A method, device and equipment for dynamic risk assessment of the whole process of construction of a subway station in a highly weathered rock stratum by a cut-and-cover arch cover method, and a storage medium
By using a dynamic risk assessment method, combining the analytic hierarchy process (AHP) and the CRITIC method to determine weights, and utilizing a cloud model to generate comprehensive risk assessment results, the accuracy problem of subway station construction risk assessment under the static assessment model is solved, and dynamic risk early warning and control during the construction process is realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ERCHU CO LTD OF CHINA RAILWAY TUNNEL GRP
- Filing Date
- 2026-06-17
- Publication Date
- 2026-07-14
AI Technical Summary
The existing risk assessment methods for underground construction of subway stations adopt a static evaluation model, which cannot reflect the dynamic changes in the types and importance of risks at different construction stages. This leads to inaccurate evaluation results and makes it difficult to achieve dynamic risk early warning and control throughout the entire construction process of subway stations.
A dynamic risk assessment method is adopted. By acquiring risk indicator data of subway station construction in stages, subjective and objective weights are determined by combining the analytic hierarchy process and the CRITIC method. A cloud model is used to generate comprehensive risk assessment results, thereby realizing dynamic risk assessment of the construction process.
It enables accurate risk assessment during the underground excavation of subway stations, provides decision support for dynamic risk early warning and control, reflects changes in risk characteristics during the construction phase, and improves the accuracy of evaluation results and engineering guidance value.
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Figure CN122390488A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of risk assessment technology for underground construction of subway stations, and in particular to a dynamic risk assessment method, device, equipment and storage medium for the entire process of underground arch-cover construction of subway stations in strongly weathered rock strata. Background Technology
[0002] With the rapid development of urban rail transit construction, subway stations, as key hubs in the rail transit system, have made construction safety management a major task. In cities with widely distributed soft-overhead and hard-underhead strata, strongly weathered rock strata are characterized by fractured rock masses, poor self-stability, and complex groundwater occurrence. Traditional open-cut and cut-and-cover methods cause significant disturbance to the surrounding environment and have high site requirements, making them difficult to implement in complex environments such as subway stations. The arch-cover method of tunneling, which involves first forming an upper arch support system and then excavating the lower soil, has been widely used in the construction of subway stations in strongly weathered rock strata. However, the arch-cover method requires multiple fundamental transformations of the stress system, each accompanied by a redistribution of loads. The risks during construction are characterized by strong dynamics, high uncertainty, and concentrated risks during the system transformation period.
[0003] Currently, the risk assessment methods for underground construction of subway stations generally adopt a static evaluation model, which cannot reflect the dynamic changes in the main risk types and importance at different construction stages during the underground arch method construction of subway stations. This leads to inaccurate evaluation results, making it difficult to achieve dynamic risk early warning and control throughout the entire construction process of subway stations, and failing to provide reliable decision support for risk management in subway station construction. Summary of the Invention
[0004] This application provides a method, device, equipment, and storage medium for dynamic risk assessment of the entire process of underground excavation of subway stations in strongly weathered rock strata using the arch-cover method. It can accurately assess the construction risks of underground excavation of subway stations in strongly weathered rock strata using the arch-cover method, so as to realize dynamic risk early warning and control of the entire process of subway station construction and provide reliable decision support for subway station construction risk management.
[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a dynamic risk assessment method for the entire construction process of the tunnel-excavation arch cover method for subway stations in strongly weathered rock strata, including: Obtain risk indicator data for each stage of the phased construction of subway stations; Based on the risk indicator data for each construction stage, the subjective weights of each risk indicator for each construction stage and the information content of each risk indicator for each construction stage are determined; the objective weights of each risk indicator for each construction stage are determined based on the information content of each risk indicator for each construction stage. The subjective weights and objective weights of each risk indicator in each construction stage are integrated to obtain the dynamic combined weights of each risk indicator in each construction stage. Based on the dynamic combination weights of each risk indicator in each construction stage and the risk indicator data of each construction stage, a comprehensive risk assessment result for each construction stage is generated using a cloud model.
[0006] Optionally, the subjective weights of the target risk indicators for each construction stage are determined as follows: Obtain the importance ratio between the target risk indicator and other risk indicators in the construction phase; the target risk indicator for the target construction phase is any risk indicator in any construction phase. A judgment matrix is constructed based on the aforementioned importance ratio; Based on the judgment matrix, the subjective weights of the target risk indicators for the target construction stage are calculated.
[0007] Optionally, the calculation of the subjective weights of the target risk indicators for the target construction stage based on the judgment matrix includes: The judgment matrix is subjected to column normalization to obtain a column normalized matrix; The subjective weights of the target risk indicators for the target construction stage are obtained by averaging the rows of the normalized matrix.
[0008] Optionally, the method further includes: The consistency index of the judgment matrix is obtained based on the largest eigenvalue of the judgment matrix; The consistency ratio of the judgment matrix is obtained based on the consistency index of the judgment matrix. The consistency of the judgment matrix is checked by comparing the consistency ratio of the judgment matrix with a preset threshold.
[0009] Optionally, the information content of the target risk indicator for each construction stage is determined in the following way: The risk indicator data for the target construction stage are standardized to obtain the standardized risk indicator data for the target construction stage; the target risk indicator for the target construction stage is any risk indicator for any construction stage among all risk indicators for each construction stage; the risk indicator data for the target construction stage is any risk indicator data for any construction stage among all risk indicator data for each construction stage. Based on the standardized target construction stage risk index data, the standard deviation representing the comparative strength of the target risk indexes at the target construction stage is obtained. Based on the correlation coefficient between the target risk indicator and other risk indicators in the same construction phase, the conflict between the target risk indicator and other risk indicators is calculated. Based on the standard deviation and the conflict, the amount of information about the target risk indicators for the target construction stage is obtained.
[0010] Optionally, the process of integrating the subjective weights and objective weights of each risk indicator at each construction stage to obtain a dynamic combination weight for each risk indicator at each construction stage includes: The weighting coefficients of each risk indicator at each construction stage are determined by minimizing the total deviation of the combined weights relative to the subjective weights and the objective weights. Based on the weighted synthesis coefficient, the subjective weights of each risk indicator in each construction stage are weighted and synthesized with the objective weights of the corresponding risk indicators in the corresponding construction stage to obtain the dynamic combination weights of each risk indicator in each construction stage.
