A method and system for assessing safety risks in construction of a hydraulic tunnel

Abstract

This invention provides a method for assessing safety risks in hydraulic tunnel construction, relating to the field of engineering safety risk management technology. The method includes: first, constructing a hierarchical evaluation index system for tunnel construction safety risks; second, using an improved G1 method to determine the subjective weights of risk indicators, and introducing the COWA operator to sort the evaluation data in descending order and assign weights based on their positions, constructing an improved G1-COWA combined weighting model to obtain the weights of each indicator; third, based on the index system, introducing the WASPAS multi-attribute decision method to evaluate the tunnel construction event to be assessed, obtaining the evaluation values ​​of each indicator; finally, combining the indicator weights and evaluation values ​​to determine the tunnel construction safety assessment result. This invention, through diversified weight determination methods, achieves a deep integration of subjective experience and objective data, effectively eliminating the interference of expert cognitive uncertainty and evaluation data fluctuations on the results, and significantly improving the scientific rigor and accuracy of tunnel construction safety risk assessment.

Description

Technical Field

[0001] This invention relates to the field of safety risk assessment for hydraulic tunnel construction, specifically a method and system for safety risk assessment of hydraulic tunnel construction. Background Technology

[0002] As a critical national infrastructure project, water conservancy projects often involve large-scale tunnels and underground engineering during construction. This is especially true in inter-basin water diversion projects, where tunnel construction is characterized by long mileage, limited excavation cross-sections, complex mechanized operation environments, and significant challenges in ensuring ventilation and power supply. Because the construction environment is constantly evolving, numerous interfering factors exist, and accidents can have severe cascading effects. Collapses, water inrushes, or rock bursts can directly lead to casualties, significant property damage, and project delays. As my country's water conservancy construction extends to extreme geological areas such as high ground stress, high osmotic pressure, strong karst formations, and deep-buried long tunnels, the uncertainty and risk coupling of the construction environment have increased dramatically. However, existing risk assessment models still have limitations in handling the ambiguity of expert perception, the nonlinear relationships of multi-criteria decision-making, and the scientific allocation of assessment index weights, making it difficult to meet the stringent requirements of accuracy and robustness for modern complex hydraulic tunnels. Therefore, developing a method and system for assessing the safety risks of hydraulic tunnel construction that can effectively integrate the uncertainty of expert experience and possess high-precision multi-criteria decision-making capabilities has become a critical technical problem urgently needing to be solved in the field of water conservancy project safety management.

[0003] By reading the literature and summarizing the findings, the following problems can be identified with existing methods: Key issue ①: Existing assessments often focus only on a few isolated safety factors, lacking comprehensive coverage of risk elements and characterization of their internal relationships, especially neglecting the interaction of multi-dimensional factors such as geology, construction, and management, leading to one-sided assessment results; Key issue ②: The existing risk indicator system for hydraulic tunnels lacks a systematic design and is insufficient to encompass all key risk factors; Key issue ③: The commonly used multi-attribute decision-making methods, such as TOPSIS and fuzzy comprehensive evaluation, have shortcomings in handling uncertain information and improving decision-making accuracy. For example, the TOPSIS method is sensitive to data distribution, and the fuzzy comprehensive evaluation relies on membership functions, both of which are difficult to effectively handle risk decisions in high uncertainty environments. Therefore, collaboratively addressing the systematic deficiencies in the indicator system, the distortion of weight allocation, and the insufficient robustness of the decision-making model, and constructing an assessment framework that integrates multi-dimensional systematic indicators, a subjective and objective weighting mechanism, and high-precision uncertainty handling capabilities, is key to improving the scientific nature of safety risk assessment in hydraulic tunnel construction. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for safety risk assessment in hydraulic tunnel construction, aiming to solve the problems of insufficient systematicity of the indicator system, distorted weight allocation, and poor robustness of the decision-making model in the aforementioned background technology, thereby improving the scientificity and robustness of risk assessment. To achieve the above objective, the present invention adopts the following technical solution: A method for assessing the safety risks of hydraulic tunnel construction includes the following steps: Step S1: Construct a safety risk indicator system for hydraulic tunnel construction, which includes primary indicators and secondary indicators; Step S2: Determine the weights of each level of indicators in the indicator system using the improved G1-COWA combined weighting method; Step S3: Construct an initial decision matrix using the expert evaluation method, then further aggregate and normalize it to obtain the final expert decision matrix; Step S4: Based on the aforementioned indicator system, using the above indicator weights and decision matrix, the WASPAS method is employed to comprehensively calculate, quantitatively compare, and prioritize the identified and weighted risk factors, thereby achieving an assessment of the safety risks of the construction case.

[0005] In step S1, the indicator system construction step includes: based on the water conservancy engineering industry standards and combined with the construction environment of hydraulic tunnels, introducing the revised Human Factor Analysis and Classification System (HFACS) framework; establishing a multi-level risk assessment indicator system through correlation analysis of construction operation risk factors; wherein, the first-level indicators of the indicator system include the influence of enterprise organization, safety supervision, on-site operation related factors and construction personnel related factors, and each first-level indicator has several second-level indicators, thus forming an indicator system for safety risk assessment of hydraulic tunnel construction; The primary indicator system includes factors related to corporate organization, safety supervision, on-site operations, and construction personnel. The specific secondary evaluation indicators are as follows: Enterprise organizational impact includes organizational structure and responsibilities, safety production investment, and safety management procedures; safety supervision includes risk monitoring and early warning, supervision and management violations, and work plan arrangements; on-site operation related factors include technical measures, materials and machinery, geological conditions, work environment, and weather condition changes; and construction personnel related factors include violations of regulations, skill errors, intuition and decision-making errors, and personnel quality.

[0006] In step S2, the process of determining the combined weights includes the following: An improved G1 method is used to determine the subjective weight set: experts are invited to sort the indicators at each level and provide judgment information on the relative importance between adjacent indicators. Then, based on the scaling table of triangular fuzzy numbers, the judgment information on the relative importance of adjacent indicators is converted into triangular fuzzy numbers. After defuzzification, the determined relative importance coefficients are obtained, and then the subjective weight set is calculated. The objective weight set is determined using the COWA operator: the expert scores of the indicators are sorted in descending order to obtain a sequence set, and the objective weight set is determined by calculating the weight vector; The subjective weight set and the objective weight set are aggregated using a linear weighting method to obtain the final combined weight.

[0007] The scaling mapping table based on triangular fuzzy numbers transforms the relative importance judgment information of adjacent indicators into triangular fuzzy numbers, specifically: A scaling mapping table based on triangular fuzzy numbers is pre-constructed, defining linguistic variables and triangular fuzzy numbers r. k =(r kL r kM r kU The correspondence between () and () is discussed; for adjacent evaluation indicators x in the indicator system. k With x k-1 The relative importance linguistic judgment information provided by experts is obtained, and it is converted into the corresponding triangular fuzzy number scale r according to the scale mapping table. k ; where r kL r kM r kU These represent the lower limit, most likely value, and upper limit of the importance ratio of adjacent indicators, respectively, used to characterize the uncertainty range of expert judgment.

