A risk classification and identification method applied to open-cut foundation pit engineering for rail transit
By constructing an analytical calculation model for numerical calculation, the problem of insufficient accuracy of qualitative criteria in open-cut foundation pit engineering for rail transit was solved, and high-precision and reliable risk classification judgment was achieved.
Patent Information
- Application Number
- CN202512001880.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-05-26
AI Technical Summary
Existing technologies lack sufficient accuracy and reliability in qualitative and composite criteria for judging open-cut foundation pit projects in rail transit, and cannot meet the needs of efficient and accurate intelligent management and control.
By acquiring multi-dimensional monitoring datasets, establishing structured data, constructing an analysis and calculation model based on preset grading rules, performing numerical calculations to replace direct logical comparisons, achieving grading processing, determining the sub-item grade values, and comprehensively evaluating the grades.
It improves the accuracy and reliability of risk classification in open-cut foundation pit engineering for rail transit, avoids the discrimination error caused by the semantic complexity of the criteria, and realizes the data-driven and standardized discrimination process.
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Figure CN122089047A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of safety technology in rail transit engineering construction, and in particular to a risk classification and identification method applied to open-cut foundation pit engineering in rail transit. Background Technology
[0002] Safety monitoring of open-cut foundation pit projects for rail transit involves real-time assessment and dynamic hierarchical management of multi-dimensional parameters such as support structure deformation, ground settlement, stress changes, and environmental risks during construction. With the expansion of urban rail transit construction and the increasing complexity of the construction environment, traditional assessment methods relying on manual experience are no longer sufficient to meet the demands for efficient and accurate intelligent control. In particular, the dynamic comprehensive assessment grading standard for open-cut methods in rail transit engineering includes dozens of qualitative criteria such as cumulative settlement, monitoring and early warning, structural deformation, and environmental risks. Manual processing is not only inefficient but also susceptible to subjective factors, failing to achieve real-time monitoring and rapid response. Therefore, how to transform complex grading criteria into computer-executable intelligent analysis models has become a pressing technical problem in the field of rail transit engineering safety monitoring.
[0003] To address the aforementioned technical challenges, existing technologies generally employ logical search methods from computer languages to programmatically implement the grading criteria. Specifically, technicians directly translate the judgment rules in the dynamic comprehensive evaluation of the open-cut method, such as "the proportion of monitoring points with cumulative deformation values exceeding control values exceeds 50%" and "three or more red monitoring warnings appearing in a single instance in a unit project," into logical judgment statements in a computer program. Through structured programming methods such as conditional branching and loop traversal, the monitoring data is compared and searched one by one to output the evaluation results. This method can achieve automated judgment in simple rule scenarios and has already been initially applied in some engineering monitoring systems.
[0004] However, existing logical search methods have significant drawbacks. First, the discrimination criteria in the dynamic comprehensive evaluation grading standard for open-cut excavation projects are highly semantically complex and logically inclusive, heavily relying on qualitative descriptions such as "severe cracking" and "adjacent to large-diameter, water-bearing, and pressurized pipelines." Traditional computer languages require logical expressions to strictly conform to formal specifications, creating a fundamental gap between the two. Second, direct logical search cannot effectively handle fuzzy boundaries and complex conditions, such as judgments requiring spatial relationships and attribute coupling, like "adjacent to rivers and lakes with no seepage prevention measures at the bottom," which easily leads to omissions or misjudgments. Furthermore, the nonlinear correlations and weighting effects between multi-dimensional indicators are difficult to reflect in simple logical search, causing bias in evaluation results. Practice has shown that intelligent discrimination methods implemented using direct logical search have a high error rate when facing complex engineering conditions and cannot meet the stringent requirement of 100% accuracy for dynamic hierarchical management of open-cut foundation pit projects in rail transit.
[0005] In summary, although existing technologies attempt to computerize the classification criteria through logical search, the inherent contradiction between the qualitative characteristics of the discrimination criteria and the formal requirements of computer processing leads to insufficient discrimination accuracy and limited reliability. Summary of the Invention
[0006] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically the insufficient accuracy and limited reliability of qualitative and composite criteria in open-cut foundation pit engineering for rail transit. Specifically, this invention provides a risk classification and identification method for open-cut foundation pit engineering for rail transit, as detailed below: 1) In a first aspect, the present invention provides a risk classification and identification method for open-cut foundation pit engineering in rail transit, the specific technical solution of which is as follows: S1, Obtain multi-dimensional monitoring datasets during the construction process of open-cut foundation pit projects; S2, determine the structured data corresponding to each type of monitoring data based on the multi-dimensional monitoring dataset; S3, establish an analysis and calculation model based on preset hierarchical rules, and analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model to obtain the sub-item level values corresponding to various types of monitoring data; S4. Determine the comprehensive evaluation level of the open-cut foundation pit project based on all sub-item level values.