[0011] Optionally, the step of generating a comprehensive risk assessment result for each construction stage using a cloud model based on the dynamic combination weights of each risk indicator at each construction stage and the risk indicator data at each construction stage includes: The positive cloud generator in the cloud model is used to transform the risk index data of each construction stage into cloud droplet distributions corresponding to each risk index of each construction stage. Based on the dynamic combination weights of each risk indicator in each construction stage, the cloud droplet distribution corresponding to each risk indicator in each construction stage is comprehensively synthesized to obtain the comprehensive cloud feature value of each construction stage. The comprehensive cloud feature values of each construction stage are compared with the preset standard cloud feature values to determine the risk level of each construction stage.
[0012] Secondly, this application provides a dynamic risk assessment device for the entire construction process of the tunnel-excavation arch cover method for subway stations in strongly weathered rock strata, including: The data acquisition module is used to acquire risk indicator data for each stage of the phased construction of the subway station. The weight acquisition module is used to determine the subjective weight of each risk indicator in each construction stage and the information content of each risk indicator in each construction stage based on the risk indicator data of each construction stage; determine the objective weight of each risk indicator in each construction stage based on the information content of each risk indicator in each construction stage; and integrate the subjective weight and the objective weight of each risk indicator in each construction stage to obtain the dynamic combination weight of each risk indicator in each construction stage. The evaluation result generation module is used to generate comprehensive risk evaluation results for each construction stage based on the dynamic combination weights of each risk indicator in each construction stage and the risk indicator data of each construction stage, using a cloud model.
[0013] Thirdly, this application provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.
[0014] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.
[0015] As can be seen from the above technical solution, this application has at least the following beneficial effects: This application obtains risk indicator data for each construction stage of a subway station; based on this data, it determines the subjective weight and information content of each risk indicator at each stage; it then determines the objective weight based on the information content of each risk indicator; finally, it merges the subjective and objective weights to obtain a dynamic combination weight for each risk indicator at each stage; and finally, based on this dynamic combination weight and the data, it uses a cloud model to generate a comprehensive risk assessment result for each construction stage. This dynamic combination weight incorporates both subjective judgment from expert experience and the inherent patterns of objective data, allowing for dynamic adjustment of the importance of each risk indicator as construction progresses, thus more accurately reflecting the risk characteristics of different construction stages. Meanwhile, by using cloud models to process risk indicator data, the ambiguity of expert language and the randomness of data can be effectively transformed into quantitative cloud droplet distribution and comprehensive cloud characteristic values. This achieves a transformation from qualitative to quantitative analysis, solving the problem that traditional methods are difficult to handle ambiguity and randomness. It can accurately assess the construction risks of subway station tunneling using the arch cover method in strongly weathered rock strata, so as to realize dynamic risk early warning and control throughout the construction process and provide reliable decision support for construction risk management.
[0016] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description
[0017] Figure 1 A flowchart illustrating the dynamic risk assessment method for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, provided in this application embodiment; Figure 2 A schematic diagram illustrating the principle of a forward cloud generator algorithm provided in this application embodiment; Figure 3 A schematic diagram of a dynamic risk assessment device for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, provided in this application embodiment; Figure 4 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation
[0018] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.
[0019] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0020] The existing technology uses a static evaluation model to assess the risks of underground construction in subway stations. This evaluation method does not take into account the technological characteristics of phased construction of subway stations, nor does it acquire risk indicator data that matches the risk characteristics of each construction phase. Therefore, it cannot reflect the dynamic changes in the types and importance of risks at different construction phases.
[0021] In view of this, this application provides a dynamic risk assessment method for the entire construction process of the tunnel excavation arch cover method for subway stations in strongly weathered rock strata. This method can be executed by a processing device. The processing device can be a terminal or a server. Terminals include, but are not limited to, smartphones, tablets, laptops, personal digital assistants, or smart wearable devices. Servers can be cloud servers, such as central servers in a central cloud computing cluster or edge servers in an edge cloud computing cluster. Alternatively, servers can be located in a local data center. A local data center refers to a data center directly controlled by the user.
[0022] To make the technical solution of this application clearer and easier to understand, the following describes, in conjunction with the accompanying drawings, a dynamic risk assessment method for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, as provided in the embodiments of this application. Figure 1 As shown in the figure, this is a schematic flowchart of a dynamic risk assessment method for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, provided in an embodiment of this application. The method includes: S101, the processing equipment acquires risk indicator data for each stage of the phased construction of the subway station.
[0023] It should be noted that phased construction refers to dividing the entire construction process into multiple consecutive construction phases based on the characteristics of the construction procedures during the underground excavation of subway stations. Examples include the pilot tunnel excavation phase, the support structure construction phase, the system transition phase, and the subgrade excavation phase. It should be understood that the phase division is not limited to the above examples and can also be divided into different procedural phases depending on the specific project type. Risk indicator data refers to a set of parameters that reflect the safety status of each construction phase, including but not limited to geological parameters (e.g., rock strength, groundwater level), environmental parameters (e.g., settlement monitoring values of surrounding buildings), construction parameters (e.g., excavation progress, support strength), and qualitative scores of specific risks based on expert experience. These data have distinct phase attributes; that is, the importance or numerical range of the same indicator may change abruptly in different construction phases. For example, the data characteristics of the "system transition instability risk" indicator are significantly different in the system transition phase compared to other phases.
[0024] In the embodiments provided in this application, phased construction refers to dividing the entire construction process into multiple consecutive construction phases based on the characteristics of the underground excavation of subway stations. It should be understood that underground excavation of subway stations is a complex and dynamic process, with different construction procedures corresponding to different stress system transformations and risk distributions. Traditional static evaluations often treat the entire construction period as a whole, ignoring the significant differences in risk weights between different procedures. The embodiments provided in this application, by dividing the process into phases based on the characteristics of the construction procedures, can capture the dynamic patterns of risk evolution with each procedure. For example, the pilot tunnel excavation phase is mainly characterized by ground disturbance risks, while the structural safety risks gradually increase during the pile-beam support construction phase. This division method allows the evaluation model to conduct targeted analysis of the main risks at different stages, thereby improving the engineering guidance value of the evaluation results.