[0008] The calculation process of the subjective weight set includes: Experts were invited to rank the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction, and to provide fuzzy linguistic information on the relative importance of adjacent indicators. Then, the linguistic information was mapped to the corresponding triangular fuzzy numbers, and defuzzification was used to convert the fuzzy scores given by the experts into definite relative importance coefficients. Then, the subjective weights of the indicators are calculated according to the formula to form a set of subjective weights; The specific formula for calculating the subjective weight is as follows: ; The weighting formulas for other indicators in the same series as this indicator are as follows: ; In the formula, For the first j The relative importance coefficients of each indicator For the number of indicators, This is the initial set of indicator weights.

[0009] The calculation of the weights of the objective indicators includes: Experts were invited to score the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction. The scores of several experts for each indicator were sorted in descending order to obtain a ranking set. Then, the weighted vector of each indicator was calculated according to the formula to obtain the absolute weight and relative weight. After normalization, the final objective indicator weight was obtained. The weighted vector for each indicator is calculated according to the formula. H The specific formula is as follows: ; In the formula, To draw from n-1 elements j The number of combinations of distinct elements Let n be the weighted vector, and n be the number of data points in the new weight set. The expression for calculating the absolute weight is: ; In the formula, For absolute weight, a j For the new weight set j One data point; The formula for calculating the relative weights (normalized) is as follows: ; In the formula, The normalized version i Each indicator has a weight.

[0010] The calculation of the combined weights is as follows: Based on linear weighting and a compromise selection of combination coefficients, the subjective and objective weights of the indicators are aggregated to obtain the final combined weights; The final combined weight calculation expression is: ; In the formula, W The set of subjective weights for the indicators, calculated using the improved G1 method. H This is the set of objective weights for the indicators obtained by the COWA operator.

[0011] The specific steps of step S3 are as follows: first, construct an initial decision matrix D; then, aggregate expert opinions; and finally, perform normalization processing to obtain the final decision matrix D. ; The steps for constructing the initial decision matrix D are as follows: Obtain the initial score data of m experts for n evaluation indicators, and construct the initial decision matrix corresponding to each expert. , where k = 1, 2, ..., m; The steps for aggregating expert opinions are as follows: Introduce expert weighting coefficients to satisfy... The initial decision matrices of each expert are aggregated using a weighted average operator to calculate the aggregated comprehensive index value. e i : ; in, This represents the score given by the Kth expert to the i-th indicator; The normalization process specifically involves: applying a normalization function to the comprehensive index value. e i After performing dimensionless processing, the final expert decision matrix D is obtained. Its element normalization calculation expression is: ; In the formula, The normalized version i Each indicator has a weight.

[0012] In step S4, the WASPAS method is used to comprehensively calculate, quantify, compare, and prioritize the identified and weighted risk factors. Specifically: Using decision matrix D With indicator weight set The weighted sum WSM model and the weighted product WPM model were used to calculate the overall relative importance of the alternative methods. Q i The optimal evaluation result is obtained by selecting or weighting the overall relative importance based on the calculated total relative importance, so as to ensure the accuracy of the evaluation and determine the ranking. The WSM model calculates the overall relative importance of the alternative methods as follows: ; In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WSM model. Indicates the first i The first alternative is in the... j The normalized evaluation value under each evaluation criterion w j Weighting for a single indicator; The WPM model calculates the overall relative importance of the candidate methods using the following expression: ; In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WPM model; The expression for calculating the final result of the WASPAS method is as follows: .

[0013] The present invention also provides a system based on the above-mentioned method for assessing the safety risks of hydraulic tunnel construction, comprising: The indicator system determination module summarizes construction accidents in hydraulic tunnels based on literature and relevant websites, extracts relevant risk factors, and determines the indicator system. The weight determination module uses an improved G1-COWA combined weighting method to determine the weight of each indicator in the indicator system. The improved G1-COWA combined weighting method uses the improved G1 method and the COWA operator to determine the indicator weights of each level of the indicator system respectively. Alternatively, the improved G1 method is used to determine the indicator weights of risk indicators first, and then the COWA operator is used for correction. The decision matrix construction module first constructs an initial decision matrix D, then introduces expert weight coefficients and a weighted average operator to aggregate expert opinions, and finally performs normalization to obtain the final decision matrix D. ; The evaluation module uses the WASPAS method to evaluate the tunnel construction safety accidents to be evaluated based on the aforementioned indicator system, obtains a decision matrix, and determines the safety evaluation result by combining the indicator weights and using a multi-attribute decision method.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention integrates multiple risk factors such as geological environment, construction technology, and organizational management to construct a systematic evaluation index system for the construction safety of hydraulic tunnels, thus solving the problems of incomplete coverage and lack of correlation of risk factors; 2. This invention constructs an improved G1-COWA combined weighting model by fuzzifying the traditional G1 method. Addressing the imprecise nature of expert evaluation in complex decision-making environments, this model uses triangular fuzzy numbers instead of traditional precise numerical values ​​to quantify the importance ratio of adjacent indicators, achieving a leap from "point estimation" to "interval fuzzy representation" of expert subjective experience. This improvement retains the advantages of the G1 method—high computational efficiency and no need for consistency checks—while overcoming the information gaps caused by single numerical expressions through in-depth analysis of the uncertainty of expert subjective judgments. This ensures the accuracy of risk assessment indicator weights and provides more precise and reliable weight support for the safety evaluation of hydraulic tunnel construction.

[0015] 3. This invention is the first to combine the combined weighting method with the WASPAS method for risk assessment of hydraulic tunnels. It employs the G1-COWA combined weighting method, utilizing triangular fuzzy numbers to represent the fuzziness of expert perception, and combining this with the COWA operator to effectively mitigate the influence of extreme outliers in expert evaluations, achieving a deep fusion of subjective and objective information. Finally, it introduces the WASPAS decision model to replace the traditional TOPSIS or fuzzy comprehensive evaluation method. By integrating the weighted sum model and the weighted product model, it significantly improves the robustness and decision accuracy of the assessment results under high uncertainty environments. This invention effectively improves the expression of uncertainty in expert evaluations, enhancing the scientific rigor, accuracy, and reliability of hydraulic tunnel construction safety risk assessment.

