Shield-stratum-pile foundation collaborative dynamic risk evaluation method and system

Abstract

The invention discloses a shield-stratum-pile foundation collaborative dynamic risk evaluation method and system, and the method comprises the steps: carrying out the global recognition of a stratum, a pile foundation, a construction process and an external environment type core risk source based on the engineering geology, hydrogeology, pile foundation structures and surrounding environment data of a shield construction region; and constructing a hierarchical collaborative evaluation index system covering a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameter. Stratum, pile foundation, construction and environment multi-source data are synchronously collected, the collection frequency is dynamically adjusted according to the construction progress and the risk state, and a standardized effective data set is obtained through preprocessing. Constructing a multi-field coupling relation model, determining an index weight by a combined weighting method, and substituting the index weight into data to calculate a collaborative risk quantized value; a risk grade standard is preset according to the specification, the grade is dynamically judged according to the quantized value, and early warning information is generated. And a scientific basis is provided for engineering safety prevention and control.

Description

Technical Field

[0001] This invention belongs to the field of underground engineering construction safety risk assessment technology, and more specifically, relates to a dynamic risk assessment method and system for shield tunneling-stratum-pile foundation coordination. Background Technology

[0002] During tunnel boring machine (TBM) construction, there are complex dynamic synergies among ground stability, pile foundation safety, and the surrounding environment. This multi-factor coupling relationship can easily trigger a chain of risks, posing a serious threat to project safety. Current TBM construction risk assessment technologies mostly focus on single-factor analysis, such as assessing ground settlement or pile foundation deformation alone, failing to fully consider the interaction mechanisms between the ground, pile foundation, and environment under construction disturbances, leading to discrepancies between the assessment results and the actual risk status.

[0003] Existing evaluation index systems often suffer from fragmentation, with index selection lacking a comprehensive consideration of synergistic effects, making it difficult to fully cover risk sources throughout the entire construction process. In the data collection phase, most systems adopt a fixed-frequency collection mode, failing to dynamically adjust collection strategies based on factors such as tunnel boring progress and geological changes. This results in either delayed early warnings due to data lag during high-risk phases or excessive data collection during low-risk phases, leading to resource waste.

[0004] In terms of risk quantification models, traditional methods often rely on empirical coefficients or subjective weighting, which are insufficient for quantitatively characterizing multi-field coupling effects and cannot accurately reflect the dynamic transmission process between construction disturbances, ground deformation, and pile foundation response. Risk level determination often adopts static threshold comparison methods, without combining dynamic characteristics such as risk evolution rate, making it impossible to predict risk development trends in advance, resulting in weak targeting of prevention and control measures.

[0005] With the increasing density of urban underground engineering projects, the scenarios of tunnel boring machines (TBMs) crossing existing pile foundation areas are becoming more frequent. Traditional technologies such as single-factor evaluation, static data collection, and subjective quantification are no longer sufficient to meet the risk management needs of complex projects. Therefore, there is an urgent need to develop an evaluation technology that can coordinate synergistic effects and dynamically adapt to risk states, enabling accurate assessment and prediction of the collaborative risks of the TBM-soil-pile foundation system. This is of great practical significance for ensuring construction safety and reducing environmental impact. Summary of the Invention

[0006] This invention aims to address the problems of single-factor orientation, insufficient characterization of synergistic effects, static data collection, and lagging risk prediction in traditional shield tunneling construction risk assessment. By constructing a hierarchical collaborative evaluation index system, a dynamically adaptable data collection mechanism, a multi-field coupled quantitative model, and a dynamic level determination logic, it achieves comprehensive coverage, accurate quantification, and real-time prediction of collaborative risks between shield tunneling, strata, and pile foundation, providing a scientific basis for engineering safety prevention and control.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, the present invention provides a dynamic risk assessment method for the coordinated operation of shield tunneling, strata, and pile foundations, comprising: S1. Based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, identify all kinds of core risk sources caused by shield tunneling involving strata, pile foundation, construction process and external environment; construct a hierarchical collaborative evaluation index system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters; S2. Synchronously collect ground condition data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data; the data collection frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; the collected multi-source data is preprocessed to generate a standardized and effective dataset. S3. Construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact to quantify the dynamic interaction mechanism among various factors; use a combined weighting method to determine the weights of each evaluation index, and substitute the standardized dataset into the coupling relationship model to dynamically calculate the quantified value of collaborative risk, thereby completing the quantitative characterization of risk status under collaborative action. S4. Based on industry standards, engineering design requirements, and risk tolerance levels, preset multi-level risk classification standards; dynamically determine the current risk level and generate early warning information based on the real-time calculated collaborative risk quantification value.