[0025] As an example, the construction phase can include the pilot tunnel excavation phase, the support structure construction phase, the system conversion phase, and the subgrade excavation phase. This division is applicable to underground station projects using the arch method or similar construction methods. The pilot tunnel excavation phase mainly involves the sectional excavation and initial support of the upper pilot tunnel, with major risks including the group tunnel effect and ground instability caused by the sequential excavation of multiple pilot tunnels. The support structure construction phase mainly involves constructing permanent support systems such as piles and beams within the pilot tunnel, with major risks shifting to the safety of manually excavated pile work and the quality of joint connections. The system conversion phase is the most risky stage, involving the removal of temporary supports and the formation of an integral arch, resulting in a change in the stress system and easily leading to overall instability. The subgrade excavation phase involves excavating the lower earth and rock under the protection of the arch, with major risks shifting to environmental cumulative deformation and foundation heave. The embodiments provided in this application, by detailing the entire construction process into the above four consecutive phases, can clearly define the risk boundaries of each phase, thereby ensuring that the risk assessment results can truly reflect the actual risk status of the project.
[0026] S102, the processing equipment determines the subjective weight of each risk indicator in each construction stage and the information content of each risk indicator in each construction stage based on the risk indicator data of each construction stage; and determines the objective weight of each risk indicator in each construction stage based on the information content of each risk indicator in each construction stage.
[0027] It should be noted that subjective weights primarily reflect expert experience and the actual engineering context, compensating for deficiencies or distortions in objective data samples; objective weights primarily uncover the inherent patterns and information content of the data itself, avoiding excessive interference from human factors. Due to the high complexity of risk assessment for underground construction of subway stations, purely subjective weighting is easily influenced by limitations in expert knowledge or subjective preferences, leading to distorted evaluation results; while purely objective weighting may overlook key risks in specific engineering environments (such as protection requirements for adjacent sensitive buildings), causing evaluation results to deviate from engineering reality.
[0028] In the embodiments provided in this application, the processing device can employ the Analytic Hierarchy Process (AHP) to construct a judgment matrix based on the relative importance of risk indicators at each construction stage, and calculate the subjective weights of each risk indicator at each construction stage. Specifically, the Analytic Hierarchy Process (AHP) is a method that decomposes a complex problem into several levels and factors, and determines the relative importance of each factor through pairwise comparisons. In this embodiment, for each construction stage (e.g., the pilot tunnel excavation stage), experts in relevant fields can be invited to conduct pairwise comparisons and scoring of each risk indicator to construct a judgment matrix. For example, for the two indicators "risk of multiple pilot tunnel excavation sequence" and "risk of groundwater control failure," experts, based on experience, judge that the former is "slightly more important" or "significantly more important" than the latter, and assign corresponding scaling values. The mathematical definition of the judgment matrix is as follows:
[0029] in, For the first t Experts t =1,2,…, m The analytic hierarchy process (AHP) judgment matrix; For the first t The expert commented on the first k ( k =1,2,…, n The risk indicator relative to the first j ( j =1,2,…, n The importance ratios of the risk indicators; For the first t The expert commented on the first j The risk indicator is relative to the first k The importance ratio of each risk indicator; n is the total number of risk indicators, i.e., the order of the judgment matrix; m is the total number of experts; This indicates the reciprocity of the judgment matrix. This indicates that the same indicator is of equal importance to itself.
[0030] The processing equipment performs column normalization on the judgment matrix to obtain a column-normalized matrix. Then, it averages the column-normalized matrix by row to obtain the subjective weight of the target risk indicator for the target construction stage. The subjective weight of the target risk indicator for the target construction stage is the subjective weight of any risk indicator for any construction stage. The calculation formula for column normalization of the judgment matrix is as follows:
[0031] in, For the firstt The expert gave the first k The risk indicator is relative to the first j Normalized values of the importance of each risk indicator; For the first t The expert gave the first k The risk indicator is relative to the first j The indicator value of each risk.
[0032] The subjective weights of each indicator are obtained by averaging the rows of the normalized matrix, as shown in the following formula:
[0033] in, For the first j Subjective weighting of each risk indicator.
[0034] To ensure the logical consistency of the judgment matrix and avoid logical contradictions such as "A is more important than B, B is more important than C, and C is more important than A," the processing device can add a consistency check step. Specifically, the processing device first calculates the consistency index using the following formula. :
[0035] in, To determine the largest eigenvalue of the matrix, the consistency ratio is then calculated using the following formula. :
[0036] in, This is a random consistency index, obtained by looking up a table based on the order n of the judgment matrix, and is a fixed value. When the consistency ratio... If the value is less than a preset threshold, the judgment matrix is considered consistent; otherwise, the expert scores need to be adjusted until the consistency requirement is met. The preset threshold can be set based on expert experience or the construction situation. By leveraging the engineering experience and prior knowledge of experts, situations with missing objective data or poor data quality can be effectively addressed.
[0037] In the embodiments provided in this application, the processing device can also employ an objective weighting method based on the comparative strength and conflict of indicators, calculating the objective weights of each risk indicator at each construction stage according to the data characteristics of each risk indicator. Specifically, the objective weighting method can directly utilize the statistical characteristics of the indicator data to determine the weights, avoiding interference from human factors. As an example, the CRITIC method (Criteria Importance Though Intercrieria Correlation) can be used as a specific implementation of the objective weighting method. The basic idea of the CRITIC method is: the greater the comparative strength of an indicator measured by standard deviation, the greater the numerical difference of the indicator in each scheme, the greater the amount of information it carries, and the higher its weight should be; the smaller the conflict between indicators measured by correlation coefficient, the lower the correlation between the indicator and other indicators, the greater its independent information content, and the higher its weight should be. In specific calculations, the processing device first performs dimensionless processing on the original risk indicator data to eliminate the influence of dimensions, that is, it performs standardization processing on the target construction stage risk indicator data to obtain standardized target construction stage risk indicator data, which is any construction stage risk indicator data among the risk indicator data of each construction stage. For positive indicators where higher values indicate higher risk, the standardized formula is:
[0038] in, For the first t The expert commented on the first j The standardized value of each risk indicator score, after standardization ; For the first t The expert commented on the first j The original scores for each risk indicator; For the first j The minimum value of a risk indicator among all expert scores; For the first j The highest value of each risk indicator among all expert scores.
