ANP-EWM-Extenics-based underground engineering risk evaluation method for extreme rainfall

Abstract

The invention provides an underground engineering risk evaluation method under extreme rainfall. The method comprises the following steps: step 1, dividing underground engineering risk areas according to geological conditions and engineering structure characteristics; 2, establishing a rainfall difference-underground engineering mechanical stability analysis model; 3, determining a risk evaluation index in combination with historical accident data, historical monitoring and a mechanical analysis model; 4, based on the determined risk evaluation indexes, an ANP method and an EWM method are adopted for combined weighting; and step 5, determining a risk matter element matrix and calculating a regional risk grade, the evaluation method can comprehensively consider the influence relationship of each risk index, and characterize the function relationship between the risk index and the risk grade by using the correlation degree, thereby reflecting the objectivity of data while ensuring the subjectivity of experts, effectively improving the effectiveness and accuracy of risk evaluation, and improving the risk evaluation efficiency. The systematicness and adaptability of construction safety management are enhanced, and the disaster prevention capacity of underground engineering under extreme rainfall is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of underground engineering risk, and in particular relates to an underground engineering risk assessment method for extreme rainfall based on ANP-EWM-Extenics. Background Art

[0002] Underground projects are an indispensable component of modern urban infrastructure. They play a key role in rationally utilizing urban underground space, alleviating surface traffic congestion, optimizing land resources, and ensuring the stable operation of urban infrastructure. They have become a strategic measure for enhancing a city's comprehensive carrying capacity. Due to their enclosed spaces and interconnected systems, underground garages, underground plazas, and underground rail transit systems are susceptible to engineering disasters such as settlement instability, anti-floating failure, and infiltration damage during extreme rainfall. These engineering disasters not only cause significant economic losses and social impacts, but can even directly threaten public safety. Therefore, establishing an accurate risk assessment system for underground projects under extreme rainfall is not only a key support for developing scientific disaster prevention plans, but also a core component of building a resilient disaster prevention system.

[0003] Current research often uses single models to assess risk. While the Analytical Network Process (ANP) can characterize the networked relationships between indicators, it relies on expert experience to determine weights, which is highly subjective. The Entropy Weight Method (EWM), while objectively assigning weights based on data dispersion, ignores the interactions between factors. Extenics can address conflicting issues through matter-element models, but it lacks the ability to deduce the dynamic boundaries of risk transmission. Furthermore, underground engineering risks under extreme rainfall are characterized by high-dimensional nonlinearity and threshold mutations, making it difficult for a single method to accurately assess the entire chain of "weight assignment, state quantification, and conflict warning." Therefore, new technologies are urgently needed to accurately assess underground engineering disaster risks under extreme rainfall.

[0004] Against the above background and the difficulties faced by underground projects under extreme rainfall, the present invention can break through the limitations of traditional models of "static and single dimension", while reflecting the weight heterogeneity, state continuity and mutation warning of complex systems, and provide technical support for disaster management and emergency decision-making of underground projects under extreme rainfall. Summary of the Invention

[0005] To solve the problems existing in the prior art, the present invention provides an underground engineering risk assessment method that reflects the weight heterogeneity, state continuity and mutation warning of complex systems, providing technical support for disaster management and emergency decision-making of underground projects under extreme rainfall.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics, comprising the following steps: S1: Divide underground engineering risk areas according to geological conditions and engineering structural characteristics; S2: Establish a “rainfall difference-underground engineering mechanics stability analysis” model to analyze the structural stability under different rainfall intensities; S3: Combine historical accident data, historical monitoring data, and mechanical stability analysis models to construct a risk dataset for underground projects under extreme rainfall, and determine the main risk categories and risk assessment indicators for underground projects under extreme rainfall; S4: Based on the determined risk assessment indicators, the ANP method is used to characterize the dependency relationship between the indicators and calculate the subjective weight of each indicator. The EWM method is used to quantify the degree of dispersion of the indicator data and calculate the objective weight of each indicator. Finally, the weighted average method is used to combine the weights calculated by the ANP method and the EWM method to balance expert experience and data objectivity to obtain the combined weight of the risk assessment indicators. S5: Determine the risk element matrix, construct the correlation function, and calculate the risk level of each area and level through extension operations; Furthermore, in said S1: the project types include underground shopping malls, basements, underground garages, and subway stations, and the risk areas are divided according to the underground project types, geological structure characteristics, hydrological dynamic data, and the locations of protective measures.