[0008] Furthermore, the secondary classification indicators in S1 specifically include: Formation characteristic indicators are selected from one or more combinations of formation deformation-related parameters, formation stress state parameters, and groundwater state parameters. Pile foundation response indicators are selected from one or more combinations of pile foundation displacement parameters, pile foundation mechanical response parameters, and pile foundation structural integrity parameters. Construction control indicators are selected from one or more combinations of shield tunneling parameters, support parameters, and construction process-related parameters. External impact indicators are selected from one or more combinations of deformation parameters of surrounding buildings, response parameters of underground pipelines, and surface environmental change parameters.

[0009] Furthermore, the comprehensive index of primary collaborative risk in S1 is a comprehensive quantitative index formed by weighted aggregation of monitoring parameter data of secondary classification indicators. It is used to characterize the overall risk status under the synergistic effect of multiple factors such as shield tunneling construction, stratum deformation, pile foundation response, and environmental impact. Its aggregation logic covers the coupling risk of construction and stratum, the linkage risk of stratum and pile foundation, the feedback risk of pile foundation and stratum, and the environmental risk derived from the synergistic effect.

[0010] Furthermore, the process of dynamically adjusting the data acquisition frequency in S2 according to the construction progress and risk status is as follows: The real-time distance of the shield tunneling distance monitoring section is defined as follows: Real-time distance Positioning data is acquired through the shield tunneling control system; a preset distance threshold is defined as follows. The value is taken as a multiple of the shield tunneling influence radius; the collaborative risk quantification value is defined as... Collaborative risk quantification value The risk level was calculated using a multi-field coupling model of shield tunneling, ground deformation, pile foundation response, and environmental impact; a preset risk level threshold value was defined. This serves as a boundary standard for adjusting monitoring frequency based on risk status; the construction progress impact coefficient is defined as... The risk state influence coefficient is defined by the complexity of engineering geological conditions. The deformation resistance of the column base structure is determined by its ability to resist deformation. Data acquisition frequency The dynamic adjustment is determined by the following formula: , in, The hyperbolic tangent function is used to... and The value is mapped to The interval allows for smooth adjustment of the data acquisition frequency based on construction progress and risk status; when the real-time distance... hour, The closer the construction progress is to the monitoring section, the higher the data collection frequency should be compared to the baseline value. When the collaborative risk quantification value Approaching hour, The closer the risk level is to 1, the higher the data collection frequency should be compared to the baseline value. Ultimately, this allows the data acquisition frequency to be dynamically matched with the intensity of the synergistic effect between the tunnel boring machine, the stratum, and the pile foundation, ensuring both the timeliness of monitoring during high-risk phases and avoiding excessive data collection during non-risk phases.

[0011] Furthermore, the standardized effective dataset in S2 is presented in the form of a data matrix. The row dimension of the data matrix corresponds to the monitoring time series, and the column dimension corresponds to all the specific monitoring parameters of the three levels in the hierarchical collaborative evaluation index system. Each matrix element is the standardized processing result of the corresponding monitoring parameter under a single monitoring time series. The row index of the data matrix is ​​the monitoring timestamp, which corresponds one-to-one with the time nodes of the shield tunneling positioning data and the stratum-pile foundation synergy, ensuring the consistency of the data time sequence; the column index is the monitoring parameter identifier, which contains the secondary classification index category to which the parameter belongs and the parameter name information, realizing the association between the parameter and the evaluation index system. The standardization results of each element in the matrix are determined as follows: Based on the preset judgment threshold of the corresponding monitoring parameters, a normalization algorithm is used to convert the raw collected data into dimensionless values, and the range of these dimensionless values ​​is mapped to... The interval is defined as follows: 0 corresponds to the ideal safe value of the monitoring parameter, and 1 corresponds to the warning threshold value of the monitoring parameter.

[0012] Furthermore, the multi-field coupling relationship model in S3 is specifically as follows: The multi-field coupling relationship model is constructed through a three-step progressive logic of "parameter correlation quantification - synergistic effect aggregation - risk evolution characterization", as follows: The first step is parameter correlation quantization to characterize the fundamental interaction relationships between pairs of fields: , , , , in, , , , These are the pairwise coupling coefficients for shield tunneling-soil deformation, soil deformation-pile foundation response, pile foundation response-environmental impact, and environmental impact-shield tunneling, respectively. Their values ​​are mapped to the [0,1] interval to quantify the interaction strength between the two field parameters. , , , These are the comprehensive parameters of shield tunneling construction, comprehensive parameters of ground deformation, comprehensive parameters of pile foundation response, and comprehensive parameters of environmental impact in the standardized effective dataset. Their values ​​directly correspond to the three-level monitoring parameters of the hierarchical evaluation index system. The second step involves synergistic effect aggregation, integrating the pairwise interactions of multiple fields to form a comprehensive coupling strength: , in, The multi-field integrated coupling strength is obtained by taking the square root of the average of the squares of the pairwise coupling coefficients, which preserves the independent contribution of each field coupling while reflecting the overall characteristics of the synergistic effect. The third step is to characterize the risk evolution and output a collaborative risk quantification value by combining time-series dynamic characteristics: , in, To coordinate risk quantification values, directly connect to the subsequent risk level determination process; It is a time partial derivative operator that characterizes the temporal rate of change of the composite parameters.