[0039] The processing equipment then calculates the standard deviation of each indicator using the following formula to characterize the contrast intensity:
[0040] in, For the first j Standard deviation of the data for each risk indicator after standardization; For the first j The average of standardized scores from all experts for each risk indicator.
[0041] The processing equipment then calculates the conflict between the indicators using the following formula:
[0042] in, For each indicator j Conflict with all other indicators; For the first j The first risk indicator and the first k The correlation coefficients of the risk indicators are calculated as follows:
[0043] in, For the first t The expert commented on the first j The standardized values of each risk indicator score For the first t The expert commented on the first j Standardized values for each risk indicator score; for m The expert commented on the first j The average of the original scores of each risk indicator for m The expert commented on the first j The average of the original scores for each risk indicator.
[0044] Finally, the equipment comprehensively compares the intensity and conflict of information of each indicator and calculates the objective weights using the following formula:
[0045] in, For the first j CRITIC objective weights for each risk indicator; For the first j The amount of information contained in each risk indicator is calculated using the following formula: ; For the first k The amount of information contained in each risk indicator n This represents the total number of risk indicators. The method of obtaining objective weights can objectively reflect the dispersion and interrelationships of the risk indicator data, thereby compensating for potential biases in expert subjective judgment.
[0046] S103, the processing equipment integrates the subjective weights and objective weights of each risk indicator in each construction stage to obtain the dynamic combined weights of each risk indicator in each construction stage.
[0047] In the embodiments provided in this application, after obtaining the subjective weights and objective weights of each risk indicator at each construction stage, the processing device can fuse the subjective weights and corresponding objective weights of each risk indicator at each construction stage in the following manner to obtain the dynamic combined weights of each risk indicator at each construction stage:
[0048] in, For the first stage of construction, the second stage j Dynamic combination weights of individual risk indicators; For the first stage of construction, the second stage j Subjective weighting of each risk indicator; Let be the objective weight of the j-th risk indicator; and These are the weighted composite coefficients. and Both are ≥0, and their basic values α and β can be determined by minimizing the total deviation of the combined weights relative to the two individual weights (subjective weights and objective weights). Specifically, this can be obtained by solving the following system of linear equations:
[0049] After obtaining α and β, normalize α and β using the following formula to obtain... and : ,
[0050] By integrating subjective and objective weights, the resulting dynamic combined weights possess both the rationality of expert experience and the objectivity of data-driven approaches. Furthermore, these combined weights are dynamic; the weight value of the same risk indicator differs across different construction stages (e.g., the pilot tunnel excavation stage versus the system transition stage). This dynamic characteristic accurately reflects the shifting patterns of risk importance during construction. For instance, geological conditions have a higher weight during the excavation stage, while construction quality gains weight during the support stage.
[0051] S104, the processing equipment generates comprehensive risk assessment results for each construction stage based on the dynamic combination weights of risk indicators for each construction stage and the risk indicator data for each construction stage using a cloud model.
[0052] It should be noted that in the risk assessment of underground excavation for subway stations, expert language (e.g., "high risk," "poor geological conditions") is ambiguous, while monitoring data (e.g., settlement values) is random. Traditional mathematical models struggle to handle both types of uncertainty simultaneously. The cloud model is a mathematical model capable of converting between qualitative concepts and quantitative values, characterizing the ambiguity and randomness of concepts through three numerical features: expectation, entropy, and hyperentropy. In this step, the forward cloud generator in the cloud model transforms the risk indicator data (which may be qualitative language or quantitative values) obtained in step S101 into cloud droplet distributions. This is then combined with the dynamic weighting determined in step S102 for comprehensive calculation, ultimately generating a comprehensive cloud feature value that characterizes the risk level. This achieves a scientific conversion from qualitative to quantitative, making the assessment result no longer a simple numerical point but a "cloud" with a distributional pattern. This more intuitively demonstrates the dispersion and uncertainty of the risk assessment results, providing decision-makers with richer and more reliable risk information.
[0053] In the embodiments provided in this application, the processing device can generate comprehensive risk assessment results for each construction stage based on dynamic combination weights and risk indicator data using a cloud model through the following steps S1041-S1043: S1041, the processing equipment uses the forward cloud generator in the cloud model to convert the risk index data of each construction stage into cloud droplet distributions corresponding to each risk index of each construction stage.
[0054] It should be noted that in the risk assessment of underground excavation for subway stations, many risk indicators are difficult to quantify directly, such as "complexity of geological conditions" or "standardization of construction management." Experts often use qualitative terms like "high risk" or "medium risk" to describe them. As an example, the forward cloud generator in cloud modeling can be used to transform risk indicator data. The forward cloud generator is an algorithmic model that can convert qualitative concepts into quantitative values. The algorithmic principle of this transformation can be found in [link to relevant documentation]. Figure 2 The figure illustrates the principle of a forward cloud generator algorithm provided in this application embodiment. Its input consists of three numerical features of the cloud model (expectation Ex, entropy En, and hyperentropy He) and a preset number of cloud droplets N. Internally, cloud droplet construction is achieved through a dual normal random number generation method: first, a normal random number En' is generated using En as the expectation and He as the standard deviation; then, a normal random number x representing a quantitative risk value is generated using Ex as the expectation and |En'| as the standard deviation. Finally, the degree of certainty μ(x) corresponding to the risk concept of the cloud droplet is calculated by substituting it into the normal cloud membership function. This process is repeated N times, outputting N cloud droplets. The cloud droplet swarm is used to transform the numerical characteristics of each risk indicator's cloud model into a statistically significant quantitative distribution, visually demonstrating the dispersion and uncertainty of the risk assessment results. The membership function of the positive cloud generator based on the normal cloud model is:
[0055] in, Let x be the degree of certainty (membership) of the qualitative concept, with a value range of [0,1]. The expected value of the cloud model represents the most representative value of the qualitative concept, corresponding to the highest point of the cloud map; En is the entropy of the cloud model, representing a measure of uncertainty in qualitative concepts. The larger En is, the more macroscopic and vague the concept is; e is the natural constant.
