressure, etc.) into interconnected nodes. Directed edges clearly define the causal transmission relationships between these factors, and probabilistic quantification addresses the uncertainty of these relationships. It systematically integrates fragmented risk information, clearly presenting the complete path from trigger to intermediate transmission links to dam break. By calculating the probability of each path's occurrence, it identifies the key nodes with the greatest impact on dam breakage, providing precise quantitative evidence for risk prevention and decision-making.

[0163] 4.3.1 Bayesian Network Modeling:

[0164] like Figure 4 As shown, a Bayesian network model is constructed to define the relationships between various risk factors, forming a causal chain for dam failure.

[0165] Determine the contribution of each factor to dam failure and analyze the probability of dam failure.

[0166] 4.3.2 Sensitivity Analysis:

[0167] Sensitivity analysis of the model was performed using the Bayesian network method to identify key risk factors affecting dam failure and to focus on monitoring these factors.

[0168] This is obtained by changing the probability table of the selected node and examining the changes in the probability table of the target node. The importance of the selected node SN to the target node TN is represented by the importance index I, and the calculation formula is as follows:

[0169] (7)

[0170] Where P(TN) is the probability of the target node, This represents the conditional probability of the target node under changes in the selected nodes. The importance index indicates the degree of a node's impact on potential hazards or dam failures, reflecting the negative impact and frequency of a node's failure to meet requirements. In other words, both a larger negative impact and a higher frequency of occurrence of a node's failure to meet requirements increase the node's importance.

[0171] This paper analyzes a Bayesian network of a hydropower station's overtopping dam failure, using dam failure as the target node and above-standard earthquakes as the selection node. Assuming an above-standard earthquake occurs (P(above-standard earthquake) = 1), the probability in the Bayesian network is automatically updated: P(dam failure | above-standard earthquake occurrence) = 9.281 × 10⁻⁶. -5 The dam failure factor P (dam breakage) has already been calculated as 7.58 × 10⁻⁶. -7 Therefore, the importance indicator for dam failure caused by earthquakes exceeding the standard is:

[0172] (8)

[0173] Similarly, the importance indicators of all nodes to the dam failure can be calculated, and the results are as follows: Figure 6 As shown, based on the important indicators of this hydropower station, it is possible to identify the key parameters that could cause the dam to overflow and fail. Based on these parameters, targeted management can be strengthened.

[0174] The following is Figure 6 In China, there are six possible paths for dam overtopping to fail:

[0175] Path 1: An earthquake exceeding standard occurs → the spillway tunnel structure is damaged → the spillway tunnel cannot discharge water normally → the dam overflows → the dam body is eroded → intervention is ineffective → the dam breaks.

[0176] Path 2: An earthquake exceeding the standard occurs → all power supplies to the gates fail → the gate hoists cannot work properly → water cannot be discharged normally → dam overflows → dam body is eroded → intervention is ineffective → dam collapses.

[0177] Path 3: Upstream cascade dam failure or abnormal water release → PMF flood → dam overflow → dam scour → ineffective intervention → dam failure.

[0178] Path 4: Localized torrential rain in the hub area → super PMF flood → dam overflow → dam erosion → ineffective intervention → dam failure.

[0179] Path 5: Flood control scheduling error → Water level continues to rise → Dam overflow → Dam erosion → Intervention ineffective → Dam collapse.

[0180] Path 6: Large error in flood forecasting → continuous rise in water level → dam overflow → dam erosion → ineffective intervention → dam failure.

[0181] 4.3.3 Reverse Reasoning and Emergency Optimization:

[0182] By using reverse reasoning analysis, we can identify the effective types and key points of early prevention measures, reduce the risk of dam failure, and optimize emergency response plans.