[0007] Furthermore, the S2 includes the following steps: S21: Obtain geological data and rainfall data for the underground engineering area, establish a rainfall intensity-duration relationship curve, and focus on monitoring the rainfall difference △R; S22: The shear strength parameters at different moisture contents were determined through experiments. The unsaturated soil seepage-stress coupling model was established using FLAC3D. The seepage equation is shown below. (1); Where ψ is the matrix suction, k(ψ) is the permeability coefficient function, which characterizes the change of unsaturated soil permeability with ψ; z is the elevation head, which reflects the gravitational potential energy and is related to the terrain elevation. During rainfall infiltration, z drives water to move deep underground; C(ψ) is the specific water capacity, which characterizes the water storage capacity of the soil; ∇(ψ + z) is the total head gradient. The rainfall difference △R affects the magnitude and direction of the total head gradient by changing the distribution of ψ. S23: Establish a quantitative relationship between rainfall difference △R and safety factor FS, as shown in the following formula, and construct a rainfall difference-underground engineering mechanics stability analysis model; (2); Where, is the effective cohesion, which can be measured by triaxial testing; is the normal total stress, calculated from the formation pressure, , is the soil weight, H is the burial depth; is the pore gas pressure, which can be measured by a pressure sensor; is the pore water pressure, measured by a piezometer; is the rainfall influence coefficient, which characterizes the amplifying effect of rainfall on matrix suction and is obtained by inverting the field monitoring data; is the effective internal friction angle, measured by direct shear test; is the shear stress, which is calculated by the limit equilibrium method, namely ,in is the internal friction angle of soil, and c is the soil cohesion.

[0008] Preferably, in S3: Based on the rainfall difference-underground engineering stability analysis model established by S2, the historical accident records and historical monitoring data of underground engineering are integrated to construct a risk data set for underground engineering under rainfall. The influence curves of various risk factors on the stability of underground engineering are calculated. According to the change rate and peak size of each influence curve, the main risk categories and risk evaluation indicators of underground engineering under extreme rainfall are comprehensively determined.

[0009] Furthermore, in said S4: S41: Based on the risk assessment indicators determined in S3, the subjective weight value of each indicator is calculated using the ANP method. The specific method is as follows: (1) Constructing a judgment matrix: Use the 1-9 scaling method to compare each indicator pairwise to generate a judgment matrix; Table 1 1-9 scaling method: scale meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is extremely important than the latter 9 Indicates that compared with two elements, the former is more important than the latter 2,4,6,8 Indicates the middle value of the above adjacent judgment Countdown from 1 to 9 Indicates the importance of comparing the order of the corresponding two elements (2) Consistency test: Calculate the maximum eigenvalue of the judgment matrix and use the consistency ratio CR to determine whether the matrix meets the consistency requirements. When CR < 0.1, the matrix passes the consistency test. The consistency index is calculated as follows: (3); Where λ is the maximum eigenvalue of the consistency matrix, n is the order of the consistency matrix, if CI = 0, there is complete consistency; if CI is close to 0, it means there is satisfactory consistency; the larger the CI, the more serious the inconsistency; (3) Calculate the subjective weight W j ANP :According to the judgment matrix results, the weight matrix and super matrix are established in turn, the super matrix is ​​weighted to obtain the weighted super matrix, and the relative ranking vector of the weighted super matrix is ​​solved. The relative ranking vector obtained is the weight value of each risk factor under the ANP criterion; the calculation formulas of the unweighted super matrix, weight matrix and weighted super matrix required in the process are shown in formulas (4), (5) and (6) respectively: (4); (5); (6); S42. The specific method for calculating objective weights using the EWM method is as follows: (1) Standardized data: All evaluation indicators are dimensionlessly standardized, such as minimum-maximum standardization, to ensure that the dimensions of each indicator are consistent; (2) Calculate information entropy: Calculate the information entropy value of each indicator based on the standardized value of each indicator. Information entropy reflects the degree of dispersion of the indicator. The greater the dispersion, the smaller the information entropy. The information entropy formula is: (7); where p ij is the ratio of the standardized value of the i-th sample of the j-th indicator to the total, k' is a constant, k'=1 / ln(n), n is the total number of samples; The objective weight is thus obtained: (8); in, is the objective weight of the jth indicator; s is the total number of evaluation indicators; S43. The subjective weight calculated by the ANP method and the objective weight calculated by the entropy weight method are combined using the weighted average method. (9); Among them, α is the subjective weight ratio.