[0007] The beneficial effects of the risk classification and identification method for open-cut foundation pit engineering in rail transit provided by this invention are as follows: By acquiring multi-dimensional monitoring datasets and determining corresponding structured data, qualitative discrimination criteria are transformed into quantitative data representations, overcoming the shortcomings of traditional logical search methods that are difficult to adapt to qualitative criteria. An analytical calculation model is established based on preset grading rules, and various monitoring data are graded according to the analytical calculation model to obtain sub-item grade values. Numerical calculation replaces direct logical comparison, avoiding discrimination errors caused by the semantic complexity of the criteria. By determining the comprehensive evaluation level based on all sub-item grade values, the datafication and standardization of the discrimination process are realized, significantly improving the accuracy and reliability of risk grading discrimination in open-cut foundation pit engineering for rail transit.
[0008] Based on the above solution, the present invention can be further improved as follows.
[0009] Furthermore, the multi-dimensional monitoring dataset includes at least: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data.
[0010] Furthermore, the step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix; The settlement excess ratio is determined based on the aforementioned settlement excess identification matrix; The sub-level values of the cumulative settlement monitoring data are determined based on the settlement excess ratio and the analysis and calculation model. The settlement monitoring matrix is the structured data corresponding to the cumulative settlement monitoring data. It is constructed by the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
[0011] Furthermore, the step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: The daily cumulative warning value is determined by the sum of the column elements of the monitoring and warning matrix, and the continuous warning indicator value is determined by the product of the adjacent row elements of the monitoring and warning matrix. The sub-level values of the monitoring and early warning data in the risk early warning monitoring data are determined based on the daily cumulative early warning value, the continuous early warning identification value, and the analysis and calculation model. The monitoring and early warning matrix is the structured data corresponding to the monitoring and early warning data in the risk early warning monitoring data. It is constructed by the monitoring and early warning data according to the monitoring points and dates. The matrix elements in the monitoring and early warning matrix represent the early warning level of the corresponding monitoring point on the corresponding date.
[0012] 2) In a second aspect, the present invention also provides a risk classification and identification system for open-cut foundation pit engineering of rail transit, the specific technical solution of which is as follows: a data acquisition module, a data organization module, a classification calculation module and a comprehensive evaluation module; The data acquisition module is used to acquire multi-dimensional monitoring datasets during the construction process of open-cut foundation pit engineering; The data organization module is used to determine the structured data corresponding to various types of monitoring data based on the multi-dimensional monitoring dataset; The hierarchical calculation module is used to establish an analysis and calculation model based on preset hierarchical rules, and to analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model to obtain the sub-item level values corresponding to various types of monitoring data. The comprehensive evaluation module is used to determine the comprehensive evaluation level of the open-cut foundation pit project based on all sub-item level values.
[0013] Based on the above solution, the present invention can be further improved as follows.
[0014] Furthermore, the multi-dimensional monitoring dataset includes at least: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data.
[0015] Furthermore, the step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix; The settlement excess ratio is determined based on the aforementioned settlement excess identification matrix; The sub-level values of the cumulative settlement monitoring data are determined based on the settlement excess ratio and the analysis and calculation model. The settlement monitoring matrix is the structured data corresponding to the cumulative settlement monitoring data. It is constructed by the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
[0016] Furthermore, the step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: The daily cumulative warning value is determined by the sum of the column elements of the monitoring and warning matrix, and the continuous warning indicator value is determined by the product of the adjacent row elements of the monitoring and warning matrix. The sub-level values of the monitoring and early warning data in the risk early warning monitoring data are determined based on the daily cumulative early warning value, the continuous early warning identification value, and the analysis and calculation model. The monitoring and early warning matrix is the structured data corresponding to the monitoring and early warning data in the risk early warning monitoring data. It is constructed by the monitoring and early warning data according to the monitoring points and dates. The matrix elements in the monitoring and early warning matrix represent the early warning level of the corresponding monitoring point on the corresponding date.
[0017] 3) In a third aspect, the present invention also provides a computer device, the computer device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the computer device to implement any of the above methods.
[0018] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above methods.
[0019] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description
[0020] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of a risk classification and identification method applied to open-cut foundation pit engineering for rail transit, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
[0022] like Figure 1 As shown in the figure, a risk classification and identification method for open-cut foundation pit engineering in rail transit according to an embodiment of the present invention includes the following steps: S1, Obtain multi-dimensional monitoring datasets during the construction process of open-cut foundation pit projects; S2, determine the structured data corresponding to various types of monitoring data based on the multi-dimensional monitoring dataset; S3, establish an analysis and calculation model based on preset grading rules, analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model, and obtain the sub-item grade values corresponding to various types of monitoring data; S4. Determine the comprehensive evaluation level of the open-cut foundation pit project based on all sub-item level values.
[0023] The beneficial effects of the risk classification and identification method for open-cut foundation pit engineering in rail transit provided by this invention are as follows: By acquiring multi-dimensional monitoring datasets and determining corresponding structured data, qualitative discrimination criteria are transformed into quantitative data representations, overcoming the shortcomings of traditional logical search methods that are difficult to adapt to qualitative criteria. An analytical calculation model is established based on preset grading rules, and various monitoring data are graded according to the analytical calculation model to obtain sub-item grade values. Numerical calculation replaces direct logical comparison, avoiding discrimination errors caused by the semantic complexity of the criteria. By determining the comprehensive evaluation level based on all sub-item grade values, the datafication and standardization of the discrimination process are realized, significantly improving the accuracy and reliability of risk grading discrimination in open-cut foundation pit engineering for rail transit.