[0056] In practical operation, the processing equipment collects qualitative or quantitative scores from multiple experts for each risk indicator, and obtains the three digital features (expectation, entropy, and hyperentropy) of the cloud model corresponding to that indicator through statistical calculation. The specific calculation formula is as follows: Cloud droplet expectation calculation formula:
[0057] in, Let t be the cloud droplet expectation corresponding to the score given by the t-th expert to the j-th risk indicator.
[0058] Cloud droplet entropy calculation formula:
[0059] in, Let t be the cloud droplet entropy corresponding to the score given by the t-th expert to the j-th risk indicator.
[0060] Cloud droplet hyperentropy calculation formula:
[0061] in, Let t be the cloud droplet hyperentropy corresponding to the score given by the t-th expert to the j-th risk indicator.
[0062] The processing equipment utilizes the forward cloud generator algorithm in the cloud model to generate a large number of cloud droplets. Each droplet contains two dimensions: one is the specific value of the risk indicator, and the other is the certainty that the value belongs to a certain risk level. These droplets exhibit a cloud-like distribution pattern in the numerical space, encompassing both the central trend of the risk values and intuitively demonstrating the dispersion of the data. This allows the originally vague and uncertain expert language to be transformed into a statistically regular quantitative distribution.
[0063] S1042, the processing equipment performs comprehensive cloud synthesis on the cloud droplet distribution corresponding to each risk indicator in each construction stage based on the dynamic combination weight of each risk indicator in each construction stage, and obtains the comprehensive cloud feature value of each construction stage.
[0064] It should be noted that after obtaining the cloud droplet distributions of each risk indicator, the processing equipment needs to synthesize them into a comprehensive cloud that reflects the overall risk situation of the construction phase. As an example, the processing equipment can use dynamic weighting to weightedly synthesize the digital features of the cloud model for each indicator. The resulting comprehensive cloud feature value can include expectation, entropy, and hyperentropy. The formula for calculating the expectation of the comprehensive cloud is as follows:
[0065] in, This represents the comprehensive cloud expectation for the first stage of construction.
[0066] The formula for calculating the comprehensive cloud entropy is as follows:
[0067] in, This represents the comprehensive cloud entropy for the first construction stage.
[0068] The formula for calculating the hyperentropy of the comprehensive cloud is as follows:
[0069] in, This represents the comprehensive cloud hyperentropy for the first construction stage. It should be noted that the expected value, used to characterize the central value of the risk level, reflects the central tendency of the risk level at this construction stage. For example, if the calculated expected value is 3.8, it means that the risk level at this stage is between high and medium risk, but leans more towards high risk. Entropy is used to characterize the ambiguity of the risk assessment, reflecting the degree of disagreement among experts. The larger the entropy value, the more inconsistent the experts' evaluation of the risk indicator, and the stronger the uncertainty of the risk; the smaller the entropy value, the more concentrated the experts' opinions, and the more certain the evaluation result. Hyperentropy is used to characterize the uncertainty of entropy, i.e., the dispersion of entropy, reflecting the randomness of the risk assessment. The larger the hyperentropy, the more uneven the thickness of the cloud droplets, and the more severe the random fluctuations in the risk assessment.
[0070] S1043, the processing equipment compares the comprehensive cloud characteristic values of each construction stage with the preset standard cloud characteristic values to determine the risk level of each construction stage.
[0071] It should be noted that, in order to convert the comprehensive cloud feature value into an intuitive risk level, standard cloud feature values can be pre-set. Standard cloud feature values are standardized numerical characteristics set for different risk levels (e.g., Level I to Level V) based on industry standards or historical experience. For example, the expected standard cloud value for Level I risk (low risk) might be set to 0.5, while the expected standard cloud value for Level V risk (high risk) might be set to 4.5. During the comparison process, the distance or similarity between the comprehensive cloud feature value and each standard cloud feature value is calculated. The principle of maximum membership can be used, that is, determining which standard cloud the comprehensive cloud feature value is closest to, thereby determining the risk level of that construction stage. For example, if the expected comprehensive cloud value calculated for a certain stage is 4.3, and its distance is closest to the Level V risk standard cloud (expected value 4.5), then the processing equipment determines that stage to be at a high risk level. The processing equipment performs the above steps to achieve a scientific mapping from quantitative calculation results to qualitative risk levels, providing clear decision-making basis for on-site management personnel.
[0072] In the embodiments provided in this application, to verify the effectiveness of the dynamic risk assessment method for the entire construction process of the tunnel-and-cover method for subway stations in strongly weathered rock strata provided in this application, a detailed description is given using the tunnel-and-cover method construction of Daminghu East Station on Metro Line 6 of a certain city as an example. It should be understood that this embodiment is only used to explain this application and does not constitute a limitation on the scope of protection of this invention.
[0073] It should be noted that this project is located in the central area of the old city, in an extremely sensitive surrounding environment. It passes alongside a 220kV power tunnel and is adjacent to the East Moat and Daming Lake. The geological conditions are typical of "soft upper layer and hard lower layer" strata, with the upper part of the arch consisting of strongly weathered diorite with poor self-stability. The project adopts the arch method of construction, which is complex and involves multiple stress system transformations, resulting in risks with obvious dynamic characteristics.
[0074] First, step S101 is executed, and the processing equipment acquires risk indicator data for each construction stage of the subway station's phased construction. This embodiment divides the entire construction process into four consecutive stages based on the characteristics of the construction procedures: the pilot tunnel excavation stage, the support structure construction stage (pile-beam construction), the system conversion stage, and the lower excavation stage. Considering the characteristics of the arch-type tunnel excavation method, an evaluation system containing nine risk indicators is constructed, specifically including: risk of multi-pilot tunnel excavation sequence and spatial effects, risk of pile-beam support system forming quality, risk of system conversion instability, risk of arch lining pouring and settlement, risk of groundwater control failure, risk of side / underpassing sensitive environments, risk of specialized manual excavation of bored piles, risk of cross-operations in confined spaces, and risk of initial support construction quality. Five geotechnical engineering experts were invited to score each risk indicator using a 7-level scale (1-7 points correspond to risk levels: 1 = extremely low risk, 7 = extremely high risk), obtaining the original risk indicator data for each construction stage.