[0183] based on Figure 6 Among the important indicators, the two most important influencing factors are the flood control scheduling errors in Path 5 and the large error in the flood forecast in Path 6. The second most important factors are the chain reaction caused by the occurrence of super-standard earthquakes in Path 1 and 2, which leads to overtopping and dam failure. Finally, the super-standard floods caused by local rainstorms in Path 4 and upstream cascade dam failures or abnormal water discharge in Path 3 lead to overtopping and dam failure.

[0184] To address the issues of errors in flood control scheduling and large margins in flood forecasting, the primary task is to strengthen flood control scheduling and hydrological forecasting management. This includes enhancing the professional capabilities of scheduling personnel through regular training to ensure they can accurately assess hydrological trends and make timely and appropriate scheduling actions. Furthermore, it involves establishing and improving flood forecasting models, continuously optimizing model parameters by combining historical data and real-time monitoring, and enhancing the ability to predict future flood events.

[0185] To address potential flood discharge disruptions caused by earthquakes exceeding standard levels, maintain the backup power system to ensure a rapid switch to backup power in the event of a main power failure due to an earthquake, thus guaranteeing the normal operation of the flood discharge facilities. Simultaneously, strengthen the daily maintenance of the flood discharge tunnel structure and the exploration and assessment of the surrounding geological structure to ensure the earthquake resistance effectiveness of the flood discharge facilities.

[0186] Strengthen information sharing and coordination mechanisms with upstream cascade reservoirs, establish a real-time data exchange platform, and ensure that downstream reservoirs can promptly grasp the operational status and discharge plans of upstream reservoirs to prepare for flood control in advance. Enhance the monitoring and early warning capabilities for localized torrential rains, utilizing monitoring networks such as meteorological radar and rain gauges to achieve accurate forecasts of torrential rains, providing sufficient lead time for flood control scheduling. In response to the risks of upstream cascade dam failures or abnormal discharges, conduct regular emergency drills based on established emergency plans to improve the ability to respond quickly and handle sudden disasters, including measures such as emergency evacuation and temporary reinforcement of dikes.

[0187] By strengthening operation management, improving forecast accuracy, and enhancing emergency response capabilities, we can effectively reduce the threat of various risk factors to reservoir safety and ensure the safe and stable operation of reservoirs under extreme conditions.

[0188] Step 5, Dynamic Risk Monitoring and Feedback:

[0189] 5.1 Construction of a real-time monitoring system:

[0190] Automatic monitoring equipment is installed at key locations on the dam to obtain real-time data on dam operation, such as water level, seepage flow, and displacement.

[0191] 5.2 Data Analysis and Updates:

[0192] Regularly input real-time monitoring data into the risk assessment model to update the risk assessment results and identify risk change trends.

[0193] 5.3 Feedback and Response Adjustments:

[0194] Risk control measures should be adjusted promptly based on monitoring results. If the risk level increases, the emergency plan should be activated immediately and additional prevention and control measures should be taken.

[0195] Prevention and control measures include:

[0196] 5.3.1 Structural reinforcement measures:

[0197] The dam body was reinforced, including adding seepage prevention facilities, repairing cracks in the dam body, and improving the dam body's resistance to sliding.

[0198] Strengthen the dam foundation, such as by adding seismic bracing and reinforcing weak parts of the foundation, to ensure its stability.

[0199] 5.3.2 Drainage and flood discharge measures:

[0200] The spillway was activated to control the reservoir water level and prevent excessive water levels from causing flooding.

[0201] Adding temporary drainage facilities reduces the seepage pressure in the dam body and foundation, thereby reducing the risk of leakage;

[0202] Clean and unclog drainage pipes to ensure unobstructed drainage systems.

[0203] 5.3.3 Real-time monitoring and early warning:

[0204] Increase the density and frequency of monitoring equipment to track key parameters such as dam deformation, seepage pressure, and downstream water level in real time.

[0205] Establish a comprehensive early warning system to promptly issue risk warnings to relevant departments and downstream residents, and make preparations for personnel evacuation.