[0024] It should be noted that, for ease of understanding, the technical terms used in this solution will be explained one by one, and will not be repeated hereafter: Open-cut foundation pit engineering for urban rail transit: refers to foundation pit engineering constructed using the open-cut method in urban rail transit construction. The foundation pit is formed by excavating the ground surface, and the station or section structure is constructed inside the foundation pit. During the construction process, systematic safety monitoring of the support structure, surrounding ground surface, underground pipelines and buildings is required to ensure the safety and reliability of the construction process.
[0025] Risk classification and identification method: refers to the method of quantitatively assessing and dynamically classifying the safety risks in the construction process of open excavation foundation pit projects. By collecting multi-dimensional monitoring data and using preset rules for calculation and analysis, the risk level of the project is output, providing technical support for safety management decisions.
[0026] Multi-dimensional monitoring dataset: This refers to the collection of various monitoring data that need to be collected during the construction of foundation pit excavation projects, including but not limited to cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data. These various monitoring data reflect the safety status of the project from different dimensions.
[0027] Structured data refers to the data format obtained after standardizing multi-dimensional monitoring datasets according to a preset data organization form, including two-dimensional data arrays and one-dimensional data sequences, which facilitates batch calculations and logical judgments by computers.
[0028] Preset grading rules: These refer to the quantitative judgment standards set according to the safety management requirements of open excavation foundation pit projects, including rules for calculating the proportion of settlement exceeding limits, rules for statistical analysis of early warning frequency, rules for comparing structural deformation thresholds, and rules for logical judgment of environmental risks, which are used to construct analysis and calculation models.
[0029] Analysis and calculation model: This refers to the transformation of pre-defined analysis rules into mathematical models, used to convert structured data into corresponding sub-item level values. In this scheme, the analysis and calculation models include: settlement exceedance ratio analysis model, cumulative settlement deformation analysis model, monitoring and early warning analysis model, inspection and early warning analysis model, retaining structure deformation analysis model, stress monitoring analysis model, structural crack analysis model, environmental risk analysis model, excavation depth analysis model, and hydrogeological analysis model. The process of establishing the analysis and calculation model based on the pre-defined grading rules is as follows: The calculation rules for the settlement excess ratio are based on the cumulative settlement monitoring data. The analysis directions are divided into whether the settlement excess ratio exceeds the threshold and whether the average cumulative settlement deformation exceeds the threshold. Based on the settlement excess ratio and the average cumulative settlement deformation, the corresponding settlement excess ratio analysis model and cumulative settlement deformation analysis model are constructed respectively. The preset classification is bound to the analysis direction, and the corresponding first sub-item level value and second sub-item level value are determined by the magnitude of the analysis value.
[0030] For the statistical rules on the frequency of early warnings, the corresponding data basis is risk early warning monitoring data. The analysis direction is divided into monitoring and early warning data analysis and patrol early warning data analysis. Based on the monitoring and early warning data and the patrol early warning data, corresponding monitoring and early warning analysis models and patrol early warning analysis models are constructed respectively. The preset level is bound to the analysis direction, and the corresponding third and fourth sub-item level values are determined by the magnitude of the analysis values.
[0031] The structural deformation threshold comparison rules are based on structural deformation monitoring data. The analysis directions are divided into enclosure structure deformation monitoring data analysis and stress monitoring data analysis. Corresponding enclosure structure deformation analysis models and stress monitoring analysis models are constructed based on the enclosure structure deformation monitoring data and stress monitoring data, respectively. The preset classification is bound to the analysis direction, and the corresponding fifth and sixth sub-item level values are determined by the magnitude of the analysis values.
[0032] For the environmental risk logic judgment rules, the corresponding data basis is surface environmental monitoring data. The analysis directions are divided into structural crack data analysis, environmental risk data analysis, excavation depth data analysis, and hydrogeological condition data analysis. Based on the structural crack data, environmental risk data, excavation depth data, and hydrogeological condition data, corresponding structural crack analysis models, environmental risk analysis models, excavation depth analysis models, and hydrogeological analysis models are constructed respectively. The preset classification is bound to the analysis direction, and the corresponding seventh, eighth, ninth, and tenth sub-item level values are determined by the magnitude of the analysis values.
[0033] Sub-item grade value: refers to the grade value obtained by independently calculating each type of monitoring data according to the analysis and calculation model, reflecting the degree of safety risk corresponding to that type of monitoring data. Each monitoring data has an independent sub-item grade value.
[0034] Comprehensive assessment level: After obtaining the level values of all sub-items, the overall risk level of the open excavation foundation pit project is determined by extracting the level values from all sub-item level values. The comprehensive assessment level represents the highest risk level of the project at present.