[0075] In the embodiments provided in this application, in order to clarify the potential risks of the tunnel excavation of the arch cover method for subway stations in strongly weathered rock strata, and in combination with the actual situation of the Daminghu East Station project of Metro Line 6 in a certain city and the characteristics of the arch cover method, risk identification is carried out from three dimensions: main structural risks, geological and environmental interaction risks, and construction operation safety risks, and a risk identification list is formed, as shown in Table 1 below.
[0076] Table 1 Risk Identification List for the Arch-Cover Method Excavation of Daming Lake East Station
[0077] Then, step S102 is executed. The processing equipment determines the subjective weights and information content of each risk indicator in each construction stage based on the risk indicator data for each stage. The objective weights of each risk indicator in each construction stage are determined based on the information content. Specifically, the Analytic Hierarchy Process (AHP) is used to calculate the subjective weights of each risk indicator in each construction stage, and the CRITIC method is used to calculate the objective weights. Taking the system transformation stage as an example, this stage is the most risk-concentrated part of the arch construction, involving the removal of temporary supports and the formation of the overall arch, resulting in a fundamental transformation of the stress system. The expert judgment matrix shows that the "system transformation instability risk" has the highest relative importance in this stage, leading to its significantly higher subjective weight than other indicators. Simultaneously, the CRITIC method's calculation results based on data comparison strength and conflict also show that the data fluctuations of each risk indicator in this stage are large, and the distribution of objective weights has a certain degree of dispersion. Then, step S103 is executed, where the processing equipment integrates the subjective weights and objective weights of each risk indicator in each construction stage to obtain the dynamic combined weights of each risk indicator in each construction stage. The processing equipment obtains the dynamic combined weights of each risk indicator in that stage by integrating the subjective weights with the corresponding objective weights.
[0078] In the embodiments provided in this application, the relevant weight calculation results are shown in Table 2 below.
[0079] Table 2. Calculation results of weights at each stage and
[0080]
[0081] The calculation results show that during the system transition phase, the combined weight of "system transition instability risk" reaches 0.284, and the combined weight of "arch lining pouring risk" reaches 0.210, both significantly higher than other indicators. This indicates that the focus of risk management during the system transition phase should be on the stress transformation of the structural system and the quality of the arch lining. In contrast, during the pile-beam construction phase, the combined weight of "specialized operation risk of manually excavated piles" is the highest, at 0.288, reflecting that manually excavated piles still pose a significant risk in engineering construction. During the pilot tunnel excavation phase, the combined weight of "multi-pilot tunnel excavation sequence risk" is 0.276, accurately reflecting the risk characteristic dominated by the "group tunnel effect" at this stage. In the lower excavation construction phase, since the upper construction has been completed, the combined weight of "system transition instability risk" is relatively small at 0.249. This characteristic of dynamic changes in weight with the construction stage verifies that this application can capture the migration pattern of risks.
[0082] Finally, in step S104, the processing equipment generates a comprehensive risk assessment result for each construction stage based on the dynamic combination weights of each risk indicator and the risk indicator data for each construction stage, using a cloud model. The expert scoring data is converted into cloud droplet distributions using the forward cloud generator in the cloud model, obtaining the cloud digital characteristics of each risk indicator for each construction stage. In the embodiments provided in this application, the cloud digital characteristics of each risk indicator in the system conversion stage are shown in Table 3 below.
[0083] Table 3 Cloud Digital Characteristics of 9 Risk Indicators During the System Transformation Phase
[0084] The cloud digital characteristics of each risk indicator at each construction stage are combined with dynamic weights to synthesize a comprehensive cloud. The calculated comprehensive cloud characteristic values for each stage, namely the comprehensive cloud expectation, comprehensive cloud entropy, and comprehensive cloud hyperentropy, are as follows: 3.77, 0.44, and 0.223 for the pilot tunnel excavation stage; 3.71, 0.43, and 0.22 for the support structure construction stage; 4.10, 0.46, and 0.23 for the system transformation stage; and 3.29, 0.43, and 0.22 for the lower excavation stage. Here, the comprehensive cloud expectation represents the central value of the risk level, the comprehensive cloud entropy represents ambiguity, and the comprehensive cloud hyperentropy represents randomness.
[0085] In the embodiments provided in this application, in order to achieve quantitative classification and judgment of construction risks, the safety of underground construction of subway stations is divided into five risk levels according to relevant specifications and research results, and the scaling interval and standard cloud parameters corresponding to each level are given, as shown in Table 4 below.
[0086] Table 4 Safety Levels for Cut-and-Cut Construction of Subway Stations (Standard Cloud)
[0087] The processing equipment compares the aforementioned comprehensive cloud characteristic value with the preset standard cloud characteristic value. This application adopts a comprehensive cloud expectation proximity judgment method: the comprehensive cloud expectation value is assigned to the corresponding risk level if the difference between it and the standard cloud expectation value at each level is the smallest. When the value of the comprehensive cloud expectation value is at the critical position between two levels of standard cloud expectation values, in accordance with the strict control requirements for construction safety, it is preferentially judged as a higher level of risk. Specifically, the critical position refers to the situation where the absolute difference between the comprehensive cloud expectation value and the expectation values of two adjacent levels of standard clouds is equal. For example, if the comprehensive cloud expectation value at a certain construction stage is 4.00, the absolute difference between it and the level IV standard cloud expectation value of 3.5 is 0.50, and the absolute difference between it and the level V standard cloud expectation value of 4.5 is also 0.50. The absolute differences are equal, and the value of the comprehensive cloud expectation value is at the critical position. Its corresponding risk level is preferentially judged as a higher level of risk, namely, level V high risk. In the embodiments provided in this application, the comprehensive cloud expectation values for the pilot tunnel excavation stage (3.77), the support structure construction stage (3.71), and the lower excavation stage (3.29) are all closest to the Level IV standard cloud expectation value of 3.5, thus classifying it as Level IV with higher risk. The comprehensive cloud expectation value for the system transition stage (4.10) is closer to the Level V standard cloud expectation value of 4.5, classifying it as Level V with high risk. This judgment result is highly consistent with the actual engineering situation: the system transition stage, as a critical node for the transformation of the stress system, experiences a sharp increase in stress at the arch foot, making it highly susceptible to overall instability, and is indeed the stage with the highest risk throughout the entire life cycle. Simultaneously, the over-entropy He=0.46 of the system transition stage is higher than other stages, indicating that there is some disagreement among experts regarding the risk assessment of this stage, which also aligns with the objective fact that the stress mechanism of this stage is complex and highly uncertain.