[0206] 5.3.4 Emergency Response and Personnel Evacuation:

[0207] Immediately activate the emergency response plan, organize relevant rescue teams and equipment to be on standby, and ensure that emergency supplies and equipment are fully prepared.

[0208] Based on the early warning information, downstream residents should be evacuated quickly to ensure their safety, especially in extremely high-risk areas.

[0209] Temporary shelters were set up to ensure the living and medical support for the evacuated population.

[0210] 5.3.5 Dam load reduction and water storage control:

[0211] By lowering the reservoir water level and controlling the inflow, the load on the dam body can be reduced, thereby reducing the risk of dam instability or landslides.

[0212] Stop or reduce reservoir water storage, especially during the rainy season or floods, to prevent water levels from exceeding design limits.

[0213] 5.3.6 Post-disaster recovery and assessment:

[0214] After the risk has been reduced, a detailed inspection and repair work will be carried out to repair the dam damage caused by emergency operations or natural disasters.

[0215] An assessment of the overall operation of the dam was conducted to ensure that it met safety standards before resuming operation.

[0216] This invention addresses the shortcomings of traditional dam risk assessment methods, such as insufficient multi-dimensional integration and weak dynamic response. It employs a series of scientific measures to improve assessment and management efficiency: First, it systematically integrates multi-dimensional elements including dam structure, geological conditions, hydrology and meteorology, and the social environment, deeply analyzing the risk coupling mechanisms of each dimension to accurately identify cross-chain disasters. Second, it establishes a solid assessment foundation through preliminary systematic data collection, risk classification, and a scientific indicator system. Third, it uses the analytic hierarchy process (AHP) to construct a model and assign initial weights, followed by objective calculation and verification using the entropy weight method, thus ensuring the scientific validity and credibility of each risk factor's weight. Fourth, it relies on risk matrix construction and weighted comprehensive calculation for a comprehensive risk assessment, using Bayesian network analysis to analyze the correlation logic between various risk factors and construct the causal chain of dam failure accidents. Finally, it integrates online real-time monitoring data to conduct dynamic assessments, promptly capturing risk evolution trends to achieve accurate early warning. This solution not only addresses the shortcomings of traditional methods, such as the lack of consideration for coupling risks, and outputs more accurate risk assessment results to help locate core risk points, but also optimizes preventive measures to reduce the risk of dam failure through Bayesian network reverse reasoning and sensitivity analysis. At the same time, it provides scientific decision support for dam management and maintenance, improves emergency response efficiency, and comprehensively enhances the scientific, systematic and effective nature of dam risk management.

[0217] Example 2:

[0218] This embodiment provides a multi-dimensional risk assessment system for high earth-rock dams. This system is used to implement the aforementioned multi-dimensional risk assessment method for high earth-rock dams, specifically including:

[0219] Data collection and processing module: including dam structure data collection (obtaining data from design drawings and reconstructing the internal structure of the dam through 3D modeling technology), geological condition survey (obtaining geological information of the dam area and its surroundings), hydrological and meteorological data collection (monitoring rainfall, flood data and meteorological conditions), and social and environmental data collection (obtaining information on downstream population distribution, economic activities, land use and ecosystems).

[0220] Risk identification and classification module: including historical data analysis (processing historical failure records of dams and similar earth-rock dam failure cases), real-time monitoring data analysis (acquiring and analyzing sensor data, such as seepage pressure, displacement, and dam surface deformation), and risk source classification (classifying and analyzing natural risks, human risks, and structural risks).

[0221] Risk assessment indicator system module: including indicator selection and setting (selecting key risk assessment indicators and setting threshold standards), assessment model construction (using methods such as the analytic hierarchy process (AHP) to build a risk assessment model), and weight calculation (determining indicator weights through expert evaluation and entropy weight method).

[0222] Risk occurrence probability analysis module: includes natural risk probability calculation (analyzing the probability of occurrence of natural risks such as floods and rainstorms), structural risk analysis (conducting structural analysis of key parts of the dam body), and human risk assessment (analyzing human risks in daily maintenance and management).