[0013] Furthermore, the calculation process for the weights of each evaluation index in S3 is as follows: The first step is to calculate the dynamic volatility of the indicator and quantify the risk sensitivity of a single indicator: , in, For the first The dynamic fluctuation of each of the three-level monitoring indicators; For this indicator in the 1st Standardized time-series data; , These are the standardized maximum and minimum values ​​of the indicator during the monitoring period; This is the standardized mean of the indicator; The second step is to calculate the degree of synergistic correlation between indicators, and to quantify the strength of interdependence among indicators: , in, For the first The degree of synergistic correlation among the indicators; This refers to the total number of three-level monitoring indicators in the hierarchical evaluation indicator system. For the first The first indicator and the first Covariance of each indicator; , These are the variances of the two indicators, respectively. The third step is to calculate the final weight of the indicators and integrate sensitivity and synergy to form a comprehensive weight: , in, For the first The final weights of the three-level monitoring indicators range from (0,1); The total number of evaluation indicators participating in the weight calculation; the numerator is the product of volatility and correlation, which comprehensively reflects the "independent sensitivity" and "co-dependency" of the indicators; the denominator is the sum of the numerator terms corresponding to all indicators, which realizes the normalization of weights.

[0014] Furthermore, the process of dynamically determining the current risk level in S4 is as follows: First, a baseline risk level matching process is performed. This step serves as the foundation for level determination and directly links the collaborative risk quantification results with preset threshold standards, specifically through a formula. Implementation, in which Based on the baseline risk level, It is the real-time collaborative risk quantification value output by the multi-field coupling relationship model. These are preset multi-level risk level thresholds; these thresholds are not set independently, but are adapted to the judgment thresholds of each monitoring parameter in the hierarchical collaborative evaluation index system, and strictly follow industry standards and engineering design requirements. The threshold comparison function... By and Compare each item individually to accurately output the corresponding baseline risk level; Based on the baseline level, a risk evolution rate is introduced for dynamic correction, making the level determination more closely reflect the real-time trend of risk changes. The core of the correction process is the calculation and verification of the risk evolution rate, which is expressed by the formula... Received, among which This is the risk quantification value from the previous monitoring period. The interval between adjacent monitoring sessions is directly linked to the dynamically adjusted acquisition frequency in the data acquisition process, ensuring that the rate calculation reflects the real-time nature of data acquisition. Then With respect to the preset rate threshold Substitute into the correction formula Through the verification function Adjust the baseline level upwards or downwards to obtain the corrected level. ; Finally, the final risk level is determined through boundary verification to ensure the rationality and standardization of the assessment results; the verification formula is as follows: ,in , These are the preset minimum and maximum values ​​for the level. Boundary constraint calculations are used to ensure the final level. It is within a reasonable range.

[0015] As a second aspect of the present invention, a dynamic risk assessment system for shield tunneling-soil-pile foundation coordination is also provided, comprising: The indicator system construction unit is used to identify, based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, to identify all kinds of core risk sources caused by shield tunneling construction involving strata, pile foundation, construction process and external environment; and to construct a hierarchical collaborative evaluation indicator system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters. The multi-source data acquisition and preprocessing unit is used to simultaneously acquire ground condition data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data; the data acquisition frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; and the acquired multi-source data is preprocessed to generate standardized and effective datasets. The risk quantification unit is used to construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact, and to quantify the dynamic interaction mechanism among various factors. The weight of each evaluation index is determined by the combined weighting method, and the standardized dataset is substituted into the coupling relationship model to dynamically calculate the synergistic risk quantification value, thereby completing the quantitative characterization of the risk state under synergistic effect. The risk level determination unit is used to preset multi-level risk level classification standards based on industry standards, engineering design requirements and risk tolerance levels; and dynamically determine the current risk level and generate early warning information based on the real-time calculated collaborative risk quantification value.

[0016] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor to provide a dynamic risk assessment method for shield-soil-pile foundation coordination as described in any one of the claims.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The dynamic risk assessment method for shield tunneling-soil-pile foundation collaboration of this invention identifies core risk sources involving soil, pile foundation, construction process, and external environment across the entire shield tunneling area based on basic data on engineering geology, hydrogeology, pile foundation structures, and surrounding environment. It constructs a hierarchical collaborative evaluation index system comprising a primary collaborative risk comprehensive index, secondary classification indicators, and tertiary specific monitoring parameters. The primary collaborative risk comprehensive index serves as the core hub, formed through weighted aggregation of secondary classification indicators. The aggregation logic covers construction-soil coupling, soil-pile foundation linkage, pile foundation-soil feedback, and collaboratively derived environmental risks. This index system breaks through the limitations of single-factor evaluation, achieving systematic coverage of multi-dimensional risk sources. It makes risk assessment more closely aligned with the actual scenarios of multi-factor collaborative effects, providing a comprehensive and hierarchically clear index foundation for subsequent accurate evaluation, and solving the problems of fragmentation and insufficient collaboration in traditional evaluation indicators.

