Ultra-deep soft soil foundation pit safety evaluation method and system

Abstract

The invention relates to the technical field of safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety assessment, and in particular to a safety assessment method and system for an ultra-deep soft soil foundation pit. Background Art

[0002] The field of safety assessment technology mainly involves the systematic analysis and evaluation of safety risks in technical facilities, engineering construction or production processes, as well as the impact of risks on personnel, equipment and the environment. The core task is to identify potential risk sources, predict the probability of accidents, assess the severity of accident consequences, and propose corresponding risk control and mitigation measures. Safety assessment methods include qualitative and quantitative analysis techniques, such as fault tree analysis (FTA), accident tree analysis (ETA) and risk matrix. Help decision makers formulate scientific and reasonable safety management strategies to ensure the safe operation of technology and engineering projects.

[0003] Among them, the ultra-deep soft soil foundation pit safety evaluation method refers to a technical means of systematically evaluating the stability and safety of a specific type of engineering project - ultra-deep soft soil foundation pit. The main purpose is to scientifically analyze the safety issues encountered during the design, construction and operation of the foundation pit, predict potential risk points, take effective preventive measures, and ensure the safety of construction personnel and the stability of the structure. By applying this method, the risk of accidents in foundation pit projects can be significantly reduced, and the engineering design and construction plan can be optimized.

[0004] Existing safety assessment technologies have shown certain limitations when dealing with complex and dynamically changing engineering conditions, especially in ultra-deep soft soil foundation pit projects. Existing methods focus on using static data and preset models for risk assessment, and to a certain extent ignore the impact of real-time changes on safety. For example, although fault tree analysis (FTA) and accident tree analysis (ETA) provide a framework for risk identification, they are not flexible and timely enough when dealing with continuous data streams and immediate responses. Traditional methods require a lot of manual input and expert analysis, resulting in insufficient response in a rapidly changing construction environment and missed opportunities to implement key preventive measures. Therefore, in actual operations, the ability to predict and mitigate emergencies is limited, especially in situations where rapid decision-making is required, and the most effective safety guarantee cannot be provided. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a safety evaluation method and system for an ultra-deep soft soil foundation pit.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for safety assessment of ultra-deep soft soil foundation pit, comprising the following steps:

[0007] S1: Obtain geological data of ultra-deep soft soil foundation pit, record soil type, soil density, groundwater level and temperature data, sort and format the data, and generate a geological data record table;

[0008] S2: Based on the geological data record table, record the impact data of soil temperature changes on moisture, density and compressibility, calculate the changes in physical properties of soft soil under differentiated temperature conditions, and construct a thermodynamic impact model;

[0009] S3: According to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, the mechanical behavior of the foundation pit structure affected by temperature changes is analyzed, the stability of the structure under differentiated environmental conditions is calculated, and the mechanical stability analysis results are generated;

[0010] S4: Using the mechanical stability analysis results, summarizing historical accident records and real-time monitoring data, assessing construction risks, calculating the impact of differentiated risk factors, and classifying and prioritizing risks to generate construction risk assessment results;

[0011] S5: using the construction risk assessment results, analyzing the dependencies between the differentiated risk factors, and deriving the relationships between the risk factors to generate a risk dependency model;

[0012] S6: Based on the risk dependency model, set real-time monitoring standards and data collection frequency, design monitoring plans and establish emergency response mechanisms, and generate safety assessment protocols.

[0013] As a further solution of the present invention, the geological data record table includes classified soil types, measured soil density, recorded groundwater level and real-time updated temperature data; the thermodynamic influence model includes soil heat capacity calculation, water evaporation rate estimation and soil compressibility response analysis; the mechanical stability analysis results include compressive strength values, deformation characteristics description and stability safety boundaries of foundation pit structures; the construction risk assessment results include risk factor grading, accident frequency statistics and quantification of risk factor impact; the risk dependency relationship model includes risk factor correlation analysis, dependency path diagram and key risk node identification; the safety assessment protocol includes monitoring frequency setting, monitoring standard updating and emergency response process.

[0014] As a further solution of the present invention, the steps of obtaining geological data of ultra-deep soft soil foundation pit, recording soil type, soil density, groundwater level and temperature data, sorting and formatting the data, and generating a geological data record table are specifically as follows:

[0015] S101: Arrange soil type, soil density, groundwater level and temperature measurement equipment in the ultra-deep soft soil foundation pit, install, calibrate and perform functional tests on the equipment, and record the equipment configuration and initial status to obtain original geological data;

[0016] S102: regularly reading soil type, density, groundwater level and temperature data from a differential measurement device according to the original geological data, classifying and processing the data, deleting invalid and erroneous data, and obtaining formatted geological data;

[0017] S103: Based on the formatted geological data, data is imported according to a set data table structure, data content is sorted, and column width and format are adjusted to generate a geological data record table.

[0018] As a further solution of the present invention, based on the geological data record table, the influence data of soil temperature change on moisture, density and compressibility are recorded, and the physical property changes of soft soil under differentiated temperature conditions are calculated. The steps of constructing a thermodynamic influence model are specifically as follows:

[0019] S201: extracting soil temperature change data from the geological data record table, correspondingly recording soil moisture, density and compressibility data, performing multiple measurements under gradual temperature changes, and obtaining a temperature impact data set;

[0020] S202: Based on the temperature impact data set, comparative analysis is performed on the moisture, density and compressibility of soft soil under differentiated temperature conditions, and changes of each property under differentiated temperature are evaluated item by item to obtain physical property change data;

[0021] S203: Using the physical property change data, simulating the change of the physical properties of soft soil under differentiated temperature conditions, and defining model parameters to generate a thermodynamic influence model.