[0035] Cumulative settlement monitoring data refers to the settlement deformation data obtained from continuous observation of surface settlement monitoring points during the monitoring period, including the cumulative settlement deformation values of each monitoring point at each time point. It is used to assess the level of surface settlement deformation in the monitoring area and to further assess the impact of the foundation pit excavation on the surrounding surface.
[0036] Risk warning monitoring data: refers to the early warning information data obtained during the construction process through automated monitoring and manual inspection, including monitoring early warning data and inspection early warning data. Among them, monitoring early warning data reflects the early warning status fed back by automated monitoring equipment, and inspection early warning data reflects the abnormal status found by manual inspection.
[0037] Structural deformation monitoring data: refers to the data obtained by monitoring the deformation and stress state of the support structure itself, including the deformation monitoring data and stress monitoring data of the retaining structure. The deformation monitoring data of the retaining structure characterizes the horizontal or vertical displacement of the support structure, and the stress monitoring data characterizes the changes in the internal forces of the support structure components.
[0038] Surface environmental monitoring data refers to data obtained from monitoring the surface and environmental risk factors around the foundation pit, including structural crack data, environmental risk data, excavation depth data, and hydrogeological condition data. Structural crack data records the length of cracks appearing in the retaining structure or on the ground surface; environmental risk data records information on risk sources such as rivers, lakes, pipelines, and construction projects around the project; excavation depth data records the vertical distance from the original ground elevation to the designed elevation of the pit bottom in the open-cut foundation pit project; and hydrogeological condition data records the distribution of groundwater and the properties of the soil and rock strata within the influence area of the open-cut foundation pit project.
[0039] Settlement monitoring matrix: refers to a two-dimensional data array composed of cumulative settlement monitoring data arranged in order of measurement point number and time point. Each element in the settlement monitoring matrix represents the cumulative settlement deformation value of the corresponding measurement point at the corresponding time point. Each row represents the same measurement point and each column represents the same time point.
[0040] Settlement control threshold: refers to the pre-set upper limit of the cumulative settlement deformation value, which serves as the benchmark value for judging whether the settlement deformation exceeds the limit. Different types of engineering parts and different levels of risk control requirements correspond to different settlement control thresholds.
[0041] Settlement Exceedance Identification Matrix: This refers to a binary matrix consisting of 0s and 1s generated by comparing each element in the settlement monitoring matrix with the settlement control threshold one by one. 1 indicates that the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point exceeds the settlement control threshold, and 0 indicates that it does not exceed it.
[0042] The percentage of settlement exceeding the limit: refers to the arithmetic mean obtained by dividing the sum of all elements in the settlement exceeding the limit identification matrix by the total number of elements. It represents the proportion of surface settlement deformation exceeding the limit in the entire monitoring area and is used to assess the significance of the overall settlement trend.
[0043] Monitoring and early warning matrix: refers to a two-dimensional data array composed of monitoring and early warning data in risk early warning monitoring data arranged in order of monitoring point number and date. Each element in the matrix represents the early warning level of the corresponding monitoring point on the corresponding date. Each row represents the same monitoring point and each column represents the same date.
[0044] Daily cumulative warning value: This refers to the value obtained by summing all elements in a column of the monitoring and warning matrix. It reflects the cumulative warning level of all monitoring points on a single day and is used to determine the overall warning intensity on a single day.
[0045] Continuous warning indicator value: refers to the value obtained by multiplying the adjacent elements in each row of the monitoring and warning matrix. It is used to identify the characteristics of a single measuring point triggering warnings for multiple consecutive days. A non-zero product result indicates the existence of a continuous warning state.
[0046] Warning level: refers to the risk warning level classified according to the degree of abnormality of monitoring and warning data. It usually includes red warning and non-red warning levels, with different levels corresponding to different risk severity and response requirements.
[0047] This plan lists the comparison relationships between some safety control requirements and levels for open-cut foundation pit projects in a table format, as follows.
[0048] Table 1 Traditional computer languages employ direct logical search to implement intelligent discrimination methods for safety control requirements as shown in Table 1. However, these methods suffer from high error rates when faced with complex engineering conditions, failing to meet the stringent accuracy requirements of dynamic hierarchical management for open-cut foundation pit projects in rail transit. Therefore, this solution classifies the safety control requirements in Table 1 into four categories: cumulative settlement standards, risk warning standards, structural deformation standards, and surface environment standards. All sub-item level values across these four categories are then aggregated to form a comprehensive evaluation level.