[0018] 2. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination of this invention synchronously collects multi-source data on soil condition, pile foundation structural response, shield tunneling parameters, and surrounding environmental influences. The collection frequency is dynamically adjusted according to construction progress and risk status. After preprocessing, a standardized and effective dataset is generated. A multi-field coupling relationship model of shield tunneling, soil deformation, pile foundation response, and environmental influences is constructed. Weights are determined using a combined weighting method based on the dynamic fluctuation of indicators and the degree of coordination. Standardized data is substituted into the model to calculate the quantified value of coordinated risk. The dynamic adjustment of data collection frequency is achieved through a function relating construction progress and risk status. The standardized dataset is presented in the form of a time-series-parameter matrix to ensure cross-parameter comparability. The multi-field coupling model quantifies the coordination mechanism through a three-step progressive formula. This process achieves dynamic adaptation between data collection and risk status, improves the accuracy of risk quantification through data standardization and coupled modeling, and solves the problems of poor data timeliness and insufficient characterization of coupling effects in traditional assessments.

[0019] 3. The dynamic risk assessment method for shield tunneling-soil-pile foundation collaboration of the present invention presets multi-level risk level thresholds based on industry standards, design requirements, and risk acceptance levels. It then compares and matches benchmark risk levels with real-time collaborative risk quantification values ​​and these thresholds. The benchmark level is corrected by introducing a risk evolution rate associated with the data collection frequency. Finally, the risk level is verified through boundary constraints, and early warning information is generated. Benchmark level matching relies on thresholds adapted to the indicator system. Rate correction is achieved by calculating the rate using the difference between adjacent time-series risk values ​​and the data collection interval. The final level's rationality is ensured through boundary constraints. This dynamic judgment process constructs a "benchmark matching-rate correction-boundary verification" logic, establishing a closed-loop linkage between risk level, indicator system, data collection, and risk quantification. This accurately reflects the real-time risk status and evolution trend, solving the problems of static risk level determination and poor trend adaptability in traditional methods, and providing a reliable basis for collaborative dynamic risk prevention and control. Attached Figure Description

[0020] Figure 1 This is a flowchart of the dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to an embodiment of the present invention; Figure 2 This is a schematic diagram of longitudinal differential settlement of a tunnel according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the lateral horizontal displacement of the tunnel according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the layout of measuring points and lines for the settlement and clearance convergence of the tunnel lining segment arch in an embodiment of the present invention. Figure 5 This is a transverse layout diagram of the measuring points of the surface settlement monitoring section above the present invention. Figure 6 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1 This embodiment 1 provides a dynamic risk assessment method for the coordinated operation of shield tunneling, strata, and pile foundation, including: S1. Based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, identify all kinds of core risk sources caused by shield tunneling involving strata, pile foundation, construction process and external environment; construct a hierarchical collaborative evaluation index system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters; S2. Synchronously collect ground condition data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data; the data collection frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; the collected multi-source data is preprocessed to generate a standardized and effective dataset. S3. Construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact to quantify the dynamic interaction mechanism among various factors; use a combined weighting method to determine the weights of each evaluation index, and substitute the standardized dataset into the coupling relationship model to dynamically calculate the quantified value of collaborative risk, thereby completing the quantitative characterization of risk status under collaborative action. S4. Based on industry standards, engineering design requirements, and risk tolerance levels, preset multi-level risk classification standards; dynamically determine the current risk level and generate early warning information based on the real-time calculated collaborative risk quantification value.

[0023] This embodiment 1 further elaborates on the above steps.

[0024] (1) Construction of indicator system In tunnel boring machine (TBM) construction, the complex interaction between the strata, pile foundations, and the surrounding environment can easily trigger a chain of risks. Traditional single-factor assessments are insufficient to cover multi-dimensional risk sources, therefore, it is necessary to first systematically analyze the risk correlation logic. Specifically, based on basic data such as the engineering geology, hydrogeology, pile foundation structures, and surrounding environment of the TBM construction area, a comprehensive identification of the core risk sources that may be affected during TBM construction should be conducted—including stratum stability risks, pile foundation structural safety risks, construction process control risks, and external environmental impact risks—ensuring that no risk source is overlooked.