[0016] 4. This invention summarizes and concludes relevant literature on hydraulic tunnel construction accidents, further classifies accident types, determines accident causes, systematically extracts relevant risk factors, and constructs a multi-level indicator system covering four dimensions: personnel, equipment and materials, environment, and management and technology. For each indicator in the indicator system, an improved G1-COWA combined weighting method is used to determine the weights: first, the improved G1 method is used to determine the subjective weights of the indicators; then, the COWA operator is used to calculate the objective weights; finally, linear weighting is used to obtain the combined weight values ​​of the indicators, integrating subjective experience with objective data, overcoming the subjective bias or objective data being detached from practice in single weighting methods, making the weights more scientific and robust. Finally, based on the indicator system, a comprehensive safety assessment method is used to evaluate the tunnel construction safety accidents to be assessed. By obtaining the scoring data of each indicator and combining it with the corresponding indicator weights, the safety assessment results of the cases to be evaluated are determined.

[0017] 5. This invention can search for hydraulic tunnel construction accident cases through literature platforms and relevant websites. By summarizing the collected accident cases, important evaluation indicators are extracted, and the indicator system is broken down into multiple layers. Finally, a detailed, reliable, and scientific indicator system is formed based on four dimensions, which improves the hydraulic tunnel construction safety risk assessment system and enhances safety management efficiency. Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Figure 1 A flowchart illustrating a method for assessing the safety risks of hydraulic tunnel construction, provided in an embodiment of the present invention; Figure 2 A schematic diagram of the evaluation index system provided in the embodiments of the present invention; Detailed Implementation To make the technical means, creative features, achieved objectives, and effects of this invention readily understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Please refer to... Figure 1 As shown in the figure, the method for assessing the safety risks of hydraulic tunnel construction according to an embodiment of the present invention includes the following steps: Step S1: Construct a safety risk indicator system for hydraulic tunnel construction, which includes primary indicators and secondary indicators; Step S2: Determine the weights of each level of indicators in the indicator system using the improved G1-COWA combined weighting method; Step S3: Construct an initial decision matrix using the expert evaluation method, then further aggregate and normalize it to obtain the final expert decision matrix; Step S4: Based on the aforementioned indicator system, using the above indicator weights and decision matrix, the WASPAS method is employed to comprehensively calculate, quantitatively compare, and prioritize the identified and weighted risk factors, thereby achieving an assessment of the safety risks of the construction case.

[0021] Specifically, in step S1, a safety risk indicator system for hydraulic tunnel construction is constructed. Based on the "Guidelines for Hazard Identification and Risk Assessment of Water Conservancy and Hydropower Projects" (SL / T 843—2025), and combined with the construction environment of hydraulic tunnels, the revised Human Factor Analysis and Classification System (HFACS) framework is introduced. Through correlation analysis of construction operation risk factors, a multi-level risk assessment indicator system is established. The first-level indicators of the indicator system include the impact of enterprise organization, safety supervision, on-site operation-related factors, and construction personnel-related factors. Each first-level indicator has several second-level indicators, thus forming an indicator system for assessing the safety risks of hydraulic tunnel construction.

[0022] The primary evaluation indicators include the influence of corporate organization (C1), safety supervision, on-site operation-related factors, and construction personnel-related factors.

[0023] The secondary evaluation indicators are specifically: Corporate organizational impact C1, including organizational structure and responsibilities C. 11 Safety production investment C 12 Safety Management Procedures C 13 Safety supervision C2 includes risk monitoring and early warning C 21 Supervision and management violations C 221. Work Plan Arrangement C 23 On-site operation-related factors C3 include technical measures C 31 Materials and Machinery C 32 Geological conditions C 33 Operating Environment C 34 Changes in weather conditions C 35 Construction personnel-related factors C4 include violations of operating procedures C 41 Skill error C 42 Intuition and Decision Errors (C) 43 Personnel quality C 44 .

[0024] Organizational Impact (A): Organizational impact refers to the construction unit's top-level design and resource support capabilities for safe production at the macro-management level. It is mainly reflected in the soundness of its organizational structure and responsibilities (A11), the strength of its safety production investment (A12), and the standardization of its safety management procedures (A13). These factors constitute the institutional cornerstone of safety management, and through the transmission of organizational effectiveness, determine the tone of the project's safe operation from the source.

[0025] Safety supervision (B): Safety supervision refers to the effectiveness of dynamically monitoring and intervening in the controlled state of risks during construction. Its core encompasses the sensitivity of the risk monitoring and early warning (B11) system, the strength of investigating and punishing violations of supervision and management regulations (B12), and the scientific and rational nature of work plans (B13). As a crucial link between management and operational levels, safety supervision is the core line of defense ensuring the effective issuance of safety instructions and the timely correction of on-site deviations.

[0026] On-site operational factors (C): On-site operational factors refer to the safety level of the physical form at the construction site and the constraints of the natural environment. This dimension not only includes the applicability of technical measures (C11) and the inherent safety of materials and machinery (C12), but also deeply integrates objective risks such as the unique geological conditions (C13), working environment (C14), and weather changes (C15) of hydraulic tunnels. These factors constitute the material basis and environmental carrier of construction safety, directly affecting the degree of inherent safety of the project.

[0027] Relevant factors of construction personnel (D): Relevant factors of construction personnel refer to the comprehensive characteristics of individuals directly involved in construction activities in terms of physiological, psychological, and behavioral performance. The main focus is on operational violations (D11) during the production process, technological errors caused by skill deficiencies (D12), and judgment biases influenced by intuition and decision-making errors (D13). Simultaneously, personnel quality (D14), as an intrinsic support, determines their ability to cope with sudden risks and is the most direct and active factor in causing or preventing construction safety accidents.

[0028] This embodiment constructs an indicator system through step S1. Its purpose is as follows: First, by following the latest industry guideline SL / T 843—2025, the compliance and authority of the assessment dimensions are ensured. Second, by introducing the revised HFACS framework, risk factors are expanded from a single physical environment to organizational management and human error levels, solving the problem of insufficient consideration of uncertain human factors in existing technologies. Finally, by establishing multi-level correlation indicators, a solid foundation is provided for the subsequent combined weighting model, thereby ensuring the applicability and accuracy of the entire risk assessment system in the complex environment of hydraulic tunnels.

[0029] In step S2, the improved G1-COWA combined weighting method determines the subjective weights of each indicator using the improved G1 method, and then determines the objective weights using the COWA operator. Specifically, the improved G1-COWA combined weighting method determines the subjective weights of each level of indicator using the improved G1 method, then calculates the objective weights using the COWA operator, and finally linearly weights the subjective and objective weights to obtain the combined weights. The specific process is as follows: Experts were invited to rank the importance of each indicator and score the relative importance of adjacent indicators. Then, the subjective weights of the indicators were calculated according to the formula to form a set of subjective weights. Experts were invited to score the importance of the indicators. The scores from several experts for each indicator were sorted in descending order to obtain a ranking set. Then, the weighted vector of each indicator was calculated according to the formula to obtain the absolute weight and relative weight. After normalization, the final objective indicator weights were obtained. Finally, the subjective and objective weights of the indicators are aggregated based on linear weighting to obtain the final combined weight.