[0049] In this scheme, the multi-dimensional monitoring dataset consists of four subsets: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data. Each subset is obtained through different monitoring methods and data collection approaches. For S1-S4, a detailed description is provided from the perspective of each subset, and the sub-item level values output by each subset are summarized to form a comprehensive evaluation level, thereby completing the risk classification and determination in this scheme. Details are as follows: The data foundation for the cumulative settlement standard is cumulative settlement monitoring data. This data is obtained by deploying settlement monitoring points at key locations around the foundation pit, adjacent buildings, and underground pipelines. Using leveling instruments or total stations, periodic observations are conducted at each monitoring point at fixed time intervals. The observation data from all monitoring points at different time points constitute the cumulative settlement monitoring data. The process of constructing the structured data corresponding to the cumulative settlement monitoring data is as follows: Let m... sij Let m be the monitoring value of the i-th settlement monitoring point at the j-th time point, where i is the monitoring point (here referring to the settlement monitoring point), j is the time point, and m is the time point. sin Let be the cumulative settlement deformation value of the i-th settlement monitoring point, then: ; Among them, the settlement monitoring matrix M S The structured data corresponding to the cumulative settlement monitoring data is constructed by organizing the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
[0050] Based on the analysis and calculation model, the settlement monitoring matrix M is... S The analysis and calculation process is as follows: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix U. MS : ; Among them, T m Settlement control threshold; m sij Settlement monitoring matrix M S The element in, u msij Settlement Exceedance Identification Matrix U MS Elements in the matrix. Settlement exceedance identification matrix U MS The result is a binary matrix consisting of 0 and 1, obtained by comparing each element in the settlement monitoring matrix with the settlement control threshold one by one. 1 indicates that the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point exceeds the settlement control threshold, and 0 indicates that it does not exceed the threshold.
[0051] The settlement exceedance ratio is determined based on the settlement exceedance indicator matrix, and the settlement exceedance ratio k is determined based on the settlement exceedance ratio k. s The first sub-item grade value C is determined by the corresponding analysis and calculation model (i.e., the settlement excess ratio analysis model). S1 The process is as follows: ; Among them, u msin, where represents whether the i-th settlement monitoring point exceeds the settlement control threshold, is indicated by 0 or 1; 1, 2, and 3 represent the grading level corresponding to whether the settlement exceedance ratio in the preset grading rules exceeds the threshold (the meanings of 1, 2, and 3 in the subsequent analysis and calculation model are similar, all being the corresponding grading levels, and will not be elaborated further); k s k represents the percentage of settlement exceeding the limit. s >50% corresponds to Level 1, 10% <k s ≤50% corresponds to level 2 and k s ≤10% corresponds to Level 3.
[0052] The cumulative average settlement deformation is obtained by analyzing the settlement monitoring matrix M. S The average value is obtained by calculating the cumulative settlement deformation average value a. sm The corresponding analytical calculation model (i.e., cumulative settlement deformation analysis model) determines the second sub-item grade value C. S2 The process is as follows: ; Among them, A m To monitor and control early warning thresholds, a sm >T m Corresponding to Level 1, 10%≤a sm ≤T m Corresponding to level 2 and a sm <T m Corresponding to level 3.
[0053] The data foundation for risk warning standards is risk warning monitoring data. This data comprises two parts: monitoring and warning data, and inspection and warning data. Monitoring and warning data is collected in real-time through an automated sensor network, with each monitoring point generating a warning level code daily. Inspection and warning data is generated through regular manual inspections of the area surrounding the foundation pit, with each inspected area generating a daily inspection and warning level code. Both monitoring and warning data together constitute the risk warning monitoring data. The process of constructing the monitoring and warning data into structured data is as follows: ; Among them, monitoring and early warning matrix A C The structured data corresponding to the monitoring and early warning data is represented by i, where i is the monitoring point (referring to the monitoring and early warning monitoring point), j is the time point (referring to the date), and a... cij This refers to the monitoring value of the i-th measuring point at the j-th time point (here referring to the warning level code).
[0054] The process of determining the daily cumulative warning value based on the sum of the column elements of the monitoring and early warning matrix is as follows: The sum of S elements in the monitoring and early warning matrix ac Represented as: ; The sum of each column of the monitoring and early warning matrix is: ; Among them, s aclj This represents the cumulative value of the daily warning.
[0055] The process of determining the continuous early warning identifier value based on the product of adjacent row elements of the monitoring and early warning matrix is as follows: The product M of the two adjacent elements in each row of the monitoring and early warning matrix aco Represented as: ; The product M of the three adjacent elements in each row of the monitoring and early warning matrix acr Represented as: ; Among them, M aco and M acr M is the continuous warning indicator value; a non-zero value indicates the presence of a continuous warning status. aco For two consecutive days of warning indication value, M acr This is the warning indicator value for three consecutive days.
[0056] Based on the daily cumulative warning value S ac Continuous warning indicator value M aco and M acr The corresponding analytical calculation model (i.e., the monitoring and early warning analysis model) determines the third sub-item level value C of the risk early warning monitoring data. a1 The process is as follows: .
[0057] The process of converting patrol and early warning data into structured data corresponding to patrol and early warning data is as follows: ; Among them, the patrol early warning vector A X This refers to the structured data corresponding to the patrol and early warning data, where j represents a time point (here referring to the date), and a... xj This refers to the monitoring quantity at the j-th time point (here, it refers to the patrol early warning level code).
[0058] Inspection early warning vector A X The sum of the last 7 columns determines the cumulative amount of the most recent patrol warning, S. ax The formula is as follows: .
[0059] According to the recent cumulative patrol and early warning volume S axThe corresponding analytical calculation model (i.e., the inspection and early warning analysis model) determines the fourth sub-item level value C of the inspection and early warning data in the risk early warning monitoring data. a2 The process is as follows: .