[0025] Based on this, a hierarchical collaborative evaluation index system is constructed. This system takes the first-level collaborative risk comprehensive index as its core and is further divided into four secondary categories of indicators: the first is the stratum characteristic index, which covers parameters such as stratum deformation, stress state, and groundwater state, and is used to reflect the changes in the state of the stratum after construction disturbance; the second is the pile foundation response index, which includes parameters such as pile foundation displacement, mechanical response, and structural integrity, and reflects the feedback of the pile foundation to stratum changes; the third is the construction control index, which involves parameters related to shield tunneling, support, and construction technology, and is directly related to the intensity and mode of construction disturbance; the fourth is the external impact index, which covers parameters such as deformation of surrounding buildings, response of underground pipelines, and changes in the surface environment, and characterizes the degree of impact of collaborative effects on the external environment.

[0026] The Level 1 Collaborative Risk Comprehensive Index is a comprehensive quantitative index formed by weighted aggregation of monitoring parameter data of Level 2 classification indicators. It is used to characterize the overall risk status under the synergistic effect of multiple factors such as shield tunneling, ground deformation, pile foundation response, and environmental impact. Its aggregation logic covers the coupling risk between construction and ground, the linkage risk between ground and pile foundation, the feedback risk between pile foundation and ground, and the environmental risks derived from the synergistic effect.

[0027] Meanwhile, in the hierarchical collaborative evaluation index system, the third-level specific monitoring parameters are the refined implementation of the second-level classification indicators, directly corresponding to the actual collectable engineering data, ensuring the operability of the index system. For the stratum characteristic index, the third-level parameters include vertical settlement, horizontal displacement, soil lateral pressure, pore water pressure, etc., which can accurately capture the microscopic changes in the stratum under construction disturbance; the third-level parameters for pile foundation response cover the differential settlement at the top of the pile, pile inclination rate, pile bending moment, pile crack width, etc., which can directly reflect the specific response of the pile foundation structure after being stressed; the third-level parameters for construction control include shield tunneling speed, soil chamber pressure, synchronous grouting pressure and grouting volume, cutterhead torque, etc., which are the core parameters for regulating the intensity of construction disturbance; the third-level parameters for external impact include the settlement rate of surrounding buildings, displacement of underground pipelines, length of surface cracks, etc., which can intuitively reflect the actual impact of collaborative effects on the external environment. These tertiary parameters are all derived from routine monitoring methods at the engineering site, which not only ensures the feasibility of data collection but also transforms the abstract descriptions of the secondary classification indicators into concrete and measurable quantitative data, providing accurate basic data support for subsequent risk quantification analysis.

[0028] like Figures 2-5 As shown, these diagrams represent the on-site collection and data representation of specific monitoring parameters in the hierarchical collaborative evaluation index system of this embodiment. Among them, Figure 2 Longitudinal differential settlement of tunnels Figure 3 The lateral horizontal displacement of the tunnel corresponds to the stratum deformation parameter in the stratum characteristic index. Its data can directly reflect the longitudinal and lateral displacement state of the stratum under the disturbance of shield tunneling construction. Figure 4 The crown settlement measuring points and convergence measuring lines are the collection points for parameters of stratum deformation and stress state. The crown settlement and cross-sectional convergence data obtained through these measuring points are the core basic data of stratum characteristic indicators. Figure 5 The arrangement of monitoring points on the surface subsidence monitoring section above the land can be correlated with the surface environmental change parameters in the external impact index. The surface subsidence data collected can characterize the extent to which the synergistic effect affects the external environment.

[0029] The monitoring data corresponding to these illustrations all fall within the scope of the specific monitoring parameters of the third level of this invention. They need to undergo standardized preprocessing to form an effective dataset, and then, combined with the weights determined by the combined weighting method, participate in the weighted aggregation of the second-level classification indicators. Finally, they are integrated into the calculation process of the first-level collaborative risk comprehensive index. This is the key data source for the present invention to realize the quantitative characterization of the collaborative risk of shield tunneling-soil-pile foundation, and it also provides practical engineering data support for the subsequent calculation of multi-field coupling relationship models and risk level determination.

[0030] (2) Multi-source data acquisition and preprocessing In the risk assessment of tunnel boring machine (TBM) construction, the timeliness and accuracy of data directly affect the reliability of the assessment results. Traditional fixed-frequency data collection methods are ill-suited to the dynamic changes in construction progress and risk status. Therefore, a dynamically adaptable data collection and preprocessing mechanism is needed. Firstly, based on the three levels of specific monitoring parameters of the hierarchical collaborative evaluation index system, multi-source data on ground conditions, pile foundation response, TBM construction parameters, and the impact of the surrounding environment are collected simultaneously. This ensures that the data covers all dimensions required for the assessment, providing complete data support for subsequent analysis.