[0030] The COWA operator is an improved combinational ordered weighting operator based on the ordered weighted average (OWA) operator. By optimizing the weighting vector based on the number of combinations, it can effectively reduce the impact of extremely high or low scores from expert evaluations on the overall assessment results. This operator exhibits good objectivity in the weight integration process. Compared to methods that rely on prior probability distributions, the COWA operator calculates directly based on the order of the data sequence, thus possessing wider applicability. However, its results are still influenced to some extent by the selected weight vector.

[0031] The G1 method is a subjective weighting method. Its operation requires experts to first establish the order relationship between indicators, then compare and judge the importance of adjacent indicators, and finally calculate the weights based on the order relationship. This method provides a relatively flexible way to determine weights when it is difficult to precisely quantify importance, but its results mainly depend on the expert's subjective judgment. This paper, based on the original G1 method, uses triangular fuzzy numbers to represent the relative importance coefficient r instead of precise values.k The value of is used to represent the cognitive uncertainty of experts caused by information asymmetry, thereby improving the scientific nature of weight allocation.

[0032] By combining the COWA operator with the improved G1 method, an improved G1-COWA combined weighting method can be constructed. This combined method uses the improved G1 method and the COWA operator to calculate the subjective and objective weights of the indicators respectively, and obtains the combined weight value by linearly weighting the two weight values. This ensures that the weights not only reflect expert experience but also effectively correlate with actual data, achieving a balance between subjective experience and objective data, reducing the impact of uncertain information, and ultimately obtaining more scientific and robust indicator weights. There are various ways to combine the COWA operator and the G1 method, and different combination modes are suitable for different application scenarios. For example, the G1 method can be used to weight secondary indicators, and then the COWA-G1 combined weighting method can be used to weight primary indicators; or, as shown in this embodiment, the subjective and objective weights of the indicators can be calculated separately using the G1 method and the COWA operator, and then the combined weights can be obtained by linearly weighting the subjective and objective weights.

[0033] In one optional implementation, the improved G1-COWA combined weighting method determines the subjective weights of each level of indicators using the improved G1 method, then calculates the objective weights using the COWA operator, and finally obtains the combined weights by linearly weighting the subjective and objective weights, including: The subjective weights of the indicators are determined based on the improved G1 method, specifically as follows: Experts were invited to rank the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction, and to score the relative importance of adjacent indicators. Then, the subjective weights of the indicators were calculated using a formula, forming a set of subjective weights; specifically: Using the scaling table based on triangular fuzzy numbers shown in Table 1, define linguistic variables and triangular fuzzy numbers. The correspondence between them; for adjacent evaluation indicators x in the indicator system k With x k-1 The system obtains the relative importance language judgment information provided by experts and converts it into the corresponding triangular fuzzy number scale according to the scale mapping table. ;in, r jL 、r jM 、r jU These represent the lower limit, most likely value, and upper limit of the importance ratio of adjacent indicators, respectively, used to characterize the uncertainty range of expert judgment.

[0034] Table 1 Scale Mapping Table

[0035] The calculation of the subjective weight of the indicator according to the formula is as follows: Based on the fuzzy relative importance coefficient of the indicators The fuzzy subjective weights of the indicators are calculated. The calculation formula is as follows: ; The weighting formulas for other indicators in the same series as this indicator are as follows: ; In the formula, For the first j The relative importance coefficients of each indicator For the number of indicators, The subjective weighting of the indicator is fuzzy.

[0036] The final subjective weights of the indicators are obtained by defuzzifying the subjective weights of the indicators. : ; In the formula, , , Indicators j The lower limit, median, and upper limit of fuzzy subjective weights. For the first j The subjective weights of each indicator after defuzzification.

[0037] The objective weights of the indicators are determined based on the COWA operator, specifically as follows: Constructing an evaluation dataset: Experts were invited to score the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction. The scores of several experts for each indicator were sorted in descending order to obtain a sorted set. Then, the weighted vector of each indicator was calculated according to the formula, thereby obtaining the absolute weight and relative weight. After normalization, the final objective indicator weights were obtained.

[0038] The scoring criteria are as follows: scores are in increments of 0.5, ranging from 1 to 5 points. The higher the score, the more important the corresponding risk factor indicator is.

[0039] The ranking set is obtained by sorting the importance scores of several experts for each indicator in descending order, as follows: By inviting n relevant experts to form an expert review panel, the importance of each level of risk assessment indicators within the indicator system is scored. Let's assume that risk indicator C... i Scoring yields a set of original data (c1, c2, ..., c...). n Then, sort these datasets in descending order and number them to obtain a sequence a0≥ a1≥ ≥ an 1, i.e., a Cj =(a1, a2, ..., a n That is, the evaluation index C. j The expert-assigned data are arranged in descending order to form a vector a. Cj ; Calculate the weighted vector for each indicator according to the formula. H The specific formula is as follows: ; In the formula, Let j be the number of combinations of selecting j distinct elements from n-1 elements. Let n be the weighted vector, and n be the number of data points in the new weight set. The expression for calculating the absolute weight is: ; In the formula, For absolute weight, a Cj For the new weight set j One data point; The formula for calculating the relative weights (normalized) is as follows: ; In the formula, The normalized version i Each indicator has a weight.

[0040] Finally, the subjective and objective weights of the indicators are aggregated based on linear weighting to obtain the final combined weight.

[0041] The weighting of the indicator combination is calculated as follows: Based on linear weighting and a compromise selection of combination coefficients, the subjective and objective weights of the indicators are aggregated to obtain the final combined weight.

[0042] The final combined weight calculation expression is: ; In the formula, W This is the set of subjective weights of the indicators calculated using the improved G1 method. H This is the set of objective weights for the indicators obtained by the COWA operator.

[0043] In step S3, firstly, an initial decision matrix D is constructed, then expert opinions are aggregated, and finally, normalization is performed to obtain the final decision matrix D. ; The steps for constructing the initial decision matrix D are as follows: Obtain the initial score data of m experts for n evaluation indicators, and construct the initial decision matrix corresponding to each expert. , where k = 1, 2, ..., m; The steps for aggregating expert opinions are as follows: Introduce expert weighting coefficients to satisfy... The initial decision matrices of each expert are aggregated using a weighted average operator to calculate the aggregated comprehensive index value. e i : ; in, This represents the score given by the Kth expert to the i-th indicator.

[0044] The normalization process specifically involves: applying a normalization function to the comprehensive index value. e i After performing dimensionless processing, the final expert decision matrix D is obtained. Its element normalization calculation expression is: ; In the formula, The normalized version i Each indicator has a weight.