[0088] To verify the stability of the weight synthesis method based on the weight synthesis coefficient, this application also performed calculation and analysis using the product weight combination method. Except for slight differences in the data, the obtained ranking results were completely consistent with the ranking results obtained by the weight synthesis method based on the weight synthesis coefficient.
[0089] Through the verification of this embodiment, the method provided by this application can not only achieve quantitative classification of risk levels, but also explain the degree of uncertainty of risk assessment through entropy and hyperentropy. This provides a scientific basis for on-site managers to formulate targeted control measures (such as implementing full-process monitoring and stress monitoring and early warning during the system transformation stage), and effectively verifies the engineering applicability and accuracy of the method provided by this application.
[0090] Based on the above description, this application has the following beneficial effects: The dynamic risk assessment method for the entire construction process of the tunnel-excavation arch cover method for subway stations in strongly weathered rock strata provided in this application acquires risk indicator data for each stage of construction, determines subjective and objective weights separately, and then integrates them to obtain a dynamic combined weight. This dynamic combined weight considers both the subjective judgment of expert experience and the inherent laws of objective data, and can dynamically adjust the importance of each risk indicator as the construction stage progresses, thereby more accurately reflecting the risk characteristics of different construction stages. Simultaneously, by using a cloud model to process the risk indicator data, the ambiguity of expert language and the randomness of data can be effectively transformed into quantitative cloud droplet distribution and comprehensive cloud characteristic values, achieving a qualitative to quantitative conversion. This solves the problem of risk ambiguity and randomness that traditional methods struggle to handle, improves the accuracy and reliability of risk assessment results, and provides scientific support for the safety of tunnel-excavation construction of subway stations. This application addresses the characteristics of arch-type construction, which involves multiple stress system transformations and dramatic risk shifts across stages. It divides the entire construction process into multiple key stages and dynamically adjusts the importance of risk indicators according to each stage through dynamic weighting. This solves the technical problem that traditional static evaluation methods cannot capture the high-risk characteristics of system transformation stages. Furthermore, by combining cloud models, it effectively handles the fuzziness and randomness of risk assessment, thereby improving the accuracy and reliability of risk assessment for arch-type construction in strongly weathered rock formations.
[0091] The above text combined Figure 1 This application provides a detailed description of a dynamic risk assessment method for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata. The apparatus and equipment provided in this application will be described below with reference to the accompanying drawings.
[0092] like Figure 3 As shown in the figure, this is a schematic diagram of a dynamic risk assessment device for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, provided in an embodiment of this application. The dynamic risk assessment device 300 for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata includes: The data acquisition module 310 is used to acquire risk indicator data for each stage of the phased construction of the subway station. The weight acquisition module 320 is used to determine the subjective weight of each risk indicator in each construction stage and the information content of each risk indicator in each construction stage based on the risk indicator data of each construction stage; determine the objective weight of each risk indicator in each construction stage based on the information content of each risk indicator in each construction stage; and integrate the subjective weight and the objective weight of each risk indicator in each construction stage to obtain the dynamic combination weight of each risk indicator in each construction stage. The evaluation result generation module 330 is used to generate a comprehensive risk evaluation result for each construction stage based on the dynamic combination weights of each risk indicator in each construction stage and the risk indicator data of each construction stage, using a cloud model.
[0093] Optional, the weight acquisition module 320 is specifically used for: Obtain the importance ratio between the target risk indicator and other risk indicators in the construction phase; the target risk indicator for the target construction phase is any risk indicator in any construction phase. A judgment matrix is constructed based on the aforementioned importance ratio; Based on the judgment matrix, the subjective weights of the target risk indicators for the target construction stage are calculated.
[0094] Optional, the weight acquisition module 320 is specifically used for: The judgment matrix is subjected to column normalization to obtain a column normalized matrix; The subjective weights of the target risk indicators for the target construction stage are obtained by averaging the rows of the normalized matrix.
[0095] Optionally, the weight acquisition module 320 is also used for: The consistency index of the judgment matrix is obtained based on the largest eigenvalue of the judgment matrix; The consistency ratio of the judgment matrix is obtained based on the consistency index of the judgment matrix. The consistency of the judgment matrix is checked by comparing the consistency ratio of the judgment matrix with a preset threshold.
[0096] Optional, the weight acquisition module 320 is specifically used for: The risk indicator data for the target construction stage are standardized to obtain the standardized risk indicator data for the target construction stage; the target risk indicator for the target construction stage is any risk indicator for any construction stage among all risk indicators for each construction stage; the risk indicator data for the target construction stage is any risk indicator data for any construction stage among all risk indicator data for each construction stage. Based on the standardized target construction stage risk index data, the standard deviation representing the comparative strength of the target risk indexes at the target construction stage is obtained. Based on the correlation coefficient between the target risk indicator and other risk indicators in the same construction phase, the conflict between the target risk indicator and other risk indicators is calculated. Based on the standard deviation and the conflict, the amount of information about the target risk indicators for the target construction stage is obtained.
[0097] Optional, the weight acquisition module 320 is specifically used for: The weighting coefficients of each risk indicator at each construction stage are determined by minimizing the total deviation of the combined weights relative to the subjective weights and the objective weights. Based on the weighted synthesis coefficient, the subjective weights of each risk indicator in each construction stage are weighted and synthesized with the objective weights of the corresponding risk indicators in the corresponding construction stage to obtain the dynamic combination weights of each risk indicator in each construction stage.
[0098] Optionally, the evaluation result generation module 330 is specifically used for: The positive cloud generator in the cloud model is used to transform the risk index data of each construction stage into cloud droplet distributions corresponding to each risk index of each construction stage. Based on the dynamic combination weights of each risk indicator in each construction stage, the cloud droplet distribution corresponding to each risk indicator in each construction stage is comprehensively synthesized to obtain the comprehensive cloud feature value of each construction stage. The comprehensive cloud feature values of each construction stage are compared with the preset standard cloud feature values to determine the risk level of each construction stage.