[0031] The dynamic adjustment of data collection frequency is based on a dual consideration of construction progress and risk status, achieving precise control by identifying key influencing factors. The specific process is as follows: The real-time distance of the shield tunneling distance monitoring section is defined as follows: Real-time distance By acquiring positioning data from the tunnel boring machine control system, the spatial relationship between the construction progress and the monitored section is directly reflected, thereby demonstrating the degree of impact of construction activities on the synergistic effect of the strata and pile foundation; a preset distance threshold is defined as... The value is a multiple of the shield tunneling influence radius, specifically determined based on the stratum diffusion angle and pile foundation depth, used to define the distance range within which construction progress significantly affects monitoring frequency; the collaborative risk quantification value is defined as... Collaborative risk quantification value The risk status under the current collaborative action of shield tunneling, ground deformation, pile foundation response, and environmental influence is calculated using a multi-field coupling model. The pre-defined risk level threshold is defined as follows: This value corresponds to the risk warning threshold, determined by engineering design requirements and acceptable risk levels, and serves as the boundary standard for adjusting monitoring frequency based on risk status; the construction progress impact coefficient is defined as... The degree of geological complexity determines the stability of the formation; the worse the geological conditions, the higher the stability. The larger the value, the greater the weighting of the impact of construction progress on monitoring needs under different geological conditions; the risk state influence coefficient is defined as... The deformation resistance of the pile foundation is determined by the structural resistance of the column foundation; the weaker the deformation resistance of the pile foundation, the lower its resistance. The larger the value, the more it is used to quantify the weight of the impact of risk status on monitoring requirements under different pile foundation characteristics; Data acquisition frequency The dynamic adjustment is determined by the following formula: , in, The hyperbolic tangent function is used to... and The value is mapped to The interval allows for smooth adjustment of the data acquisition frequency based on construction progress and risk status; when the real-time distance... hour, The closer the construction progress is to the monitoring section, the higher the data collection frequency should be compared to the baseline value. When the collaborative risk quantification value Approaching hour, The closer the risk level is to 1, the higher the data collection frequency should be compared to the baseline value. Ultimately, this allows the data acquisition frequency to be dynamically matched with the intensity of the synergistic effect between the tunnel boring machine, the stratum, and the pile foundation, ensuring both the timeliness of monitoring during high-risk phases and avoiding excessive data collection during non-risk phases.

[0032] After the data collection is completed, the multi-source data needs to be preprocessed to form a standardized and effective dataset. This dataset is presented in the form of a data matrix. The row dimension of the data matrix corresponds to the monitoring time series, and the column dimension corresponds to all the specific monitoring parameters of the three levels in the hierarchical collaborative evaluation index system. Each matrix element is the standardized processing result of the corresponding monitoring parameter under a single monitoring time series. The row index of the data matrix is ​​the monitoring timestamp, which corresponds one-to-one with the time nodes of the shield tunneling positioning data and the stratum-pile foundation synergy, ensuring the consistency of the data time sequence; the column index is the monitoring parameter identifier, which contains the secondary classification index category to which the parameter belongs and the parameter name information, realizing the association between the parameter and the evaluation index system. The standardization results of each element in the matrix are determined as follows: Based on the preset judgment threshold of the corresponding monitoring parameters, a normalization algorithm is used to convert the raw collected data into dimensionless values, and the range of these dimensionless values ​​is mapped to... The interval is defined as follows: 0 corresponds to the ideal safe value of the monitoring parameter, and 1 corresponds to the warning threshold value of the monitoring parameter.

[0033] (3) Risk quantification In the multi-factor system of shield tunneling, ground deformation, pile foundation response, and environmental impact, the dynamic coupling relationship between the factors is the core of risk assessment. Traditional assessment methods are difficult to accurately quantify this complex synergistic effect. Therefore, it is necessary to construct a targeted multi-field coupling relationship model and scientifically determine the weight of each evaluation index in order to achieve accurate quantification of risk status.