[0060] For structural deformation standards, the corresponding data basis is structural deformation monitoring data. This data comprises two parts: enclosure structure deformation monitoring data and stress monitoring data. Enclosure structure deformation monitoring data is acquired by deploying displacement monitoring points on the support structure using inclinometers or displacement sensors, denoted as m. gbi Let m be the cumulative deformation at the i-th measuring point (referring to the deformation monitoring of the retaining structure). Stress monitoring data is acquired by installing stress sensors on the supporting structure components. gli This represents the stress monitoring value at the i-th measuring point (here referring to the deformation monitoring of the building envelope). The deformation monitoring data of the building envelope and the stress monitoring data together constitute the structural deformation monitoring data. The process of constructing the structured data corresponding to the structural deformation monitoring data is as follows: Among them, the deformation vector M of the enclosure structure gb The structured data corresponding to the deformation monitoring data of the building envelope, stress monitoring vector M gl This is the structured data corresponding to the stress monitoring data; Based on the deformation vector M of the enclosure structure gb The fifth sub-item grade value C is determined based on the corresponding analysis and calculation model (i.e., the deformation analysis model of the building envelope). j1 The process is as follows: ; Among them, T gb This is the deformation threshold of the enclosure structure.
[0061] Based on stress monitoring vector M gl The corresponding analytical calculation model (i.e., stress monitoring analysis model) determines the sixth sub-item level value C. j2 The process is as follows: ; Among them, T gl This is the threshold for stress monitoring and control.
[0062] For surface environmental standards, the corresponding data basis is surface environmental monitoring data. Surface environmental monitoring data includes four parts: structural crack data, environmental risk data, excavation depth data, and hydrogeological condition data. Structural crack data is obtained by measuring the length of cracks on the surface of the retaining structure and the ground through manual on-site measurement or image recognition technology; environmental risk data is obtained comprehensively through engineering survey data collection, on-site investigation, and geographic information system queries, including information such as whether there are rivers or lakes in the vicinity of the project and whether there are no anti-seepage measures at the bottom of the rivers or lakes, whether there are large-diameter water-bearing and pressurized pipelines nearby, whether there are ongoing construction projects in the earthwork excavation stage nearby, and whether it crosses water-bearing sand layers, silty soil, or deep mixed fill geological strata with poor geology; excavation depth data is determined by engineering survey measurement and elevation information from design drawings; hydrogeological condition data is obtained through comprehensive analysis of borehole sampling from previous geological surveys, on-site hydrological tests, and indoor geotechnical tests.
[0063] Compared to cumulative settlement monitoring data, risk warning monitoring data, and structural deformation monitoring data, surface environment monitoring data, in terms of constructing structured data, directly treats the collected surface environment monitoring data as structured data for hierarchical processing. Specifically, structural crack data is used as its structured data, and the seventh sub-item level value C is determined based on its corresponding analysis and calculation model (i.e., the structural crack analysis model). j3 The process is as follows: ; Among them, L f This represents the length of the structural crack.
[0064] Using environmental risk data as its structured data, and its corresponding analytical calculation model (i.e., environmental risk analysis model), the eighth sub-item level value C is determined. e The process is as follows: ; Among them, L f This represents the length of the structural crack.
[0065] Using the excavation depth data as its structured data, and its corresponding analytical calculation model (i.e., the excavation depth analysis model), the ninth sub-item level value C is determined. h The process is as follows: ; Among them, H w This refers to the excavation depth.
[0066] Hydrogeological condition data is used as its structured data, and its corresponding analytical calculation model (i.e., hydrogeological analysis model) is used to determine the tenth item level value C. g The process is as follows: .
[0067] By quantifying and classifying the cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data, the complex semantics contained in Table 1 can be converted into ten sub-item level values. The comprehensive evaluation level of the open-cut foundation pit project is determined based on all sub-item level values, specifically: ; Here, C represents the overall assessment level. The minimum value of all sub-levels is used because the level setting is based on severity, with the lowest level corresponding to the most severe risk level. The appropriate algorithm can be set according to actual needs.
[0068] The beneficial effects are as follows: By transforming qualitative discrimination criteria into quantitative data matrix representations, an analytical computational model capable of directly processing complex semantics is constructed, laying the foundation for subsequent accurate calculations. A dynamic hierarchical mathematical model is built through matrix element operations, transforming the hierarchical judgment process into a numerical calculation process, avoiding discrimination errors caused by the complexity of criteria in traditional logical search methods. By transforming the mathematical model into computer program modules, the automation and intelligence of hierarchical discrimination are realized, significantly improving discrimination efficiency and response speed.
[0069] Example 1: Identification of Level 1 Risk Engineering.