[0010] Furthermore, in S5, the specific steps are as follows: S51: Based on the risk assessment indicators determined in S3, an extension risk element matrix is ​​constructed, including a risk indicator matrix and a risk level matrix. The risk assessment matrix is ​​composed of the risk assessment indicators determined in S3 and is divided into a target layer, a criterion layer, and an indicator layer. The risk level matrix divides risk levels according to relevant regulations and standards and determines the intervals for each risk level. S52: For qualitative risk assessment indicators, industry experts are invited to assign scores. For quantitative risk assessment indicators, real-time monitoring data of underground projects and weather station forecast data are input and scored using the rainfall difference-underground project mechanical stability analysis model. Based on the grading standards and actual scores of each evaluation indicator, the evaluation indicators are quantified and normalized. S53: According to the determined risk indicator matrix and risk level matrix, respectively determine the classical domain, section domain and matter-element to be evaluated of each indicator; S54: Combined with the combined weights obtained in S4, the extension distance between each risk assessment indicator and the risk assessment level interval is calculated according to formulas (10) and (11), and formula (12) is selected as the correlation function between the risk assessment indicator and the risk assessment level interval; then, the correlation between each layer of risk element and each risk assessment level is calculated according to formula (13); then, the comprehensive correlation of the region is determined according to the maximum correlation criterion, and the risk level interval to which the comprehensive correlation belongs is the risk level of the region; finally, the risk assessment levels of other regions are calculated in sequence according to the above calculation steps; (10); (11); (12); (13); Where, is the actual measured value of the i-th indicator of the k-th risk element; Indicates the jth Under each risk assessment level, i The classical domain interval of the indicator, For the i-th The minimum value of the indicator, For the i-th The maximum value of an indicator; Indicates the The section interval of i indicators, For the i The global minimum value of an indicator. For the i The maximum value of an indicator in the global range; Represents risk assessment indicators and the classical domain interval The extension distance, Represents risk assessment indicators and section interval The extension distance; Risk Element The correlation degree with the j-th risk assessment level.

[0011] Compared with the prior art, the present invention has the following beneficial effects: This model transcends the limitations of traditional models' static, single-dimensional nature, simultaneously reflecting the weight heterogeneity, state continuity, and sudden change warning capabilities of complex systems. It characterizes the influence relationships between risk factors while embodying the objectivity of monitoring data, achieving a systematic integration of subjective and objective weights. The combination of mathematical evaluation methods, engineering monitoring data, and mechanical analysis models enables real-time dynamic assessment of underground project risks under extreme rainfall, providing technical support for disaster management and emergency decision-making for underground projects under extreme rainfall. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the overall analysis structure flow of the present invention; Figure 2 Schematic diagram of the structure of the underground engineering mechanics stability analysis model of the present invention; Figure 3 This is a schematic diagram of the ANP-EWM combined weight calculation process of the present invention; Figure 4 This is a schematic diagram of the structure of the underground engineering risk level extension matter-element assessment process of the present invention; Figure 5 This is an example diagram of the numerical representation of the random consistency index RI of the present invention. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inner", "outer", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are merely for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed or operate in a specific direction. Therefore, they should not be understood as limiting the present invention. Example