[0022] As a further solution of the present invention, according to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, the mechanical behavior of the foundation pit structure is analyzed by analyzing the effect of temperature change on the structure, and the stability of the structure under differentiated environmental conditions is calculated. The specific steps of generating the mechanical stability analysis result are as follows:

[0023] S301: inputting the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit into the thermodynamic influence model, and combining them with the temperature change data, identifying the influence of the temperature change on the ultra-deep soft soil foundation pit, and obtaining an input data set;

[0024] S302: Analyze the input data set using a thermodynamic influence model, evaluate the influence of temperature change on the foundation pit structure by simulating the mechanical behavior of the foundation pit structure under differentiated temperature conditions, and obtain mechanical behavior analysis data;

[0025] S303: Based on the mechanical behavior analysis data, the stability of the foundation pit structure under differentiated environmental conditions is calculated, and the stability of the ultra-deep soft soil foundation pit is quantified, and weight distribution is performed to obtain a mechanical stability analysis result.

[0026] As a further solution of the present invention, the mechanical stability analysis results are used to summarize historical accident records and real-time monitoring data, evaluate construction risks, calculate the impact of differentiated risk factors, and classify and prioritize risks. The specific steps for generating construction risk assessment results are as follows:

[0027] S401: using the mechanical stability analysis results, summarizing historical accident records and real-time monitoring data, extracting accident records associated with ultra-deep soft soil foundation pits, and collecting real-time construction environment data to obtain a summary data set;

[0028] S402: Based on the summary data set, the construction risk is evaluated, the influence degree of the differentiated risk factors in the construction environment is analyzed, and the potential influence of the differentiated risk factors on the construction is calculated, and the risks are classified to obtain the construction risk data;

[0029] S403: Using the construction risk data, adopting the hierarchical analysis method, prioritize risk factors, divide risk categories according to risk levels, check construction areas with priority risk levels and give priority to them, and obtain construction risk assessment results.

[0030] As a further solution of the present invention, the formula of the hierarchical analysis method is as follows:

[0031]

[0032] Among them, w i is the weight of risk factor i, n j is the total number of risk factors, r ij is the risk adjustment coefficient of risk factor i to risk factor j, m ij is the environmental impact coefficient of risk factor i on risk factor j, w j is the weight of risk factor j, r kj is the risk adjustment coefficient of risk factor k for risk factor j, m kj is the environmental impact coefficient of risk factor k on risk factor j, w k is the weight of risk factor k, a ij Score the relative importance of risk factor i to risk factor j.

[0033] As a further solution of the present invention, the construction risk assessment results are used to analyze the dependencies between the differentiated risk factors, and the relationships between the risk factors are derived. The steps of generating a risk dependency model are specifically as follows:

[0034] S501: extracting risk factor data from the construction risk assessment results, analyzing the association between risk factors, identifying the dependency relationship between differentiated risk factors, and obtaining dependency relationship data;

[0035] S502: Based on the dependency relationship data, perform relationship derivation, analyze the mutual influence between risk factors, verify the correlation and influence degree between risk factors, and obtain relationship derivation data;

[0036] S503: Derive data through the relationship, illustrate the relationship between differentiated risk factors, use nodes to represent risk factors, edges to represent the relationship between risk factors, define the strength of the dependency, and generate a risk dependency model.

[0037] As a further solution of the present invention, based on the risk dependency model, real-time monitoring standards and data collection frequency are set, monitoring plans are designed, and emergency response mechanisms are established. The steps of generating a safety assessment protocol are specifically as follows:

[0038] S601: According to the risk dependency model, set the real-time monitoring standard and data collection frequency, determine the real-time monitoring standard of key risk factors, and set the frequency and time interval of data collection to obtain monitoring standard and frequency records;

[0039] S602: Based on the monitoring standards and frequency records, design a plan for the ultra-deep soft soil foundation pit, including data collection, monitoring equipment configuration and data transmission process, to obtain a monitoring plan;

[0040] S603: Through the monitoring plan, establish an emergency response mechanism, formulate emergency response processes and measures, set emergency handling schedules, responsibility allocation and response time, and generate a safety assessment protocol.

[0041] A safety assessment system for an ultra-deep soft soil foundation pit, the ultra-deep soft soil foundation pit safety assessment system is used to execute the ultra-deep soft soil foundation pit safety assessment method, the system comprising:

[0042] The data collection module collects soil type, soil density, groundwater level and temperature data in ultra-deep soft soil foundation pits, organizes the data and checks the integrity of the data to generate a geological data record table;

[0043] The model building module records the influence of soil temperature change on moisture, density and compressibility based on the geological data record table, calculates the change of soft soil physical properties under differentiated temperature conditions, and builds a thermodynamic influence model;

[0044] The mechanical behavior prediction module analyzes the mechanical behavior of the foundation pit structure affected by temperature changes according to the thermodynamic influence model and the compressive strength and deformation characteristics of the foundation pit, and generates mechanical stability analysis results;

[0045] The risk classification module uses the mechanical stability analysis results to evaluate construction risks, quantify the impact of differentiated risk factors, and classify risk levels to generate construction risk assessment results;