[0045] In step S4, based on the indicator scoring data and the corresponding indicator weights, the WASPAS method is used to calculate the weighted sum-product model to comprehensively calculate, quantitatively compare and prioritize the different risk factors that have been identified and assigned weights. The WASPAS method consists of two parts: a weighted summation WSM model and a weighted product WPM model. The overall relative importance of the candidate methods is calculated based on both the WSM and WPM models. Q i Based on the actual situation, select an appropriate model, calculate the best evaluation result, that is, find the minimum dispersion and ensure the maximum accuracy of the estimate.

[0046] Furthermore, the expression for the overall relative importance of the alternative methods, calculated according to the WSM model, is as follows: ; In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WSM model. Indicates the first i The first alternative is in the... j Normalized evaluation values ​​under each evaluation criterion w j Weighting is based on a single indicator.

[0047] The expression for the overall relative importance of the alternative methods, calculated according to the WPM model, is as follows: ; In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WPM model.

[0048] Calculating the overall utility value: The final calculation expression for the WSAPAS method is as follows: ; In one alternative implementation, such as Figure 2 As shown, based on the "Guidelines for Hazard Identification and Risk Assessment of Water Conservancy and Hydropower Projects" (SL / T 843—2025) and combined with the construction environment of hydraulic tunnels, the revised Human Factor Analysis and Classification System (HFACS) framework is introduced. Through the correlation analysis of construction operation risk factors, a multi-level risk assessment index system is established. Among them, the first-level indicators of the index system include the influence of enterprise organization, safety supervision, on-site operation-related factors, and construction personnel-related factors. Each first-level indicator has several second-level indicators, thus forming an index system for safety risk assessment of hydraulic tunnel construction.

[0049] In related technologies, existing indicator system construction methods generally lack objectivity and accident case evidence. Most of them use relatively simple weight calculation methods, such as the analytic hierarchy process and the entropy weight method. They do not have a deep understanding of risk assessment. Risk assessment is a systematic process that identifies potential dangers and threats, analyzes their probability of occurrence and their possible consequences, thereby understanding and evaluating the magnitude of the risk and deciding whether to take corresponding risk control measures.

[0050] Example 1 A reservoir water conservancy project is located in Hunan Province. The dam site is situated on steep mountains and a narrow river valley. The construction employed a method of one-time riverbed damming, concrete cofferdam construction, and tunnel diversion. The diversion tunnel, located on the left bank, is 527.21 meters long. The surrounding rock is Class IV, primarily composed of medium-thick layers of quartz sandstone interbedded with shale. The quartz sandstone is severely weathered, with highly developed joints and fissures, making the rock extremely fragmented. Significant interlayer displacement and severe dissolution are also observed. In particular, the shale in the No. 3 gully softens upon exposure to water and easily weathers and disintegrates upon dehydration, generally scabbing away upon exposure to air. Construction of the diversion tunnel began in June 1994, and the risk of collapse persisted throughout construction. On October 10th at 2:20 PM, a large-scale roof collapse suddenly occurred, breaking through to the surface, with a collapse volume of approximately 1500 cubic meters. 3 All four workers involved in the construction perished. Based on this example of the diversion tunnel collapse, a safety risk assessment will be conducted for the diversion tunnel construction of this project.

[0051] First, a hierarchical evaluation index system for tunnel construction safety risks was constructed. Based on the "Guidelines for Hazard Identification and Risk Assessment of Water Conservancy and Hydropower Projects" (SL / T 843—2025) and considering the construction environment of hydraulic tunnels, the revised Human Factor Analysis and Classification System (HFACS) framework was introduced. Through correlation analysis of construction operation risk factors, a multi-level risk assessment index system was established. The primary indicators of this index system include the impact of enterprise organization, safety supervision, on-site operation-related factors, and construction personnel-related factors. Each primary indicator has several secondary indicators, thus forming an index system for assessing the safety risks of hydraulic tunnel construction.

[0052] The primary indicator system includes the influence of enterprise organization (C1), safety supervision (C2), on-site operation-related factors (C3), and construction personnel-related factors (C4).

[0053] The specific secondary evaluation indicators are as follows: Enterprise organizational impact C1 includes organizational structure and responsibilities C11, safety production investment C12, and safety management procedures C13; Safety supervision C2 includes risk monitoring and early warning C21, supervision and management violations C22, and work plan arrangement C23; On-site operation related factors C3 include technical measures C31, materials and machinery C32, geological conditions C33, working environment C34, and weather condition changes C35; Construction personnel related factors C4 include violations of regulations C41, skill errors C42, intuition and decision-making errors C43, and personnel quality C44.

[0054] Then, data collection and organization were carried out. First, experts scored the importance of the indicators: five industry experts (E1~E5) were invited, and they ranked and scored the indicators based on their analysis of the actual engineering situation and their professional knowledge. The scoring standard was: with a 0.5 increment, the range was limited to 1 to 5 points; the higher the score, the more important the corresponding risk factor indicator.

[0055] Table 2 Expert Scoring Table for Indicator Importance

[0056] The consensus among experts regarding the importance of the primary indicators is: C3 > C2 > C4 > C1 (on-site operational factors > safety supervision > construction personnel factors > enterprise organizational impact). The consensus among experts regarding the importance of the secondary indicators is: Group C1: C11 > C13 > C12; Group C2: C21 > C23 > C22; Group C3: C33 > C31 > C34 > C32 > C35; Group C4: C41 > C44 > C42 > C43.

[0057] Experts scored the relative importance of the indicators: using the scaling table based on triangular fuzzy numbers shown in Table 1 above, the linguistic variables and triangular fuzzy numbers were defined. The correspondence between them; for adjacent evaluation indicators x in the indicator system k With x k-1 The system obtains the relative importance language judgment information provided by experts and converts it into the corresponding triangular fuzzy number scale according to the scale mapping table. ; where r jL r jM r jU These represent the lower limit, most likely value, and upper limit of the importance ratio of adjacent indicators, respectively, used to characterize the uncertainty range of expert judgment.

[0058] Table 3. Fuzzy Evaluation Table of Relative Importance of Primary Indicators

[0059] Table 4. Fuzzy Evaluation Table of Relative Importance of Secondary Indicators

[0060] Table 5. Relative Importance Coefficient Assignment

[0061] After data collection, subjective weights were first determined based on the improved G1 method. Taking the primary risk assessment indicators in the constructed risk assessment indicator system as an example, experts ranked the primary risk assessment indicators according to their importance and quantified the relative importance between adjacent indicators. The specific order relationships and data are detailed in Tables 3-5. The specific calculation process is as follows: 1) First, taking the data provided by the expert group in Tables 3-5 as an example, we can determine the order of the primary indicators as C3>C2>C4>C1, indicating the importance of each indicator. r j The values ​​can be represented by fuzzy numbers as follows: r2 = (3, 5, 7); r3 = (1, 3, 5); r4 = (2, 4, 6); Then we can obtain: r2 r3 r4=(6, 60, 210), r3 r4 = (2, 12, 30).