[0014] See attached Figure 1-5 As shown, a risk assessment method for underground engineering under extreme rainfall based on ANP-EWM-Extenics includes the following steps: S1: Divide underground engineering risk areas according to geological conditions and engineering structural characteristics; S2: Establish a “rainfall difference-underground engineering mechanics stability analysis” model to analyze the structural stability under different rainfall intensities; S3: Combine historical accident data, historical monitoring data, and mechanical stability analysis models to construct a risk dataset for underground projects under extreme rainfall, and determine the main risk categories and risk assessment indicators for underground projects under extreme rainfall; S4: Based on the determined risk assessment indicators, the ANP method is used to characterize the dependency relationship between the indicators and calculate the subjective weight of each indicator. The EWM method is used to quantify the degree of dispersion of the indicator data and calculate the objective weight of each indicator. Finally, the weighted average method is used to combine the weights calculated by the ANP method and the EWM method to balance expert experience and data objectivity to obtain the combined weight of the risk assessment indicators. S5: Determine the risk element matrix, construct the correlation function, and calculate the risk level of each area and level through extension operations; Furthermore, in said S1: the project types include underground shopping malls, basements, underground garages, and subway stations, and the risk areas are divided according to the underground project types, geological structure characteristics, hydrological dynamic data, and the locations of protective measures.

[0015] Furthermore, the S2 includes the following steps: S21: Obtain geological data and rainfall data for the underground engineering area, establish a rainfall intensity-duration relationship curve, and focus on monitoring the rainfall difference △R; S22: The shear strength parameters at different moisture contents were determined through experiments. The unsaturated soil seepage-stress coupling model was established using FLAC3D. The seepage equation is shown below. (1); Where ψ is the matrix suction, k(ψ) is the permeability coefficient function, which characterizes the change of unsaturated soil permeability with ψ; z is the elevation head, which reflects the gravitational potential energy and is related to the terrain elevation. During rainfall infiltration, z drives water to move deep underground; C(ψ) is the specific water capacity, which characterizes the water storage capacity of the soil; ∇(ψ + z) is the total head gradient. The rainfall difference △R affects the magnitude and direction of the total head gradient by changing the distribution of ψ. S23: Establish a quantitative relationship between rainfall difference △R and safety factor FS, as shown in the following formula, and construct a rainfall difference-underground engineering mechanics stability analysis model; (2); Where, is the effective cohesion, which can be measured by triaxial testing; is the normal total stress, calculated from the formation pressure, , is the soil weight, H is the burial depth; is the pore gas pressure, which can be measured by a pressure sensor; is the pore water pressure, measured by a piezometer; is the rainfall influence coefficient, which characterizes the amplifying effect of rainfall on matrix suction and is obtained by inverting the field monitoring data; is the effective internal friction angle, measured by direct shear test; is the shear stress, which is calculated by the limit equilibrium method, namely ,in is the internal friction angle of soil, and c is the soil cohesion.

[0016] Preferably, in S3: Based on the rainfall difference-underground engineering stability analysis model established by S2, historical accident records of underground engineering and historical monitoring data are integrated to construct a risk data set for underground engineering under rainfall. The influence curves of various risk factors (such as rainfall intensity, soil density, soil pore pressure, etc.) on the stability of underground engineering are calculated. The main risk categories and risk evaluation indicators of underground engineering under extreme rainfall are comprehensively determined based on the change rate and peak size of each influence curve.