[0046] The monitoring and emergency response module identifies the correlation between risk factors based on the construction risk assessment results, formulates real-time monitoring standards and data collection frequencies, and generates a safety assessment protocol through an emergency response mechanism.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, the accuracy and response speed of safety assessment are optimized through specific data recording, dynamic model construction and real-time monitoring strategies. By acquiring and organizing detailed geological data of ultra-deep soft soil foundation pits, the accuracy and applicability of basic data are enhanced, so that subsequent analysis can be carried out more based on clear actual conditions. A thermodynamic influence model is constructed and physical property changes are analyzed to provide a quantitative method to predict and adjust engineering design. By directly linking the prediction of mechanical behavior with risk assessment, the timeliness and accuracy of risk prediction are ensured, and through the comprehensive use of real-time monitoring data, the risk assessment model can be dynamically adjusted to improve the effectiveness and accuracy of early warning. Integrating risk dependency models and monitoring plans, effectively linking theory with actual operations, making emergency responses more targeted and immediate, effectively improving the systematicness and adaptability of construction safety management, and significantly improving the ability to prevent accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In the description of the present invention, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for safety evaluation of ultra-deep soft soil foundation pit, comprising the following steps:

[0060] S1: Obtain geological data of ultra-deep soft soil foundation pit, record soil type, soil density, groundwater level and temperature data, sort and format data, check data integrity, and generate geological data record table;

[0061] S2: Based on the geological data record table, record the impact of soil temperature changes on moisture, density and compressibility, calculate the changes in the physical properties of soft soil under differentiated temperature conditions, and construct a thermodynamic impact model;

[0062] S3: According to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, and the mechanical behavior of the foundation pit structure affected by temperature changes is analyzed to calculate the stability of the structure under differentiated environmental conditions and generate mechanical stability analysis results;

[0063] S4: Use the results of mechanical stability analysis, summarize historical accident records and real-time monitoring data, evaluate construction risks, calculate the impact of differentiated risk factors, and classify and prioritize risks to generate construction risk assessment results;

[0064] S5: Using the construction risk assessment results, analyze the dependencies between differentiated risk factors, derive the relationships between risk factors, and generate a risk dependency model;

[0065] S6: Based on the risk dependency model, set the real-time monitoring standards and the frequency of data collection for ultra-deep soft soil foundation pits, design a monitoring plan and establish an emergency response mechanism, and generate a safety assessment protocol.

[0066] The geological data record table includes classified soil types, measured soil density, recorded groundwater level and real-time updated temperature data. The thermodynamic influence model includes soil heat capacity calculation, water evaporation rate estimation and soil compressibility response analysis. The mechanical stability analysis results include compressive strength values, deformation characteristics description and stability safety boundaries of foundation pit structures. The construction risk assessment results include risk factor level classification, accident frequency statistics and quantification of risk factor impact. The risk dependency relationship model includes risk factor correlation analysis, dependency path diagram and key risk node identification. The safety assessment protocol includes monitoring frequency setting, monitoring standard update and emergency response process.

[0067] See also Figure 2 , obtain the geological data of the ultra-deep soft soil foundation pit, record the soil type, soil density, groundwater level and temperature data, sort and format the data, and generate the geological data record table in the following steps:

[0068] S101: Arrange soil type, soil density, groundwater level and temperature measurement equipment in the ultra-deep soft soil foundation pit, install, calibrate and perform functional tests on the equipment, and record the equipment configuration and initial status to obtain the original geological data. The execution process is as follows;

[0069] Various types of measuring equipment are arranged in the ultra-deep soft soil foundation pit. The equipment is mainly used to determine soil type, soil density, groundwater level and temperature. The appropriate location and depth are selected for equipment installation to ensure that each device can accurately measure key geological data. After installation, strict calibration and functional testing are carried out to ensure the accuracy and stability of each device, which is crucial to the reliability of subsequent data. The configuration and initial status of the equipment are recorded in detail to facilitate subsequent data monitoring and maintenance work, and the original geological data is obtained using the formula:

[0070]

[0071] Among them, A geo represents the original geological data, T a,i represents the temperature data of the ath measurement point, exp is the natural exponential function, D a,i represents soil density, W a,i Indicates the water level measurement value, H a,i Indicates the depth of the measuring point, H ref is the reference depth, n a Represents the total number of measurement points.

[0072] S102: Based on the original geological data, soil type, density, groundwater level and temperature data are regularly read from the differential measurement equipment, the data are classified and processed, invalid and erroneous data are deleted, and the execution process of obtaining formatted geological data is as follows;

[0073] Data is read regularly from various measuring devices, including soil type, density, groundwater level and temperature. Data is automatically collected by different sensors and measuring tools at fixed time intervals and transmitted to the central data processing equipment. The raw data is classified and processed, including verifying the integrity and accuracy of the data and deleting invalid and erroneous data records. Data quality can be ensured through data processing. Only through accurate data processing can reliable information be provided for subsequent analysis and decision-making. After screening and processing, formatted geological data is obtained. The formula used is:

[0074]

[0075] Among them, B geo Represents formatted geological data, D b,i represents the density value of the bth data point, S b,i represents the score of soil type, σ(D b,i ) is the standard deviation of the density values, ∈ is a decimal to prevent division by zero, and n b Represents the total number of data points.

[0076] S103: Based on the formatted geological data, data is imported according to the set data table structure, data content is sorted, and column width and format are adjusted to generate a geological data record table. The execution process is as follows;

[0077] Import the formatted geological data into a specific data table structure for further data sorting and format adjustment. According to the preset database table structure, fill the data into the corresponding columns accurately, including adjusting the column width, formatting the data display style, and ensuring the consistency of data types, such as unifying the format of numbers or dates. Logical verification of the data will also be performed, such as checking the data range and relevance to ensure the accuracy and logic of all records. After completing the work, a geological data record table is generated using the formula:

[0078]

[0079] Among them, D geos Represents geological data record table, L c,i Represents the column width adjustment of the bth data point, R c,i Indicates the record completeness of the data point, R opt is the optimal record completeness standard, n b Represents the total number of data points.