[0070] A 28-meter-deep open-cut foundation pit project for a rail transit line traverses a thick, water-rich sand layer with no dewatering capability. It is adjacent to a river and lake without any seepage prevention measures at the bottom. Cumulative settlement monitoring data obtained during the monitoring period showed that 19 out of 30 monitoring points exceeded the settlement control threshold, resulting in an exceedance rate of 63.3%. The monitoring and early warning matrix showed that 8 monitoring points triggered red alerts within a single day, with 3 of these points triggering red alerts for three consecutive days. The inspection and early warning matrix showed 3 yellow inspection alerts and 1 orange inspection alert within a single week. In the retaining structure deformation monitoring vector, 3 monitoring points showed cumulative deformation exceeding 1.5 times the retaining structure deformation monitoring control threshold. In the stress monitoring vector, 2 monitoring points showed stress levels reaching 1.5 times the structural stress monitoring control threshold. Surface environment monitoring data recorded a 2.5-meter crack in the retaining structure. Based on the above sub-item grade calculations, the maximum value among all sub-item grade values was taken, and the final comprehensive evaluation grade was determined to be Level 1.
[0071] Example 2: Class II Risk Engineering Identification.
[0072] A 20-meter-deep open-cut foundation pit project for a rail transit line traverses a water-bearing sand layer and is adjacent to a large-diameter, pressurized, and water-bearing pipeline. Cumulative settlement monitoring data obtained during the monitoring period showed that 3 out of 25 monitoring points exceeded the settlement control threshold, with an excess settlement rate of 12%. The monitoring and early warning matrix showed that 2 monitoring points triggered red alerts within a single day, with no consecutive 3-day red alerts. The inspection and early warning matrix showed 4 yellow inspection alerts within a single week, with no orange inspection alerts. In the retaining structure deformation monitoring vector, 2 monitoring points showed cumulative deformation exceeding 85% of the retaining structure deformation monitoring control threshold but not reaching the control threshold. In the stress monitoring vector, 1 monitoring point showed stress exceeding the structural stress monitoring control threshold but not reaching 1.5 times. Surface environment monitoring data recorded a 0.8-meter crack in the retaining structure. Based on the above sub-item grade calculations, the maximum value among all sub-item grade values was taken, and the final comprehensive evaluation grade was determined to be Level II.
[0073] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, and these situations are also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.
[0074] This invention also provides a risk classification and identification system for open-cut foundation pit engineering in rail transit, the specific technical solution of which is as follows: a data acquisition module, a data organization module, a classification calculation module, and a comprehensive evaluation module; The data acquisition module is used to acquire multi-dimensional monitoring datasets during the construction process of open-cut foundation pit projects; The data organization module is used to determine the structured data corresponding to various types of monitoring data based on the multi-dimensional monitoring dataset; The hierarchical calculation module is used to establish an analysis and calculation model based on preset hierarchical rules, and to analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model to obtain the sub-item level values corresponding to various types of monitoring data. The comprehensive evaluation module is used to determine the comprehensive evaluation level of open-cut foundation pit projects based on all sub-item level values.
[0075] Based on the above solution, the present invention can be further improved as follows.
[0076] Furthermore, the multi-dimensional monitoring dataset includes at least: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data.
[0077] Furthermore, based on the analytical calculation model, various types of monitoring data in the structured data are analyzed and calculated, including: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix; The settlement excess ratio is determined based on the settlement excess indicator matrix; The sub-level values of the cumulative settlement monitoring data are determined based on the proportion of settlement exceeding the limit and the analysis and calculation model. The settlement monitoring matrix is the structured data corresponding to the cumulative settlement monitoring data. It is constructed by constructing the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
[0078] Furthermore, based on the analytical calculation model, various types of monitoring data in the structured data are analyzed and calculated, including: The daily cumulative warning value is determined by the sum of the column elements of the monitoring and early warning matrix, and the continuous warning indicator value is determined by the product of the adjacent row elements of the monitoring and early warning matrix. The sub-level values of the monitoring and early warning data in the risk early warning monitoring data are determined based on the daily cumulative early warning value, the continuous early warning indicator value, and the analysis and calculation model. The monitoring and early warning matrix is the structured data corresponding to the monitoring and early warning data in the risk early warning monitoring data. It is constructed by the monitoring and early warning data according to the monitoring points and dates. The matrix elements in the monitoring and early warning matrix represent the early warning level of the corresponding monitoring point on the corresponding date.
[0079] It should be noted that the beneficial effects of the risk classification and identification system for open-cut foundation pit engineering in rail transit provided in the above embodiments are the same as the beneficial effects of the risk classification and identification method for open-cut foundation pit engineering in rail transit provided in the above embodiments, and will not be repeated here. Furthermore, when implementing the functions of the system provided in the above embodiments, the division of the above functional modules is only used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.
[0080] like Figure 2 As shown, an embodiment of the present invention provides a computer device 300, which includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the computer device 300 to implement any of the above-described methods. Specifically: The computer device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The one or more memories 310 store at least one computer program 330, which is loaded and executed by the one or more processors 320 to enable the computer device 300 to implement the risk classification and determination method for open-cut foundation pit engineering in rail transit provided in the above embodiment. Of course, the computer device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device 300 may also include other components for implementing device functions, which will not be elaborated here.
[0081] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-described methods.
[0082] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.