[0034] The multi-field coupling model is constructed through a three-step progressive logic, comprehensively covering both spatial coupling and temporal dynamic dimensions. Specifically: The first step is parameter correlation quantization to characterize the fundamental interaction relationships between pairs of fields: , , , , in, , , , These are the pairwise coupling coefficients for shield tunneling-soil deformation, soil deformation-pile foundation response, pile foundation response-environmental impact, and environmental impact-shield tunneling, respectively. Their values ​​are mapped to the [0,1] interval to quantify the interaction strength between the two field parameters. , , , These are the comprehensive parameters of shield tunneling construction, comprehensive parameters of ground deformation, comprehensive parameters of pile foundation response, and comprehensive parameters of environmental impact in the standardized effective dataset. Their values ​​directly correspond to the three-level monitoring parameters of the hierarchical evaluation index system. The second step involves synergistic effect aggregation, integrating the pairwise interactions of multiple fields to form a comprehensive coupling strength: , in, The multi-field integrated coupling strength is obtained by taking the square root of the average of the squares of the pairwise coupling coefficients, which preserves the independent contribution of each field coupling while reflecting the overall characteristics of the synergistic effect. The third step is to characterize the risk evolution and output a collaborative risk quantification value by combining time-series dynamic characteristics: , in, To coordinate risk quantification values, directly connect to the subsequent risk level determination process; This is a time-partial derivative operator that characterizes the temporal rate of change of the integrated parameters, echoing the dynamic adjustment logic of data acquisition and reflecting the real-time evolution characteristics of the synergistic effect of the tunnel boring machine, strata, and pile foundation; in the formula... The spatial coupling dimension of risk quantification is guaranteed, while the temporal partial derivative guarantees the temporal dynamic dimension of risk quantification. The combination of these two aspects achieves a comprehensive representation of collaborative risks. All input parameters of the entire model originate from standardized and valid datasets, and the parameter meanings correspond one-to-one with the secondary classification indicators of the hierarchical evaluation index system. The output... The value naturally connects to the critical value standard for risk level determination, forming a complete closed loop of "indicator system, data standardization, multi-field coupling, and risk quantification".

[0035] The weights of each evaluation indicator are determined using a combined weighting method, employing a three-step calculation process to ensure both objectivity and rationality. Specifically, the calculation process for the weights of each evaluation indicator is as follows: The first step is to calculate the dynamic volatility of the indicator and quantify the risk sensitivity of a single indicator: , in, For the first The dynamic fluctuation of each of the three-level monitoring indicators; For this indicator in the 1st Standardized time-series data (taken from a standardized valid dataset); , These are the standardized maximum and minimum values ​​of the indicator during the monitoring period; This is the standardized mean of the indicator; the greater the volatility, the more sensitive the indicator is to the synergistic effect of the shield tunneling-soil-pile foundation, and the higher the corresponding weight should be. This step is directly related to the time series characteristics of the standardized effective dataset. The second step is to calculate the degree of synergistic correlation between indicators, and to quantify the strength of interdependence among indicators: , in, For the first The degree of synergistic correlation among the indicators; This refers to the total number of three-level monitoring indicators in the hierarchical evaluation indicator system. For the first The first indicator and the first Covariance of each indicator; , These are the variances of the two indicators; the higher the correlation, the closer the synergistic effect of the indicator with other indicators, and the more significant its contribution to the overall risk. This step connects the overall structure of the hierarchical evaluation indicator system. The third step is to calculate the final weight of the indicators and integrate sensitivity and synergy to form a comprehensive weight: , in, For the first The final weights of the three-level monitoring indicators range from (0,1); The total number of evaluation indicators participating in the weight calculation; the numerator is the product of volatility and correlation, which comprehensively reflects the "independent sensitivity" and "co-dependency" of the indicators; the denominator is the sum of the numerator terms corresponding to all indicators, which realizes the normalization of weights.

[0036] After constructing the multi-field coupling relationship model and determining the weights of each evaluation indicator, the quantification of collaborative risk can be achieved by substituting the standardized dataset into the calculation. The entire process is centered on data flow, connecting the indicator system, weight results, and coupling model to form a complete logical chain. First, the input format of the standardized dataset is clarified. This dataset is presented as a time-series-parameter matrix. The row dimension corresponds to the monitoring time series, and the timestamps are precisely matched with the shield tunneling positioning and collaborative action nodes. The column dimension corresponds to all three-level monitoring parameters and identifies the associated secondary classification indicators, ensuring the temporal consistency and indicator correlation of the input data.

[0037] Before inputting the data, the association and matching between the weights and the dataset must be completed. The weights of each of the three-level monitoring parameters obtained by the combined weighting method are assigned to the column dimensions of the data matrix according to the parameter correspondence, so that the standardized value of each monitoring parameter is accompanied by a weight attribute, providing an important basis for subsequent aggregation calculation. Then, the three-step calculation process of the multi-field coupling relationship model is started: In the parameter association quantification stage, the model extracts four comprehensive parameters from the data matrix: shield tunneling construction, stratum deformation, pile foundation response, and environmental impact. Combined with the corresponding weights, four sets of pairwise coupling coefficients are calculated to quantify the intensity of the interaction between pairs. In the synergistic effect aggregation stage, the model performs square root processing on the square average of the four sets of coupling coefficients and integrates them to obtain the multi-field comprehensive coupling strength, reflecting the overall characteristics of multi-factor synergy. Finally, in the risk evolution characterization stage, the model calculates the rate of change of comprehensive parameters based on the time-series dimension of the data matrix, combines it with the multi-field comprehensive coupling strength, and dynamically outputs the synergistic risk quantification value corresponding to each time series.