[0080] See also Figure 3 Based on the geological data record table, the influence data of soil temperature change on moisture, density and compressibility are recorded, and the changes in the physical properties of soft soil under differentiated temperature conditions are calculated. The specific steps of constructing the thermodynamic influence model are as follows:

[0081] S201: Extract soil temperature change data from the geological data record table, record soil moisture, density and compressibility data accordingly, perform multiple measurements under gradual temperature changes, and obtain the temperature impact data set. The execution process is as follows;

[0082] Detailed data on soil temperature variations are extracted from geological data logs, along with related properties such as soil moisture, density and compressibility. By taking multiple measurements at different temperatures, it is possible to observe and record how temperature affects soil properties. The process involves setting up a precise sequence of temperature variations and performing detailed data collection at each set point to obtain a temperature effect data set using the formula:

[0083]

[0084] Among them, D temp represents the temperature effect data set, T d,i represents the soil temperature at the ath measuring point, H d,i represents the moisture content at the measuring point, σ(T d,i ) is the standard deviation of the temperature data, ∈ is a decimal to prevent division by zero, P d,i is the soil density, k a is the attenuation coefficient, n a Represents the total number of measurement points.

[0085] S202: Based on the temperature impact data set, the moisture, density and compressibility of soft soil under differentiated temperature conditions are compared and analyzed, and the change of each property under differentiated temperature is evaluated item by item. The execution process of obtaining the physical property change data is as follows;

[0086] Based on the temperature effect data set, the changes in the physical properties of soft soils such as moisture, density, and compressibility under different temperature conditions are analyzed and evaluated. The analysis identifies the change pattern of each property by comparing the data measured at different temperatures. Through comparison, we can deeply understand how temperature changes affect the physical and chemical properties of soft soils, which is of great significance for engineering applications and environmental management. Understanding the changes in properties can help predict soil behavior in construction and civil engineering, optimize structural design, ensure safety and durability, and obtain physical property change data. The formula used is:

[0087]

[0088] Among them, D physRepresents the physical property change data, C f,i represents the compressibility data of the ath measurement point, S f,i represents the score of soil density, abs is the function of finding the absolute value, V f,i is the volume change at the measuring point, V ref is the reference volume, n a Represents the total number of measurement points.

[0089] S203: Using the physical property change data, simulating the change of the physical properties of soft soil under differentiated temperature conditions, and defining model parameters, the execution process of generating a thermodynamic influence model is as follows;

[0090] Using the data of physical property changes, the physical property changes of soft soil under differentiated temperature conditions are simulated, and the model parameters used to describe the changes are further defined. Through simulation, a thermodynamic impact model can be constructed, which can predict the behavior of soil properties under different environmental conditions. The model is crucial to understanding the soil response under global climate change, and also provides a valuable tool for soil management and agricultural applications. It can help decision makers develop more effective strategies to cope with future environmental challenges and generate a thermodynamic impact model using the formula:

[0091]

[0092] Among them, D thermo represents the thermodynamic influence model, M g,i represents the g-th model parameter, max is the function for finding the maximum value, K g,i is the correlation coefficient, R g,i Represents the value of the response variable, n g Represents the total number of parameters.

[0093] See also Figure 4 According to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, and the mechanical behavior of the foundation pit structure affected by temperature changes is analyzed to calculate the stability of the structure under differentiated environmental conditions. The specific steps for generating the mechanical stability analysis results are as follows:

[0094] S301: Input the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit into the thermodynamic influence model, and combine them with the temperature change data to identify the influence of temperature change on the ultra-deep soft soil foundation pit. The execution process of obtaining the input data set is as follows;

[0095] The compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input into the thermodynamic influence model as key input parameters, and the physical and mechanical property data of the foundation pit are collected and sorted, including compressive strength, elastic modulus and deformation related parameters. The data is combined with the temperature change data to analyze and predict the impact of temperature fluctuations on the mechanical properties of the foundation pit soil. Through an accurate data model, the behavior changes of the foundation pit soil under different temperature conditions can be identified, which is crucial for engineering design and construction safety. The input data set is generated using the formula:

[0096]

[0097] Among them, D input represents the input data set, C h,i represents the compressive strength of the bth data point, T h,i represents the corresponding temperature data, exp is the natural exponential function, D h,i is the deformation characteristic data, k h is the shape parameter of the response curve, n b Represents the total number of data points.

[0098] S302: Analyze the input data set using the thermodynamic influence model, evaluate the influence of temperature change on the foundation pit structure by simulating the mechanical behavior of the foundation pit structure under differentiated temperature conditions, and obtain the execution process of mechanical behavior analysis data as follows;

[0099] The input data set is analyzed using a thermodynamic influence model, aiming to evaluate the actual impact of temperature changes on the foundation pit structure by simulating the mechanical behavior of the foundation pit structure under different temperature conditions. Through simulation, the behavior of the foundation pit under various temperature conditions, such as compression, expansion or displacement of the soil, can be predicted, which is crucial to ensure the safety and stability of the foundation pit structure. The simulation analysis relies on high-precision calculation methods and complex physical models to ensure the accuracy and reliability of the prediction results. The analysis results will help engineers understand the potential threat of temperature fluctuations to the stability of the foundation pit and take appropriate measures to prevent structural failure. The mechanical behavior analysis data is obtained using the formula:

[0100]

[0101] Among them, D mechan Represents mechanical behavior analysis data, F l,i represents the bth mechanical parameter, ΔT l,i Indicates the temperature change, n b Represents the total number of data points.