[0062] 2) Then, based on the fuzzy relative importance coefficient of the indicator... The fuzzy subjective weights of the indicators are calculated. The calculation formula is as follows: ; The weighting formulas for other indicators in the same series as this indicator are as follows: ; In the formula, For the first j The relative importance coefficients of each indicator For the number of indicators, The subjective weighting of the indicator is fuzzy.

[0063] Substituting, we get: , , , .

[0064] 3) Finally, the subjective weights of the indicators are obtained by using the defuzzification formula for the subjective weights of the indicators. : ; In the formula, , , Indicators j The lower limit, median, and upper limit of fuzzy subjective weights. For the first j The subjective weights of each indicator after defuzzification.

[0065] Substituting the data, we get: , , , ; Normalization yields the final indicator weights: , , , ; Finally, following the above calculation process based on the improved G1 method weights, the remaining risk indicator data in the table were calculated synchronously. The detailed results of the indicator weight calculation are shown in Table 6.

[0066] Table 6 Summary of Safety Risk Indicators for Hydraulic Tunnels Based on the Improved G1 Method

[0067] Next, the objective weights are calculated using the COWA operator, as follows: Five industry experts were invited to score the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction. The scoring standard was as follows: the score was in increments of 0.5, ranging from 1 to 5 points. The higher the score, the more important the corresponding risk factor indicator. The scoring results are shown in Table 2.

[0068] Then, the importance scores given by several experts for each indicator are sorted in descending order to obtain the sorted set; Table 7. Sorted set of scores for each indicator in descending order.

[0069] The following section uses a primary indicator as an example to calculate the objective weight of the indicator.

[0070] The weighted vector of each indicator is calculated according to the formula, thereby obtaining the absolute weight and relative weight. After normalization, the final objective indicator weight is obtained.

[0071] The weighted vector for each indicator is calculated according to the formula. H The specific formula is as follows: ; In the formula, To draw from n-1 elements j The number of combinations of distinct elements Let n be the weighted vector, and n be the number of data sources in the new weight set. Since there are 5 experts, n=5, resulting in a weighted vector. H = (0.0625, 0.2500, 0.3750, 0.2500, 0.0625).

[0072] Then, the absolute weight of the index is further calculated according to the following formula, the expression for the calculation of the absolute weight is: ; In the formula, For absolute weight, a j For a certain indicator to be weighted in the new weighting j One data point; By substituting the data into Table 7, we can obtain the absolute weights of the primary indicators C1, C2, C3, and C4.

[0073] Normalizing the absolute weights above yields the relative weights, which are the final objective weights. The calculation expression for these relative weights is as follows: ; In the formula, The normalized version i Each indicator has a weight.

[0074] The normalized weights of the primary indicators are calculated as follows: C1: 3.6250 / 17.0000=0.2132, C2: 4.5000 / 17.0000=0.2647, C3: 5.0000 / 17.0000=0.2941, C4: 3.8750 / 17.0000=0.2279.

[0075] Table 8 Final Objective Weights of Each Indicator

[0076] The weighting of the indicator combination is calculated as follows: Based on linear weighting and a compromise selection of combination coefficients, the subjective and objective weights of the indicators are aggregated to obtain the final combined weight.

[0077] The final combined weight calculation expression is:

[0078] In the formula, W This is the set of subjective weights of the indicators calculated using the improved G1 method. H This is the set of objective weights for the indicators obtained by the COWA operator.

[0079] Table 9 Weighting Table of Indicator Combinations

[0080] Finally, the WASPAS method is used to comprehensively calculate, quantify, compare, and prioritize the identified and weighted risk factors, specifically as follows: The steps for constructing the initial decision matrix D are as follows: Obtain initial score data from m experts for n evaluation indicators, and construct the initial decision matrix for each expert. , where k = 1, 2, ..., m; The steps for aggregating expert opinions are as follows: Introduce expert weighting coefficients to satisfy... The initial decision matrices of each expert are aggregated using a weighted average operator to calculate the aggregated comprehensive index value. e i :

[0081] in, This represents the score given by the Kth expert to the i-th indicator.

[0082] The normalization process specifically involves: applying a normalization function to the comprehensive index value. e i After performing dimensionless processing, the final expert decision matrix D is obtained. Its element normalization calculation expression is: ; In the formula, The normalized version i Each indicator has a weight.

[0083] Based on detailed information regarding the collapse of the reservoir's diversion tunnel, five senior industry experts (E1-E5) were invited to conduct retrospective scoring. Using authentic information such as the accident report, geological survey records, and construction logs, the experts independently scored the safety status of 15 secondary indicators related to the accident's risk. A scoring system of 1-5 points was used, with higher scores indicating a worse safety status and a higher probability of risk. After summarizing the expert scores, the average score for each indicator was calculated and normalized to obtain the comprehensive decision matrix D. .

[0084] Table 10 Summary of Expert Decisions

[0085] Finally, the WASPAS method is used to comprehensively calculate, quantify, compare, and prioritize the identified and weighted risk factors, specifically as follows: Using decision matrix D With indicator weight set Calculate the overall relative importance of the candidate methods using the weighted sum-WSM model and the weighted product-WPM model, respectively. Q i Based on the calculated total relative importance and variance analysis, the optimal evaluation result is selected or weighted to ensure the accuracy of the evaluation and determine the ranking.

[0086] The WSM model calculates the overall relative importance of the candidate methods as follows:

[0087] In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WSM model. Indicates the first i The first alternative is in the... j Normalized evaluation values ​​under each evaluation criterion w j Weighting is based on a single indicator.

[0088] The WPM model calculates the overall relative importance of the candidate methods using the following expression: ; In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WPM model.

[0089] The expression for calculating the final result of the WSAPAS method is as follows: ; In this case, take =0.5 can balance the advantages of the two models and reduce the systematic bias that a single model may bring.

[0090] Table 11 Calculation results of the WASPAS model

[0091] In this embodiment, the primary indicators mainly serve to construct the evaluation system framework and provide logical classification. The final WASPAS evaluation model is based on aggregation calculations performed on the underlying indicator data.

[0092] The ranking results clearly show that C35 (change in conditions) ranks first, as a key risk factor, which is consistent with the physical root cause of the accident; while risk factors C43 (intuitive decision-making error) and C42 (skill error) follow closely behind, revealing institutional deficiencies in the emergency response system and skills training. These risk factors need to be focused on management, reflecting the key role of management loopholes in the accident; factors such as C33 (geological conditions) and C34 (operating environment) are secondary risk factors, ranking lower, which is consistent with their actual situation as not being the primary cause of this accident.

[0093] The results of this invention are consistent with the actual situation on site, and the evaluation results are consistent with the facts, indicating that the method proposed in this invention is accurate.