[0017] Furthermore, in said S4: S41: Based on the risk assessment indicators determined in S3, the subjective weight value of each indicator is calculated using the ANP method. The specific method is as follows: (1) Constructing a judgment matrix: Use the 1-9 scaling method to compare each indicator pairwise to generate a judgment matrix; Table 1 1-9 scaling method: scale meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is extremely important than the latter 9 Indicates that compared with two elements, the former is more important than the latter 2,4,6,8 Indicates the middle value of the above adjacent judgment Countdown from 1 to 9 Indicates the importance of comparing the order of the corresponding two elements (2) Consistency test: Calculate the maximum eigenvalue of the judgment matrix and use the consistency ratio CR to determine whether the matrix meets the consistency requirements. When CR < 0.1, the matrix passes the consistency test. The consistency index is calculated as follows: (3) Where λ is the maximum eigenvalue of the consistency matrix, n is the order of the consistency matrix, if CI = 0, there is complete consistency; if CI is close to 0, it means there is satisfactory consistency; the larger the CI, the more serious the inconsistency; (3) Calculate the subjective weight W j ANP :According to the judgment matrix results, the weight matrix and super matrix are established in turn, the super matrix is ​​weighted to obtain the weighted super matrix, and the relative ranking vector of the weighted super matrix is ​​solved. The relative ranking vector obtained is the weight value of each risk factor under the ANP criterion; the calculation formulas of the unweighted super matrix, weight matrix and weighted super matrix required in the process are shown in formulas (4), (5) and (6) respectively: (4); (5); (6); S42. The specific method for calculating objective weights using the EWM method is as follows: (1) Standardized data: All evaluation indicators are dimensionlessly standardized, such as minimum-maximum standardization, to ensure that the dimensions of each indicator are consistent; (2) Calculate information entropy: Calculate the information entropy value of each indicator based on the standardized value of each indicator. Information entropy reflects the degree of dispersion of the indicator. The greater the dispersion, the smaller the information entropy. The information entropy formula is: (7); where p ij is the ratio of the standardized value of the i-th sample of the j-th indicator to the total, k' is a constant, k'=1 / ln(n), n is the total number of samples; The objective weight is thus obtained: (8); in, is the objective weight of the jth indicator; s is the total number of evaluation indicators; S43. The subjective weight calculated by the ANP method and the objective weight calculated by the entropy weight method are combined using the weighted average method. (9); Among them, α is the subjective weight ratio.

[0018] Furthermore, in S5, the specific steps are as follows: S51: Based on the risk assessment indicators determined in S3, an extension risk element matrix is ​​constructed, including a risk indicator matrix and a risk level matrix. The risk assessment matrix is ​​composed of the risk assessment indicators determined in S3 and is divided into a target layer, a criterion layer, and an indicator layer. The risk level matrix divides risk levels according to relevant regulations and standards and determines the intervals for each risk level. S52: For qualitative risk assessment indicators, industry experts are invited to assign scores. For quantitative risk assessment indicators, real-time monitoring data of underground projects and weather station forecast data are input and scored using the rainfall difference-underground project mechanical stability analysis model. Based on the grading standards and actual scores of each evaluation indicator, the evaluation indicators are quantified and normalized. S53: According to the determined risk indicator matrix and risk level matrix, respectively determine the classical domain, section domain and matter-element to be evaluated of each indicator; S54: Combined with the combined weights obtained in S4, the extension distance between each risk assessment indicator and the risk assessment level interval is calculated according to formulas (10) and (11), and formula (12) is selected as the correlation function between the risk assessment indicator and the risk assessment level interval; then, the correlation between each layer of risk element and each risk assessment level is calculated according to formula (13); then, the comprehensive correlation of the region is determined according to the maximum correlation criterion, and the risk level interval to which the comprehensive correlation belongs is the risk level of the region; finally, the risk assessment levels of other regions are calculated in sequence according to the above calculation steps; (10); (11); (12); (13); Where, is the actual measured value of the i-th indicator of the k-th risk element; Indicates the jth Under each risk assessment level, i The classical domain interval of the indicator, For the i-th The minimum value of the indicator, For the i-th The maximum value of an indicator; Indicates the The section interval of i indicators, For the i The global minimum value of an indicator. For the i The maximum value of an indicator in the global range; Represents risk assessment indicators and the classical domain interval The extension distance, Represents risk assessment indicators and section interval The extension distance; Risk Element The correlation degree with the j-th risk assessment level.

[0019] Working Principle: This model transcends the limitations of traditional models' static, single-dimensional nature, simultaneously reflecting the weight heterogeneity, state continuity, and sudden change warning capabilities of complex systems. It characterizes the influence relationships between risk factors while embodying the objectivity of monitoring data, achieving a systematic integration of subjective and objective weights. The integration of mathematical evaluation methods, engineering monitoring data, and mechanical analysis models enables real-time dynamic assessment of underground project risks under extreme rainfall, providing technical support for disaster management and emergency decision-making for underground projects under extreme rainfall.