[0102] S303: Based on the mechanical behavior analysis data, the stability of the foundation pit structure under differentiated environmental conditions is calculated, and the stability of the ultra-deep soft soil foundation pit is quantified, and weight distribution is performed to obtain the execution process of the mechanical stability analysis result as follows;

[0103] Based on the mechanical behavior analysis data, the stability of the foundation pit structure under differentiated environmental conditions is calculated, and the stability of the ultra-deep soft soil foundation pit is quantified. By comprehensively analyzing the interaction between the mechanical behavior of the soil and the temperature conditions, the structural safety of the foundation pit under different environmental influences can be evaluated, and the stability of the foundation pit can be determined by quantitative methods, such as calculating the safety factor and the slip surface. The quantitative results are an important basis for risk assessment and formulation of reinforcement measures, which can ensure the safety of the foundation pit project and reduce potential risks caused by environmental changes. The mechanical stability analysis results are obtained using the formula:

[0104]

[0105] Among them, D stab represents the result of mechanical stability analysis, S m,i represents the gth stability evaluation parameter, var(S m,i ) represents the variability of the parameter, n g Represents the total number of parameters.

[0106] See also Figure 5 , using mechanical stability analysis results, summarizing historical accident records and real-time monitoring data, assessing construction risks, calculating the impact of differentiated risk factors, and classifying and prioritizing risks. The specific steps for generating construction risk assessment results are as follows:

[0107] S401: Using the results of mechanical stability analysis, historical accident records and real-time monitoring data are summarized, accident records associated with ultra-deep soft soil foundation pits are extracted, and real-time construction environment data are collected to obtain the summary data set. The execution process is as follows;

[0108] Using the results of mechanical stability analysis, historical accident records and real-time monitoring data are combined to fully understand the risks and site conditions related to ultra-deep soft soil foundation pits. All accident records related to the foundation pits are extracted from multiple databases and records, including detailed information on landslides, collapses or structural failures that occurred in the past. At the same time, real-time construction environment data such as groundwater level, soil moisture and temperature changes are collected, and the data is transmitted in real time through sensors installed on site. Through the aggregation of historical and real-time data, a basis is provided for further risk assessment and construction strategy optimization, and a summary data set is obtained, using the formula:

[0109]

[0110] Among them, D summaryrepresents the aggregated dataset, A o,i represents the severity of the oth accident record, exp is a natural exponential function, H o,i represents historical data points, R o,i is the risk score of real-time monitoring data, ΔH o,i is the difference between historical and real-time data, n b Represents the total number of data points.

[0111] S402: Based on the summary data set, the construction risk is evaluated, the impact of the differentiated risk factors in the construction environment is analyzed, and the potential impact of the differentiated risk factors on the construction is calculated, and the risks are classified to obtain the execution process of the construction risk data as follows;

[0112] Based on the aggregated data set, an in-depth assessment of construction risks will be conducted to analyze how different construction environmental factors such as changes in geological conditions, weather changes, and the intensity of construction activities affect the safety and stability of the foundation pit. Through detailed data analysis, risk factors with greater impact are identified and quantitatively calculated to predict the potential impact on construction safety. Risk classification is based on the calculation results and the probability and severity of the risk, ensuring that the construction team can clearly understand and respond to the most critical risk points and obtain construction risk data. The formula used is:

[0113]

[0114] Among them, D risk represents the construction risk data, F p,i represents the impact score of the ith risk factor, e is the base of the natural logarithm function, var(F p,i ) is the variability of the risk score, λ is the decay coefficient, and D p,i is the distance of the risk from the construction activity, n j Represents the total number of risk factors.

[0115] S403: Using the construction risk data, adopting the hierarchical analysis method, prioritize the risk factors, divide the risk categories according to the risk level, check the construction areas with the priority risk level and give priority to them, and obtain the execution process of the construction risk assessment results as follows;

[0116] Using construction risk data, construction risk factors are prioritized through the analytic hierarchy process. All identified risk factors will be evaluated and ranked according to the severity of their impact on construction safety and progress. This can effectively distinguish high-risk areas that need to be treated as a priority, which is crucial for the rational allocation of resources and the formulation of risk management strategies. The division of risk levels will help construction teams quickly identify and respond to potential dangers in actual operations, ensure the smooth progress of the construction process, and obtain construction risk assessment results.

[0117] The formula of AHP is as follows:

[0118]

[0119] Among them, w i is the weight of risk factor i, n j is the total number of risk factors, r ij is the risk adjustment coefficient of risk factor i to risk factor j, m ij is the environmental impact coefficient of risk factor i on risk factor j, w j is the weight of risk factor j, r kj is the risk adjustment coefficient of risk factor k for risk factor j, m kj is the environmental impact coefficient of risk factor k on risk factor j, w k is the weight of risk factor k, a ij Score the relative importance of risk factor i to risk factor j.