[0094] In this embodiment, the invention's indicator system construction method solves the systematization problem of risk identification and risk analysis, the improved G1-COWA combined weighting method solves the problem of quantifying the severity of consequences in risk analysis, and the WASPAS method improves the ranking accuracy to obtain more accurate risk assessment results. These three together constitute a brand-new and superior risk assessment solution.

[0095] Example 2 This embodiment provides a system for constructing a safety risk assessment index system for hydraulic tunnel construction, as shown in Example 1, including: The indicator system determination module summarizes hydraulic tunnel construction accidents based on relevant literature, further classifies accident types and determines accident causes, thereby extracting relevant risk factors and determining the indicator system. The weighting module uses a combined weighting method to determine the weights of each indicator in the indicator system. The improved G1-COWA combined weighting method determines the weights of each indicator at each level of the indicator system using the COWA operator and the improved G1 method respectively. Alternatively, for risk indicators, the improved G1 method is first used to determine the weights, and then the COWA operator is used for correction. The evaluation module uses a comprehensive safety evaluation method based on the aforementioned indicator system to evaluate the tunnel construction safety accidents to be evaluated, obtains the scoring data of each indicator, and determines the safety evaluation result by combining the corresponding indicator weights.

[0096] In one optional implementation, the processing steps of the indicator system construction module include: This study reviews the regulations, standards, and norms governing safety accidents during the construction of hydraulic tunnels. Based on the entire process of accident cases, it determines the accident classification, accident level, and accident causes. By analyzing the texts, it identifies relevant risk factors. Combining literature review and expert consultation, it derives the final evaluation indicators. Following the principles of comprehensiveness and hierarchy, scientific rigor and feasibility, and a combination of qualitative and quantitative methods, it constructs an indicator system. Starting from four dimensions—personnel, equipment and materials, environment, and management and technology—as primary indicators, these are further refined into several secondary indicators, forming the framework of the indicator system.

[0097] In one optional implementation, the indicator weights are determined using an improved G1-COWA combined weighting method based on expert scoring data, including: Based on the improved G1-COWA combined weighting method, an indicator scoring standard is established to form an indicator set, and relative importance coefficients are used to determine the indicator scores. r k Calculate the weight set; The weighted vector and absolute weight are calculated based on the weight set, and the relative weight is obtained by normalization, which is the result of the index weight.

[0098] In one optional implementation, the assessment result of the construction safety risk of the hydraulic tunnel to be evaluated is determined based on the indicator scoring data and indicator weights, including: Based on the indicator scoring data and indicator weights, the WASPAS method is used to obtain the comprehensive utility value of the alternative solutions. Based on the calculated comprehensive utility value, the risk factors are ranked in descending order, and then a risk assessment is conducted on the construction safety of hydraulic tunnels.

[0099] In one optional implementation, based on indicator scoring data and indicator weights, the WASPAS method is used to calculate the comprehensive utility value of alternative solutions and further refine the ranking of risk factors, including: The WASPAS method is used to first establish an initial decision matrix and perform normalization. Based on the weights and decision matrix, the total relative importance of the alternative methods is calculated using a weighted product model. The overall utility value can then be calculated based on the total relative importance.

[0100] In one optional implementation, the results of the safety risk assessment for hydraulic tunnel construction are determined based on the ranking of risk factors, including: The precise comprehensive utility value is obtained by searching for the extreme value of the function, and then the comprehensive utility values ​​of each risk factor are sorted in descending order to assess the risk of hydraulic tunnel construction cases.

[0101] In summary, the implementation of this application has at least the following beneficial effects: This application utilizes literature platforms and online resources to collect and further study safety accidents during the construction of hydraulic tunnels. It combines the entire process of hydraulic tunnel construction safety to obtain accident classifications, accident levels, and direct and indirect causes of accidents, extracting relevant risk factors to construct an indicator system. Based on expert scoring data, an improved G1-COWA combined weighting method is used to determine indicator weights. An initial decision matrix is ​​constructed through expert evaluation. Based on the indicator scoring data and indicator weights, the safety assessment results of the hydraulic tunnel construction accident cases to be evaluated are determined. Using statistical analysis and mathematical models, various indicators and related influencing factors are uniformly integrated within the framework for systematic analysis and evaluation, providing a scientific basis and clear guidance for optimizing the effectiveness of hydraulic tunnel construction safety management.

[0102] This invention, from a systems theory perspective, constructs an indicator system through literature review and statistical analysis of accident cases. This overcomes the shortcomings of previous methods that relied on limited accident data and personal experience, ensuring the comprehensiveness of risk factor identification and the systematic nature of the assessment. It cleverly combines the advantages of the G1 method and the COWA operator, fully utilizing the knowledge and experience of domain experts while reducing the potential subjective arbitrariness and extreme value influence in expert judgment through mathematical operators. By using the multi-attribute decision-making method WASPAS to rank risk factors, it can comprehensively evaluate alternative solutions, further reducing subjective bias and improving decision-making accuracy and assessment precision. This invention provides a complete methodology for constructing an indicator system and an assessment framework, rather than fixed rules and regulations. In assessing different tunnel construction scenarios, indicators and weights can be fine-tuned according to actual characteristics such as geological environment, weather conditions, and construction techniques. This invention is easily promoted and applied in tunnel construction under different low-quality scenarios and weather conditions, providing a replicable and adaptable standardized tool for improving safety management across the entire industry.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the safety risks of hydraulic tunnel construction, characterized in that, Includes the following steps: Step S1: Construct a safety risk indicator system for hydraulic tunnel construction, which includes primary indicators and secondary indicators; Step S2: Determine the weights of each level of indicators in the indicator system using the improved G1-COWA combined weighting method; Step S3: Construct an initial decision matrix using the expert evaluation method, then further aggregate and normalize it to obtain the final expert decision matrix; Step S4: Based on the aforementioned indicator system, using the above indicator weights and decision matrix, the WASPAS method is employed to comprehensively calculate, quantitatively compare, and prioritize the identified and weighted risk factors, thereby achieving an assessment of the safety risks of the construction case.

2. The method for assessing the safety risks of hydraulic tunnel construction according to claim 1, characterized in that, In step S1, the indicator system construction step includes: based on the water conservancy engineering industry standards and combined with the construction environment of hydraulic tunnels, introducing the revised Human Factor Analysis and Classification System (HFACS) framework; establishing a multi-level risk assessment indicator system through correlation analysis of construction operation risk factors; wherein, the first-level indicators of the indicator system include the influence of enterprise organization, safety supervision, on-site operation related factors and construction personnel related factors, and each first-level indicator has several second-level indicators, thus forming an indicator system for safety risk assessment of hydraulic tunnel construction; The primary indicator system includes factors related to corporate organization, safety supervision, on-site operations, and construction personnel. The specific secondary evaluation indicators are as follows: Enterprise organizational impact includes organizational structure and responsibilities, safety production investment, and safety management procedures; safety supervision includes risk monitoring and early warning, supervision and management violations, and work plan arrangements; on-site operation related factors include technical measures, materials and machinery, geological conditions, work environment, and weather condition changes; and construction personnel related factors include violations of regulations, skill errors, intuition and decision-making errors, and personnel quality.