[0020] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes that fall within the meaning and range of equivalents of the claims are intended to be included in the present invention, and any reference signs in the claims should not be construed as limiting the claims involved. In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A risk assessment method for underground engineering under extreme rainfall based on ANP-EWM-Extenics, characterized by: The following steps are involved: S1: Divide underground engineering risk areas according to geological conditions and engineering structural characteristics; S2: Establish a "rainfall difference-underground engineering mechanics stability analysis" model to analyze the structural stability under different rainfall intensities; S3: Combine historical accident data, historical monitoring data, and mechanical stability analysis models to construct a risk dataset for underground projects under extreme rainfall, and determine the main risk categories and risk assessment indicators for underground projects under extreme rainfall; S4: Based on the determined risk assessment indicators, the ANP method is used to characterize the dependency relationship between the indicators and calculate the subjective weight of each indicator. The EWM method is used to quantify the degree of dispersion of the indicator data and calculate the objective weight of each indicator. Finally, the weighted average method is used to combine the weights calculated by the ANP method and the EWM method to balance expert experience and data objectivity to obtain the combined weight of the risk assessment indicators. S5: Determine the risk element matrix, construct the correlation function, and calculate the risk level of each area and level one by one through extension operations.

2. The method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics according to claim 1, characterized in that: In S1: the project types include underground shopping malls, basements, underground garages, and subway stations. Risk areas are divided based on the underground project type, geological structure characteristics, hydrological dynamic data, and the layout of protective measures.

3. The method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics according to claim 1, characterized in that: The S2 comprises the following steps: S21: Obtain geological data and rainfall data for the underground engineering area, establish a rainfall intensity-duration relationship curve, and focus on monitoring the rainfall difference △R; S22: The shear strength parameters at different moisture contents were determined through experiments. The unsaturated soil seepage-stress coupling model was established using FLAC3D. The seepage equation is shown below. ; (1) Where ψ is the matrix suction, k(ψ) is the permeability coefficient function, which characterizes the change of unsaturated soil permeability with ψ; z is the elevation head, which reflects the gravitational potential energy and is related to the terrain elevation. During rainfall infiltration, z drives water to move deep underground; C(ψ) is the specific water capacity, which characterizes the water storage capacity of the soil; ∇(ψ + z) is the total head gradient. The rainfall difference △R changes the distribution of ψ, thereby affecting the magnitude and direction of the total head gradient. S23: Establish a quantitative relationship between rainfall difference △R and safety factor FS, as shown in the following formula, and construct a rainfall difference-underground engineering mechanics stability analysis model; ; (2) In the formula, is the effective cohesion, which can be measured by triaxial testing; is the normal total stress, calculated from the formation pressure, , is the soil weight, H is the burial depth; is the pore gas pressure, which can be measured by a pressure sensor; is the pore water pressure, measured by a piezometer; is the rainfall influence coefficient, which characterizes the amplifying effect of rainfall on matrix suction and is obtained by inverting the field monitoring data; is the effective internal friction angle, measured by direct shear test; is the shear stress, which is calculated by the limit equilibrium method, namely ,in is the internal friction angle of soil, and c is the soil cohesion.

4. The method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics according to claim 1, characterized in that: In the S3: Based on the rainfall difference-underground engineering stability analysis model established by S2, the historical accident records and historical monitoring data of underground engineering are integrated to construct a risk data set for underground engineering under rainfall. The influence curves of various risk factors on the stability of underground engineering are calculated. According to the change rate and peak size of each influence curve, the main risk categories and risk evaluation indicators of underground engineering under extreme rainfall are comprehensively determined.