[0223] The comprehensive risk assessment module includes risk matrix construction (constructing a risk matrix based on the probability of occurrence and the magnitude of potential impact of various risks), weighted comprehensive calculation (weighting and summarizing risk sources to calculate the comprehensive risk level), and risk level classification (classifying risk levels based on the comprehensive calculation results and formulating countermeasures).

[0224] The Bayesian network-based dam failure risk analysis module includes Bayesian network modeling (constructing a Bayesian network model and defining the relationships between risk factors), sensitivity analysis (identifying key risk factors affecting dam failure), and reverse reasoning and emergency optimization (analyzing the effectiveness of preventive measures and optimizing emergency plans).

[0225] Dynamic risk monitoring and feedback module: This includes the construction of a real-time monitoring system (installing automatic monitoring equipment to acquire dam operation data in real time), data analysis and updating (inputting real-time data into the risk assessment model and updating the risk assessment results), and feedback and response adjustment (adjusting risk control measures and activating emergency plans based on monitoring results).

[0226] User interface and report generation module: including data visualization (providing a graphical interface to display risk assessment results and data analysis) and report generation (automatically generating detailed risk assessment reports, supporting the generation of regular and ad hoc reports).

[0227] System Management and Maintenance Module: Includes user access management (setting system user permissions and access control) and system maintenance and upgrade (performing system maintenance, software upgrades, and troubleshooting).

[0228] Example 3:

[0229] This embodiment discloses a terminal device for a multi-dimensional risk assessment system for high earth-rock dams. The terminal device includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used for the operation of a multi-dimensional risk assessment method for high earth-rock dams.

[0230] Example 4:

[0231] This embodiment discloses a storage medium for a multi-dimensional risk assessment system for high earth-rock dams, specifically a computer-readable storage medium (Memory). This Memory is a memory device within a terminal device used to store programs and data. It is understood that the Memory can include both the built-in storage medium of the terminal device and extended storage media supported by the terminal device. The Memory provides storage space containing the terminal's operating system. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the Memory can be high-speed RAM or non-volatile memory, such as at least one disk drive.

[0232] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multidimensional risk assessment method for high earth-rock dams in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by a processor.

[0233] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0235] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0236] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0237] Finally, it should be noted that the above is only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as the application of various formulas, the order of steps, etc.) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high earth-rock dam multi-dimensional risk assessment method, characterized in that, The method comprises the following steps: Step 1, data collection and analysis: Collecting operation data of the earth-rock dam, including dam structure, geological conditions and hydro-meteorological conditions; Step 2, risk identification and classification: Identifying risk sources and classifying them; Step 3, risk assessment index system and weight determination: Building a risk assessment index system, using the analytic hierarchy process to build a risk evaluation model and preliminarily determining the weight, and using the entropy weight method to recalculate the weight of each risk factor, complementing the subjective and objective methods to improve the credibility of the weight results; Step 4, multi-dimensional risk assessment analysis: Through probability analysis-comprehensive evaluation-path deduction progressive analysis of dam failure risk, the occurrence probability characteristics of different risk types are clear; Calculate the comprehensive risk value by the risk matrix method, and conduct comprehensive risk assessment; Combine the Bayesian network method to analyze the dam failure risk; Step 5, dynamic risk monitoring and feedback: Input real-time monitoring data into the risk assessment model, and develop graded prevention and control measures based on the risk assessment results and risk levels.

2. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, In step 1, the operation data further includes historical flood data, dam body leakage pressure, dam foundation deformation monitoring data and downstream population and property distribution data.

3. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, In step 2, the identification of risk sources includes comparative analysis of historical data and real-time monitoring data; The identified risk sources are classified according to the causes, including natural risks, human risks and structural risks, and a specific list of risk sources is listed.