[0099] According to the embodiments of this application, a dynamic risk assessment device for the entire process of tunnel excavation and arch cover construction of subway stations in strongly weathered rock strata can be used to execute the method described in the embodiments of this application. Furthermore, the other operations and / or functions of each module / unit of the dynamic risk assessment device for the entire process of tunnel excavation and arch cover construction of subway stations in strongly weathered rock strata are respectively for the purpose of achieving… Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.
[0100] This application also provides a computing device.
[0101] like Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.
[0102] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0103] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).
[0104] The communication interface 703 is used for external communication.
[0105] Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).
[0106] The memory 704 stores executable code, and the processor 702 executes the executable code to perform the aforementioned dynamic risk assessment method for the entire construction process of the tunnel arch cover method for subway stations in strongly weathered rock strata.
[0107] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 In the embodiment, when each module or unit of the dynamic risk assessment device for the entire process of the underground excavation arch cover method for subway stations in strongly weathered rock strata described in the example is implemented through software, the following steps are performed: Figure 3 The software or program code required for the functions of each module / unit can be partially or entirely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704, and executes the aforementioned dynamic risk assessment method for the entire construction process of the underground excavation arch cover method for subway stations in strongly weathered rock strata.
[0108] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium capable of being stored by a computing device, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned dynamic risk assessment method for the entire construction process of the underground excavation arch cover method for subway stations in strongly weathered rock strata.
[0109] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.
[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.
Claims
1. A dynamic risk assessment method for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, characterized in that... The method includes: Obtain risk indicator data for each stage of the phased construction of subway stations; Based on the risk indicator data for each construction stage, the subjective weights of each risk indicator for each construction stage and the information content of each risk indicator for each construction stage are determined; the objective weights of each risk indicator for each construction stage are determined based on the information content of each risk indicator for each construction stage. The subjective weights and objective weights of each risk indicator in each construction stage are integrated to obtain the dynamic combined weights of each risk indicator in each construction stage. Based on the dynamic combination weights of each risk indicator in each construction stage and the risk indicator data of each construction stage, a comprehensive risk assessment result for each construction stage is generated using a cloud model.
2. The method according to claim 1, characterized in that, The subjective weights of the target risk indicators for each construction stage are determined as follows: Obtain the importance ratio between the target risk indicator and other risk indicators in the construction phase; The target risk indicator for the target construction phase is any risk indicator for any construction phase among all risk indicators for each construction phase. A judgment matrix is constructed based on the aforementioned importance ratio; Based on the judgment matrix, the subjective weights of the target risk indicators for the target construction stage are calculated.
3. The method according to claim 2, characterized in that, The subjective weights of the target risk indicators for the target construction stage are calculated based on the judgment matrix, including: The judgment matrix is subjected to column normalization to obtain a column normalized matrix; The subjective weights of the target risk indicators for the target construction stage are obtained by averaging the rows of the normalized matrix.
4. The method according to claim 2, characterized in that, The method further includes: The consistency index of the judgment matrix is obtained based on the largest eigenvalue of the judgment matrix; The consistency ratio of the judgment matrix is obtained based on the consistency index of the judgment matrix. The consistency of the judgment matrix is checked by comparing the consistency ratio of the judgment matrix with a preset threshold.
5. The method according to claim 1, characterized in that, The information content of the target risk indicator for each construction stage is determined in the following way: The risk indicator data for the target construction stage are standardized to obtain the standardized risk indicator data for the target construction stage; the target risk indicator for the target construction stage is any risk indicator for any construction stage among all risk indicators for each construction stage; the risk indicator data for the target construction stage is any risk indicator data for any construction stage among all risk indicator data for each construction stage. Based on the standardized target construction stage risk index data, the standard deviation representing the comparative strength of the target risk indexes at the target construction stage is obtained. Based on the correlation coefficient between the target risk indicator and other risk indicators in the same construction phase, the conflict between the target risk indicator and other risk indicators is calculated. Based on the standard deviation and the conflict, the amount of information about the target risk indicators for the target construction stage is obtained.
6. The method according to claim 1, characterized in that, The process of integrating the subjective weights and objective weights of each risk indicator at each construction stage to obtain the dynamic combined weights of each risk indicator at each construction stage includes: The weighting coefficients of each risk indicator at each construction stage are determined by minimizing the total deviation of the combined weights relative to the subjective weights and the objective weights. Based on the weighted synthesis coefficient, the subjective weights of each risk indicator in each construction stage are weighted and synthesized with the objective weights of the corresponding risk indicators in the corresponding construction stage to obtain the dynamic combination weights of each risk indicator in each construction stage.
7. The method according to claim 1, characterized in that, The method, based on the dynamic combination weights of risk indicators at each construction stage and the risk indicator data at each construction stage, utilizes a cloud model to generate comprehensive risk assessment results for each construction stage, including: The positive cloud generator in the cloud model is used to transform the risk index data of each construction stage into cloud droplet distributions corresponding to each risk index of each construction stage. Based on the dynamic combination weights of each risk indicator in each construction stage, the cloud droplet distribution corresponding to each risk indicator in each construction stage is comprehensively synthesized to obtain the comprehensive cloud feature value of each construction stage. The comprehensive cloud feature values of each construction stage are compared with the preset standard cloud feature values to determine the risk level of each construction stage.
8. A dynamic risk assessment device for the entire construction process of a subway station using the cut-and-cover method in strongly weathered rock strata, characterized in that... The device includes: The data acquisition module is used to acquire risk indicator data for each stage of the phased construction of the subway station. The weight acquisition module is used to determine the subjective weight of each risk indicator in each construction stage and the information content of each risk indicator in each construction stage based on the risk indicator data of each construction stage; determine the objective weight of each risk indicator in each construction stage based on the information content of each risk indicator in each construction stage; and integrate the subjective weight and the objective weight of each risk indicator in each construction stage to obtain the dynamic combination weight of each risk indicator in each construction stage. The evaluation result generation module is used to generate comprehensive risk evaluation results for each construction stage based on the dynamic combination weights of each risk indicator in each construction stage and the risk indicator data of each construction stage, using a cloud model.
9. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 7.