[0083] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the aforementioned risk classification and determination methods applied to open-cut foundation pit engineering for rail transit.
[0084] It should be noted that the terms "first," "second," etc., used in the specification of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown in the figures or description.
[0085] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.
[0086] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.
[0087] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A risk classification and identification method applied to open-cut foundation pit engineering for rail transit, characterized in that, include: S1, Obtain multi-dimensional monitoring datasets during the construction process of open-cut foundation pit engineering; S2, determine the structured data corresponding to each type of monitoring data based on the multi-dimensional monitoring dataset; S3, establish an analysis and calculation model based on preset hierarchical rules, and analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model to obtain the sub-item level values corresponding to various types of monitoring data; S4. Determine the comprehensive evaluation level of the open-cut foundation pit project based on all sub-item level values.
2. The risk classification and identification method for open-cut foundation pit engineering in rail transit according to claim 1, characterized in that, The multi-dimensional monitoring dataset includes at least: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data.
3. The risk classification and identification method for open-cut foundation pit engineering in rail transit according to claim 2, characterized in that, The step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix; The settlement excess ratio is determined based on the aforementioned settlement excess identification matrix; The sub-level values of the cumulative settlement monitoring data are determined based on the settlement excess ratio and the analysis and calculation model. The settlement monitoring matrix is the structured data corresponding to the cumulative settlement monitoring data. It is constructed by the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
4. The risk classification and identification method for open-cut foundation pit engineering in rail transit according to claim 2, characterized in that, The step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: The daily cumulative warning value is determined by the sum of the column elements of the monitoring and warning matrix, and the continuous warning indicator value is determined by the product of the adjacent row elements of the monitoring and warning matrix. The sub-item level values of the monitoring and early warning data in the risk early warning monitoring data are determined based on the daily cumulative early warning value, the continuous early warning identification value, and the analysis and calculation model. The monitoring and early warning matrix is the structured data corresponding to the monitoring and early warning data in the risk early warning monitoring data. It is constructed by the monitoring and early warning data according to the monitoring points and dates. The matrix elements in the monitoring and early warning matrix represent the early warning level of the corresponding monitoring point on the corresponding date.
5. A risk classification and identification system for open-cut foundation pit engineering in rail transit, characterized in that, include: The system includes a data acquisition module, a data organization module, a hierarchical calculation module, and a comprehensive evaluation module. The data acquisition module is used to acquire multi-dimensional monitoring datasets during the construction process of open-cut foundation pit engineering; The data organization module is used to determine the structured data corresponding to various types of monitoring data based on the multi-dimensional monitoring dataset; The hierarchical calculation module is used to establish an analysis and calculation model based on preset hierarchical rules, and to analyze and calculate various types of monitoring data in the structured data according to the analysis and calculation model to obtain the sub-item level values corresponding to various types of monitoring data. The comprehensive evaluation module is used to determine the comprehensive evaluation level of the open-cut foundation pit project based on all sub-item level values.
6. A risk classification and identification system for open-cut foundation pit engineering in rail transit according to claim 5, characterized in that, The multi-dimensional monitoring dataset includes at least: cumulative settlement monitoring data, risk warning monitoring data, structural deformation monitoring data, and surface environment monitoring data.
7. A risk classification and identification system for open-cut foundation pit engineering in rail transit according to claim 6, characterized in that, The step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: Each element in the settlement monitoring matrix is compared with the settlement control threshold to obtain the settlement exceedance identification matrix; The settlement excess ratio is determined based on the aforementioned settlement excess identification matrix; The sub-level values of the cumulative settlement monitoring data are determined based on the settlement excess ratio and the analysis and calculation model. The settlement monitoring matrix is the structured data corresponding to the cumulative settlement monitoring data. It is constructed by the cumulative settlement monitoring data according to the measuring points and time points. The matrix elements in the settlement monitoring matrix represent the cumulative settlement deformation value of the corresponding measuring point at the corresponding time point.
8. A risk classification and identification system for open-cut foundation pit engineering in rail transit according to claim 6, characterized in that, The step of analyzing and calculating various types of monitoring data in the structured data according to the analytical calculation model includes: The daily cumulative warning value is determined by the sum of the column elements of the monitoring and warning matrix, and the continuous warning indicator value is determined by the product of the adjacent row elements of the monitoring and warning matrix. The sub-item level values of the monitoring and early warning data in the risk early warning monitoring data are determined based on the daily cumulative early warning value, the continuous early warning identification value, and the analysis and calculation model. The monitoring and early warning matrix is the structured data corresponding to the monitoring and early warning data in the risk early warning monitoring data. It is constructed by the monitoring and early warning data according to the monitoring points and dates. The matrix elements in the monitoring and early warning matrix represent the early warning level of the corresponding monitoring point on the corresponding date.
9. A computer device, characterized in that, The computer device includes a processor coupled to a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the computer device to implement a risk classification and identification method for open-cut foundation pit engineering in rail transit as described in any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement a risk classification and identification method for open-cut foundation pit engineering in rail transit as described in any one of claims 1 to 4.