4. The high earth-rock dam multi-dimensional risk assessment method according to claim 3, characterized in that, The risk source classification also includes classifying the identified risk sources according to the intermediate transmission, which is used to build the dam failure causal chain and identify the risk evolution path.

5. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, In step 4, the probability calculation of natural risks in the probability analysis uses statistical model to analyze the occurrence frequency of natural risks of flood and rainstorm, combines historical data and climate prediction to estimate the future risk probability; Apply the hydrological model to simulate the rainfall runoff in the basin and predict the flood risk; Structural risk analysis is a structural analysis of the key parts of the dam, including the dam foundation, the dam body and the spillway, to evaluate its safety under the action of earthquake or other stress; use finite element analysis method to calculate the stress and strain of the dam body, and evaluate its anti-sliding, anti-cracking and anti-seepage ability; The probability evaluation of human risks is to analyze the human risks in the daily maintenance, operation and management of the dam, including management negligence and equipment aging, and to evaluate their occurrence probability combined with historical data.

6. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, In step 4, the risk matrix method for calculating the comprehensive risk value includes constructing a risk matrix for each type of risk according to the probability and potential impact size, and dividing the risk level; Through linear weighting method, each risk source is weighted and summarized to calculate the comprehensive risk level of the dam; According to the weighted comprehensive calculation, the risk levels of risk factors, risk elements and evaluation objects are divided respectively, and corresponding countermeasures and action plans are developed for each level.

7. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, In step 4, the combination of the Bayesian network method for dam failure risk analysis specifically includes: Build a Bayesian network model, define the relationship between each risk factor, form a causal chain of dam failure, determine the contribution degree of each factor to dam failure, and analyze the probability of dam failure; Use the Bayesian network method to analyze the sensitivity of the model and identify the key risk factors affecting dam failure; Through reverse reasoning analysis, the effective types and implementation priorities of the early prevention measures are determined to reduce the risk of dam collapse and optimize the emergency plan.

8. The high earth-rock dam multi-dimensional risk assessment method according to claim 7, characterized in that, The sensitivity analysis identifies the key risk factors affecting dam collapse by changing the probability table of the selected node SN and checking the changes in the target node TN probability table. The importance index I is calculated as follows: where P(TN) is the probability of the target node, is the conditional probability of the target node under the change of the selected node; the importance index represents the degree of influence of the node on the dam failure, reflecting the frequency of the negative influence and occurrence of the node under the state of not meeting the requirements.

9. The high earth-rock dam multi-dimensional risk assessment method according to claim 1, characterized in that, The hierarchical prevention and control measures are developed based on the risk level, specifically including: Installing automatic monitoring equipment at key positions of the dam to obtain real-time dam operation data; Periodically inputting real-time monitoring data into the risk assessment model to update the risk assessment results and identify the risk change trend; Adjusting the risk control measures in a timely manner based on the monitoring results, and immediately starting the emergency plan and taking additional prevention and control measures if the risk level increases; Preventive measures include daily monitoring, reinforcement engineering, and emergency plans.

10. A system based on the multi-dimensional risk assessment method of high earth-rock dams according to any one of claims 1-9, characterized in that, Specifically including: Data collection and processing module: including dam structure data collection, geological condition investigation, hydrological and meteorological data collection, and social and environmental data collection; Risk identification and classification module: including historical data analysis, real-time monitoring data analysis, and risk source classification; Risk assessment index system module: including index selection and setting, evaluation model construction, and weight calculation; Risk occurrence probability analysis module: including natural risk probability calculation, structural risk analysis, and human risk assessment; Comprehensive risk assessment module: including risk matrix construction, weighted comprehensive calculation, and risk level division; Dam collapse risk analysis module based on Bayesian network: including Bayesian network modeling, sensitivity analysis, and reverse reasoning and emergency optimization; Dynamic risk monitoring and feedback module: including real-time monitoring system construction, data analysis and update, and feedback and response adjustment.

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