[0120] The execution flow is as follows:

[0121] Collect and determine the relative importance score for each risk factora ij , which is used to measure the relative importance of risk factor i to risk factor j and calculate the basic weight w of each risk factor j j , determine the risk adjustment coefficient r for each risk factor i and j ij , the coefficient takes into account the impact of actual risk adjustment and determines the environmental impact coefficient m for each pair of risk factors ij The coefficient takes into account the impact of environmental changes on risk and substitutes the coefficient into the formula to calculate the comprehensive score w i To ensure a more accurate ranking of risk factors, expert review and data analysis were used to determine the weight coefficients for each risk factor. ij and m ij In order to determine the specific value of each risk factor, it is necessary to conduct an in-depth analysis of each risk factor and consider the impact of actual environmental factors to improve the accuracy of the assessment.

[0122] See also Figure 6 ,Using the construction risk assessment results, the dependency relationship between the differentiated risk factors is analyzed, and the relationship between the risk factors is derived. The steps to generate the risk dependency model are as follows:

[0123] S501: extract risk factor data from the construction risk assessment results, analyze the association between risk factors, identify the dependency between differentiated risk factors, and obtain the execution process of dependency data as follows;

[0124] Risk factor data is extracted from the construction risk assessment results, and the associations and dependencies between factors are analyzed. By comprehensively analyzing the historical data and current monitoring data of different risk factors, it is possible to identify factors that occur simultaneously or affect each other, for example, increased soil moisture is associated with rising groundwater levels. Association analysis is essential to understanding how risk factors jointly affect construction safety. By systematically analyzing the data, a framework for risk factor dependencies can be constructed, which not only helps predict risk scenarios, but also allows for more effective interventions on key risk factors in risk management strategies to obtain dependency data.

[0125] S502: Based on the dependency data, perform relationship deduction, analyze the mutual influence between risk factors, verify the correlation and influence degree between risk factors, and obtain the execution process of relationship deduction data as follows;

[0126] Based on the dependency data, in-depth relationship deduction is carried out. By analyzing the mutual influence between different risk factors, the actual impact degree and operation mechanism of the relationship can be verified. For example, through statistical analysis and model simulation, it is possible to evaluate how the change of a risk factor will affect related factors. Relationship deduction is crucial for accurately predicting the development trend of risks and formulating effective risk response measures. To obtain relationship deduction data, the formula used is:

[0127]

[0128] Among them, D relation Represents the relationship derived data, I r,i represents the impact index of the ith risk factor, Rel(i) is the set of factors related to the ith risk factor, and C r,j and C r,i Respectively represent the correlation strength of the jth and ith factors, n j Represents the total number of risk factors.

[0129] S503: The relationship between the differentiated risk factors is graphically represented by deducing data through relationships, nodes are used to represent risk factors, edges represent the relationship between risk factors, and the strength of the dependency relationship is defined. The execution process of generating the risk dependency relationship model is as follows;

[0130] By deriving data through relationships, the differentiated relationships between risk factors are presented through graphical methods. Using chart tools, each risk factor is represented by a node, and the lines between the nodes represent the relationship between the factors. The thickness or color of the line can represent the strength of the dependency. Through visual means, the complex interactions between risk factors can be intuitively displayed, helping construction teams and project managers to quickly identify and understand high-risk areas or critical control points. It can also be used to demonstrate the effects of different risk management strategies, provide intuitive support for decision-making, and generate a risk dependency model using the formula:

[0131]

[0132] Among them, S mod represents the risk dependency model, N s,i represents the risk level of the sth node, σ(N s,i ) is the standard deviation of the risk level, W s,i is the node weight, n s Represents the total number of nodes.

[0133] See also Figure 7 , based on the risk dependency model, set real-time monitoring standards and data collection frequency, design monitoring plans and establish emergency response mechanisms. The specific steps for generating a safety assessment protocol are as follows:

[0134] S601: According to the risk dependency model, set the real-time monitoring standard and data collection frequency, determine the real-time monitoring standard of key risk factors, and set the frequency and time interval of data collection. The execution process of obtaining the monitoring standard and frequency record is as follows;

[0135] According to the risk dependency model, set the real-time monitoring standards and data collection frequency for key risk factors in ultra-deep soft soil foundation pits. According to the output of the risk dependency model, identify the risk factors that have the greatest impact on foundation pit safety, formulate specific monitoring standards, how to measure, when to measure, and what indicators to measure, to ensure that the standards can reflect the actual status of the risk in real time, determine the frequency and time interval of data collection, and adjust according to the speed of change and degree of impact of the risk factors. For example, if a risk factor changes rapidly and has a significant impact, the frequency of data collection will increase accordingly. Through the systematic setting of monitoring standards, it can ensure that the monitoring of foundation pits is both effective and economical, and the monitoring standards and frequency records are obtained. The formula used is:

[0136]

[0137] Among them, T monito Indicates monitoring standard and frequency record, S t,i represents the monitoring standard of the ith risk factor, e is the base of the natural logarithm function, Ft,i is the data collection frequency, ΔT t,i is the time interval since the last monitoring, k t is a parameter for adjusting the response sensitivity, n j Represents the total number of monitored factors.

[0138] S602: Based on the monitoring standards and frequency records, the planning arrangement of the ultra-deep soft soil foundation pit is designed, including data collection, monitoring equipment configuration and data transmission process, and the execution process of the monitoring plan is obtained as follows;

[0139] Based on the monitoring standards and frequency records, the monitoring plan for ultra-deep soft soil foundation pits is designed, including data collection, configuration of monitoring equipment, and determination of data transmission process. Based on the set monitoring standards and frequencies, ensure that all key data can be effectively collected and transmitted to the data processing center in a timely manner. Including the physical layout of the monitoring equipment, maintenance plan, and fault response measures to ensure the continuity and accuracy of monitoring activities. Considering the security and privacy protection of the data, ensure that all collected information complies with the requirements of laws and regulations, and obtain the monitoring plan, using the formula:

[0140]

[0141] Among them, U plan represents the monitoring scheme, P u,i represents the planning accuracy of the u-th monitoring point, var(P u,i ) is the variability of planning accuracy, C u,i is the reliability of data transmission, n u Represents the total number of monitoring points.