3. The method for assessing the safety risks of hydraulic tunnel construction according to claim 1, characterized in that: In step S2, the process of determining the combined weights includes the following: An improved G1 method is used to determine the subjective weight set: experts are invited to sort the indicators at each level and provide judgment information on the relative importance between adjacent indicators. Then, based on the scaling table of triangular fuzzy numbers, the judgment information on the relative importance of adjacent indicators is converted into triangular fuzzy numbers. After defuzzification, the determined relative importance coefficients are obtained, and then the subjective weight set is calculated. The objective weight set is determined using the COWA operator: the expert scores of the indicators are sorted in descending order to obtain a sequence set, and the objective weight set is determined by calculating the weight vector; The subjective weight set and the objective weight set are aggregated using a linear weighting method to obtain the final combined weight.

4. The method for assessing the safety risks of hydraulic tunnel construction according to claim 3, characterized in that: The scaling mapping table based on triangular fuzzy numbers transforms the relative importance judgment information of adjacent indicators into triangular fuzzy numbers, specifically: A scaling mapping table based on triangular fuzzy numbers is pre-constructed, defining linguistic variables and triangular fuzzy numbers r. k =(r kL r kM r kU The correspondence between () and () is discussed; for adjacent evaluation indicators x in the indicator system. k With x k-1 The relative importance linguistic judgment information provided by experts is obtained, and it is converted into the corresponding triangular fuzzy number scale r according to the scale mapping table. k ; where r kL r kM r kU These represent the lower limit, most likely value, and upper limit of the importance ratio of adjacent indicators, respectively, used to characterize the uncertainty range of expert judgment.

5. The method for assessing the safety risks of hydraulic tunnel construction according to claim 3, characterized in that: The calculation process of the subjective weight set includes: Experts were invited to rank the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction, and to provide fuzzy linguistic information on the relative importance of adjacent indicators. Then, the linguistic information was mapped to the corresponding triangular fuzzy numbers, and defuzzification was used to convert the fuzzy scores given by the experts into definite relative importance coefficients. Then, the subjective weights of the indicators are calculated according to the formula to form a set of subjective weights; The specific formula for calculating the subjective weight is as follows: ； The weighting formulas for other indicators in the same series as this indicator are as follows: ； In the formula, For the first j The relative importance coefficients of each indicator For the number of indicators, This is the initial set of indicator weights.

6. The method for assessing the safety risks of hydraulic tunnel construction according to claim 3, characterized in that: The calculation of the weights of the objective indicators includes: Experts were invited to score the importance of each level of indicators in the safety risk assessment index system for hydraulic tunnel construction. The scores of several experts for each indicator were sorted in descending order to obtain a ranking set. Then, the weighted vector of each indicator was calculated according to the formula to obtain the absolute weight and relative weight. After normalization, the final objective indicator weight was obtained. The weighted vector for each indicator is calculated according to the formula. H The specific formula is as follows: ； In the formula, To draw from n-1 elements j The number of combinations of distinct elements Let n be the weighted vector, and n be the number of data points in the new weight set. The expression for calculating the absolute weight is: ； In the formula, For absolute weight, a j For the new weight set j One data point; The formula for calculating the relative weights (normalized) is as follows: ； In the formula, The normalized version i Each indicator has a weight.

7. The method for assessing the safety risks of hydraulic tunnel construction according to claim 3, characterized in that: The calculation of the combined weights is as follows: Based on linear weighting and a compromise selection of combination coefficients, the subjective and objective weights of the indicators are aggregated to obtain the final combined weights; The final combined weight calculation expression is: ； In the formula, W The set of subjective weights for the indicators, calculated using the improved G1 method. H This is the set of objective weights for the indicators obtained by the COWA operator.

8. The method for assessing the safety risks of hydraulic tunnel construction according to claim 1, characterized in that: The specific steps of step S3 are as follows: first, construct an initial decision matrix D; then, aggregate expert opinions; and finally, perform normalization processing to obtain the final decision matrix D. ; The steps for constructing the initial decision matrix D are as follows: Obtain the initial score data of m experts for n evaluation indicators, and construct the initial decision matrix corresponding to each expert. , where k = 1, 2, ..., m; The steps for aggregating expert opinions are as follows: Introduce expert weighting coefficients to satisfy... The initial decision matrices of each expert are aggregated using a weighted average operator to calculate the aggregated comprehensive index value. e i : ； in, This represents the score given by the Kth expert to the i-th indicator; The normalization process specifically involves: applying a normalization function to the comprehensive index value. e i After performing dimensionless processing, the final expert decision matrix D is obtained. Its element normalization calculation expression is: ； In the formula, The normalized version i Each indicator has a weight.

9. The method for assessing the safety risks of hydraulic tunnel construction according to claim 1, characterized in that: In step S4, the WASPAS method is used to comprehensively calculate, quantify, compare, and prioritize the identified and weighted risk factors. Specifically: Using decision matrix D With indicator weight set The weighted sum WSM model and the weighted product WPM model were used to calculate the overall relative importance of the alternative methods. Q i The optimal evaluation result is obtained by selecting or weighting the overall relative importance based on the calculated total relative importance, so as to ensure the accuracy of the evaluation and determine the ranking. The WSM model calculates the overall relative importance of the alternative methods as follows: ； In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WSM model. Indicates the first i The first alternative is in the... j The normalized evaluation value under each evaluation criterion w j Weighting for a single indicator; The WPM model calculates the overall relative importance of the candidate methods using the following expression: ； In the formula, This indicates the overall relative importance of the alternative methods calculated based on the WPM model; The expression for calculating the final result of the WASPAS method is as follows: 。 10. A system based on the hydraulic tunnel construction safety risk assessment method according to any one of claims 1-9, characterized in that, include: The indicator system determination module summarizes construction accidents in hydraulic tunnels based on literature and relevant websites, extracts relevant risk factors, and determines the indicator system. The weight determination module uses an improved G1-COWA combined weighting method to determine the weight of each indicator in the indicator system. The improved G1-COWA combined weighting method uses the improved G1 method and COWA operator to determine the weights of each level of the indicator system; or it first uses the improved G1 method to determine the weights of the risk indicators, and then uses the COWA operator to correct them. The decision matrix construction module first constructs an initial decision matrix D, then introduces expert weight coefficients and a weighted average operator to aggregate expert opinions, and finally performs normalization to obtain the final decision matrix D. ; The evaluation module uses the WASPAS method to evaluate the tunnel construction safety accidents to be evaluated based on the aforementioned indicator system, obtains a decision matrix, and determines the safety evaluation result by combining the indicator weights and using a multi-attribute decision method.

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