5. The method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics according to claim 1, characterized in that: In said S4: S41: Based on the risk assessment indicators determined in S3, the subjective weight value of each indicator is calculated using the ANP method. The specific method is as follows: (1) Constructing a judgment matrix: Use the 1-9 scaling method to compare each indicator pairwise to generate a judgment matrix; Table 1 1-9 scaling method: (2) Consistency test: Calculate the maximum eigenvalue of the judgment matrix and use the consistency ratio CR to determine whether the matrix meets the consistency requirements. When CR < 0.1, the matrix passes the consistency test. The consistency index is calculated as follows: (3)； Where λ is the maximum eigenvalue of the consistency matrix, n is the order of the consistency matrix, if CI = 0, there is complete consistency; if CI is close to 0, it means there is satisfactory consistency; the larger the CI, the more serious the inconsistency; (3) Calculate the subjective weight W j ANP :According to the judgment matrix results, the weight matrix and super matrix are established in turn, the super matrix is ​​weighted to obtain the weighted super matrix, and the relative ranking vector of the weighted super matrix is ​​solved. The obtained relative ranking vector is the weight value of each risk factor under the ANP criterion; the calculation formulas of the unweighted super matrix, weight matrix and weighted super matrix required in the process are shown in formulas (4), (5) and (6) respectively; (4)； (5)； (6)； S42. The specific method for calculating objective weights using the EWM method is as follows: (1) Standardized data: All evaluation indicators are dimensionlessly standardized, such as minimum-maximum standardization, to ensure that the dimensions of each indicator are consistent; (2) Calculate information entropy: Calculate the information entropy value of each indicator based on the standardized value of each indicator. Information entropy reflects the degree of dispersion of the indicator. The greater the dispersion, the smaller the information entropy. The information entropy formula is: (7)； where p ij is the ratio of the standardized value of the i-th sample of the j-th indicator to the total, k' is a constant, k'=1 / ln(n), n is the total number of samples; The objective weight is thus obtained: (8)； in, is the objective weight of the jth indicator; s is the total number of evaluation indicators; S43. The subjective weight calculated by the ANP method and the objective weight calculated by the entropy weight method are combined using the weighted average method. (9); Among them, α is the subjective weight ratio.

6. The method for assessing underground engineering risk under extreme rainfall based on ANP-EWM-Extenics according to claim 1, characterized in that: In said S5, the specific steps are as follows: S51: Based on the risk assessment indicators determined in S3, an extension risk element matrix is ​​constructed, including a risk indicator matrix and a risk level matrix; the risk assessment matrix is ​​composed of the risk assessment indicators determined in S3 and is divided into a target layer, a criterion layer, and an indicator layer; The risk level matrix divides risk levels according to relevant regulations and standards and determines the intervals for each risk level; S52: For qualitative risk assessment indicators, industry experts are invited to assign scores. For quantitative risk assessment indicators, real-time monitoring data of underground projects and weather station forecast data are input and assigned scores using the rainfall difference-underground project mechanical stability analysis model. Quantify and normalize the evaluation indicators according to the grading standards and actual scores of each evaluation indicator; S53: According to the determined risk indicator matrix and risk level matrix, respectively determine the classical domain, section domain and matter-element to be evaluated of each indicator; S54: Combined with the combined weights obtained in S4, the extension distance between each risk assessment indicator and the risk assessment level interval is calculated according to formulas (10) and (11), and formula (12) is selected as the correlation function between the risk assessment indicator and the risk assessment level interval; then, the correlation between each layer of risk element and each risk assessment level is calculated according to formula (13); then, the comprehensive correlation of the region is determined according to the maximum correlation criterion, and the risk level interval to which the comprehensive correlation belongs is the risk level of the region; finally, the risk assessment levels of other regions are calculated in sequence according to the above calculation steps; (10)； (11)； (12)； (13)； Where, is the actual measured value of the i-th indicator of the k-th risk element; represents the classic domain interval of the i-th indicator under the j-th risk assessment level, where is the minimum value of the i-th indicator at this level, is the maximum value of the i-th indicator at this level; represents the section interval of the i-th index, is the minimum value of the i-th indicator in the global range, is the maximum value of the i-th indicator in the global range; Represents risk assessment indicators and the classical domain interval The extension distance, Represents risk assessment indicators and section interval The extension distance; Risk Element The correlation degree with the j-th risk assessment level.

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