[0142] S603: Through the monitoring plan, establish an emergency response mechanism, formulate emergency response processes and measures, set the time schedule, responsibility allocation and response time for emergency handling, and generate the execution process of the safety assessment protocol as follows;

[0143] Through the monitoring plan, establish an emergency response mechanism, formulate emergency response processes and measures, including formulating detailed emergency handling procedures, how to quickly and effectively respond to abnormal situations found during the monitoring process. Including emergency communication protocols, personnel evacuation plans, emergency repair work and necessary safety measures. It is also necessary to set specific time arrangements and responsibility allocations to ensure that each team member is clear about the responsibilities and time requirements for emergency response, minimize the impact of potential risks, and generate a safety assessment protocol using the formula:

[0144]

[0145] Among them, D safe represents the security assessment protocol, R v,irepresents the response time of the vth emergency measure, T v,i Is mission critical, L v,i is the level of responsibility, n v Represents the total number of emergency measures.

[0146] See also Figure 8 , a safety evaluation system for ultra-deep soft soil foundation pit, the safety evaluation system for ultra-deep soft soil foundation pit is used to execute the above ultra-deep soft soil foundation pit safety evaluation method, the system comprises:

[0147] The data collection module collects soil type, soil density, groundwater level and temperature data in ultra-deep soft soil foundation pits, organizes the data and checks the integrity of the data to generate a geological data record table;

[0148] The model building module is based on the geological data record table, records the impact of soil temperature changes on moisture, density and compressibility, calculates the changes in the physical properties of soft soil under differentiated temperature conditions, and builds a thermodynamic impact model;

[0149] The mechanical behavior prediction module analyzes the mechanical behavior of the foundation pit structure affected by temperature changes based on the thermodynamic influence model, the compressive strength and deformation characteristics of the foundation pit, and generates mechanical stability analysis results;

[0150] The risk classification module uses the results of mechanical stability analysis to evaluate construction risks, quantify the impact of differentiated risk factors, and classify risk levels to generate construction risk assessment results;

[0151] The monitoring and emergency response module identifies the correlation between risk factors based on the construction risk assessment results, formulates real-time monitoring standards and data collection frequency, and generates a safety assessment protocol through the emergency response mechanism.

[0152] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for safety assessment of ultra-deep soft soil foundation pit, characterized in that: The following steps are involved: Obtain geological data of ultra-deep soft soil foundation pits, record soil type, soil density, groundwater level and temperature data, organize and format data, and generate geological data record tables; Based on the geological data record table, record the impact data of soil temperature changes on moisture, density and compressibility, calculate the changes in physical properties of soft soil under differentiated temperature conditions, and construct a thermodynamic impact model; According to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, the mechanical behavior of the foundation pit structure affected by temperature changes is analyzed, the stability of the structure under differentiated environmental conditions is calculated, and the mechanical stability analysis results are generated; Using the mechanical stability analysis results, summarizing historical accident records and real-time monitoring data, evaluating construction risks, calculating the impact of differentiated risk factors, and classifying and prioritizing risks to generate construction risk assessment results; Utilizing the construction risk assessment results, analyzing the dependencies between differentiated risk factors, and deriving the relationships between risk factors to generate a risk dependency model; Based on the risk dependency model, real-time monitoring standards and data collection frequency are set, a monitoring plan is designed, an emergency response mechanism is established, and a safety assessment protocol is generated.

2. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: The geological data record table includes classified soil types, measured soil density, recorded groundwater levels and real-time updated temperature data; the thermodynamic influence model includes soil heat capacity calculation, water evaporation rate estimation and soil compressibility response analysis; the mechanical stability analysis results include compressive strength values, deformation characteristics description and stability safety boundaries of foundation pit structures; the construction risk assessment results include risk factor grading, accident frequency statistics and quantification of risk factor impact; the risk dependency relationship model includes risk factor correlation analysis, dependency path diagram and key risk node identification; the safety assessment protocol includes monitoring frequency setting, monitoring standard updating and emergency response process.

3. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: The steps to obtain the geological data of the ultra-deep soft soil foundation pit, record the soil type, soil density, groundwater level and temperature data, sort and format the data, and generate the geological data record table are as follows: Arrange soil type, soil density, groundwater level and temperature measurement equipment in ultra-deep soft soil foundation pits, install, calibrate and perform functional tests on the equipment, and record the equipment configuration and initial status to obtain original geological data; Based on the raw geological data, soil type, density, groundwater level and temperature data are regularly read from the differential measurement equipment, the data are classified and processed, invalid and erroneous data are deleted, and formatted geological data are obtained; Based on the formatted geological data, data is imported according to the set data table structure, data content is sorted, and column width and format are adjusted to generate a geological data record table.

4. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: Based on the geological data record table, the influence data of soil temperature change on moisture, density and compressibility are recorded, and the physical property changes of soft soil under differentiated temperature conditions are calculated. The steps of constructing the thermodynamic influence model are as follows: Extract soil temperature change data from the geological data record table, record soil moisture, density and compressibility data accordingly, perform multiple measurements under gradual temperature changes, and obtain a temperature impact data set; Based on the temperature impact data set, the moisture, density and compressibility of soft soil under differentiated temperature conditions are compared and analyzed, and the changes of each property under differentiated temperature are evaluated item by item to obtain the physical property change data; The physical property change data is used to simulate the change of the physical properties of soft soil under differentiated temperature conditions, and the model parameters are defined to generate a thermodynamic influence model.

5. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: According to the thermodynamic influence model, the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit are input, the mechanical behavior of the foundation pit structure affected by temperature changes is analyzed, and the stability of the structure under differentiated environmental conditions is calculated. The specific steps for generating the mechanical stability analysis results are as follows: Inputting the compressive strength and deformation characteristics of the ultra-deep soft soil foundation pit into the thermodynamic influence model and combining them with the temperature change data to identify the influence of the temperature change on the ultra-deep soft soil foundation pit, thereby obtaining an input data set; The input data set is analyzed using a thermodynamic influence model, and the influence of temperature change on the foundation pit structure is evaluated by simulating the mechanical behavior of the foundation pit structure under differentiated temperature conditions, thereby obtaining mechanical behavior analysis data; Based on the mechanical behavior analysis data, the stability of the foundation pit structure under differentiated environmental conditions is calculated, and the stability of the ultra-deep soft soil foundation pit is quantified, and weight distribution is performed to obtain the mechanical stability analysis results.

6. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: The mechanical stability analysis results are used to summarize historical accident records and real-time monitoring data, evaluate construction risks, calculate the impact of differentiated risk factors, and classify and prioritize risks. The specific steps for generating construction risk assessment results are as follows: Utilizing the mechanical stability analysis results, summarizing historical accident records and real-time monitoring data, extracting accident records associated with ultra-deep soft soil foundation pits, and collecting real-time construction environment data to obtain a summary data set; Based on the summary data set, the construction risk is evaluated, the influence degree of the differentiated risk factors in the construction environment is analyzed, and the potential influence of the differentiated risk factors on the construction is calculated, and the risks are classified to obtain the construction risk data; The construction risk data is used to prioritize risk factors using the analytic hierarchy process, and risk categories are divided according to risk levels. Construction areas with priority risk levels are checked and treated first to obtain construction risk assessment results.

7. The ultra-deep soft soil foundation pit safety assessment method according to claim 6, characterized in that: The formula of the hierarchical analysis method is as follows: Among them, w i is the weight of risk factor i, n j is the total number of risk factors, r ij is the risk adjustment coefficient of risk factor i to risk factor j, m ij is the environmental impact coefficient of risk factor i on risk factor j, w j is the weight of risk factor j, r kj is the risk adjustment coefficient of risk factor k for risk factor j, m kj is the environmental impact coefficient of risk factor k on risk factor j, w k is the weight of risk factor k, a ij Score the relative importance of risk factor i to risk factor j.

8. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: Using the construction risk assessment results, the dependencies between the differentiated risk factors are analyzed, and the relationships between the risk factors are derived. The steps for generating a risk dependency model are as follows: Extracting risk factor data from the construction risk assessment results, analyzing the correlation between risk factors, identifying the dependency relationships between differentiated risk factors, and obtaining dependency relationship data; Based on the dependency relationship data, relationship deduction is performed to analyze the mutual influence between risk factors, verify the correlation and influence degree between risk factors, and obtain relationship deduction data; Data is derived through the relationship, the relationship between differentiated risk factors is graphically represented, nodes are used to represent risk factors, edges represent the relationship between risk factors, and the strength of the dependency is defined to generate a risk dependency model.

9. The ultra-deep soft soil foundation pit safety assessment method according to claim 1, characterized in that: Based on the risk dependency model, the steps of setting real-time monitoring standards and data collection frequency, designing monitoring plans and establishing emergency response mechanisms to generate a safety assessment protocol are as follows: According to the risk dependency model, real-time monitoring standards and data collection frequency are set, real-time monitoring standards for key risk factors are determined, and the frequency and time interval for data collection are set to obtain monitoring standards and frequency records; Based on the monitoring standards and frequency records, design the planning arrangement of the ultra-deep soft soil foundation pit, including data collection, monitoring equipment configuration and data transmission process, and obtain the monitoring plan; Through the monitoring plan, an emergency response mechanism is established, emergency response processes and measures are formulated, the time schedule, responsibility allocation and response time of emergency handling are set, and a safety assessment protocol is generated.

10. A safety assessment system for ultra-deep soft soil foundation pit, characterized in that: According to the ultra-deep soft soil foundation pit safety assessment method according to any one of claims 1 to 9, the system comprises: The data collection module collects soil type, soil density, groundwater level and temperature data in ultra-deep soft soil foundation pits, organizes the data and checks the integrity of the data to generate a geological data record table; The model building module records the influence of soil temperature change on moisture, density and compressibility based on the geological data record table, calculates the change of soft soil physical properties under differentiated temperature conditions, and builds a thermodynamic influence model; The mechanical behavior prediction module analyzes the mechanical behavior of the foundation pit structure affected by temperature changes according to the thermodynamic influence model and the compressive strength and deformation characteristics of the foundation pit, and generates mechanical stability analysis results; The risk classification module uses the mechanical stability analysis results to evaluate construction risks, quantify the impact of differentiated risk factors, and classify risk levels to generate construction risk assessment results; The monitoring and emergency response module identifies the correlation between risk factors based on the construction risk assessment results, formulates real-time monitoring standards and data collection frequencies, and generates a safety assessment protocol through an emergency response mechanism.

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