[0038] The entire calculation process corresponds to the dynamic frequency of data acquisition. After each time series of standardized data is updated, the model repeats the above calculation steps in real time, ensuring that the quantified value of collaborative risk is dynamically updated with the construction progress, the response of the stratum-pile foundation, and environmental changes. The output quantified value not only represents the numerical value of a single time series risk state, but its time series change trend can also reflect the risk evolution law, realizing an accurate and dynamic quantitative characterization of the risk state under the collaborative action of shield tunneling, stratum, and pile foundation, providing a direct basis for subsequent risk level determination.

[0039] (4) Risk level determination In the risk management of tunnel boring machine (TBM) construction, relying solely on collaborative risk quantification values ​​is insufficient to fully reflect the actual threat level of risks. Traditional static threshold determination methods also fail to adapt to the dynamic evolution of risks. Therefore, it is necessary to establish a risk level determination mechanism that combines preset standards with dynamic adjustments, while simultaneously generating accurate early warning information. The premise of risk level determination is the pre-set multi-level risk level classification standards. These standards are not subjectively set but are strictly formulated based on industry norms, engineering design requirements, and risk tolerance levels. Furthermore, the multi-level risk level thresholds in the classification standards are compatible with the judgment thresholds of various monitoring parameters in the hierarchical collaborative evaluation index system, ensuring the standardization and relevance of the standards and providing a unified basis for subsequent level determinations.

[0040] The process by which it dynamically determines the current risk level is as follows: First, a baseline risk level matching process is performed. This step serves as the foundation for level determination and directly links the collaborative risk quantification results with preset threshold standards, specifically through a formula. Implementation, in which Based on the baseline risk level, It is the real-time collaborative risk quantification value output by the multi-field coupling relationship model. These are preset multi-level risk level thresholds; these thresholds are not set independently, but are adapted to the judgment thresholds of each monitoring parameter in the hierarchical collaborative evaluation index system, and strictly follow industry standards and engineering design requirements. The threshold comparison function... By and Compare each item individually to accurately output the corresponding baseline risk level; Based on the baseline level, a risk evolution rate is introduced for dynamic correction, making the level determination more closely reflect the real-time trend of risk changes. The core of the correction process is the calculation and verification of the risk evolution rate, which is expressed by the formula... Received, among which This is the risk quantification value from the previous monitoring period. The interval between adjacent monitoring sessions is directly linked to the dynamically adjusted acquisition frequency in the data acquisition process, ensuring that the rate calculation reflects the real-time nature of data acquisition. Then With respect to the preset rate threshold (Determined by combining factors such as soil stability and pile foundation deformation resistance) Substitute into the corrected formula Through the verification function Adjust the baseline level upwards or downwards to obtain the corrected level. ; Finally, the final risk level is determined through boundary verification, ensuring the rationality and standardization of the assessment results. The verification formula is as follows: ,in , These are the preset minimum and maximum values ​​for the level. Boundary constraint calculations are used to ensure the final level. It is within a reasonable range.

[0041] After obtaining the final risk level, early warning information is generated simultaneously. The information includes the final risk level, real-time collaborative risk quantification value, risk evolution rate, and key exceedance monitoring parameters. Among them, the key exceedance parameters directly correspond to specific parameters in the hierarchical collaborative evaluation indicator system, providing clear guidance for the formulation of subsequent risk control measures and achieving precise connection between risk level determination and early warning.

[0042] This embodiment has broad and significant application prospects in the field of urban underground engineering, especially suitable for scenarios where tunnel boring machines (TBMs) pass through areas with dense existing pile foundations, such as urban rail transit, integrated utility tunnels, and underground commercial streets. With the acceleration of urbanization, the cross-operation between underground engineering construction and existing structures is becoming increasingly frequent, making the need for coordinated risk management of the strata, pile foundations, and environment caused by TBM construction increasingly urgent. This embodiment, through core technologies such as a hierarchical indicator system, dynamic data acquisition, and multi-field coupling quantification, can accurately capture the risk evolution patterns throughout the entire construction process, providing construction units with real-time risk assessment results. This helps them to formulate prevention and control measures in advance, effectively reducing the incidence of accidents such as strata collapse, pile foundation deformation, and damage to surrounding buildings, significantly improving the safety and reliability of engineering construction, and playing an irreplaceable role in ensuring the smooth progress of complex underground projects.

[0043] In terms of industry application and technology promotion, this embodiment also has broad application prospects. Its pre-set standards and compatibility with industry norms and engineering design requirements allow it to be quickly integrated into the risk management systems of different regions and types of tunnel boring machines, enabling its implementation without large-scale adjustments. Simultaneously, the complete technical closed loop it forms—"risk identification - data collection - quantitative evaluation - level determination"—can provide a standardized technical paradigm for the field of underground engineering risk assessment, promoting the transformation from traditional static evaluation to dynamic collaborative evaluation. Furthermore, the technical logic of this invention can be extended to other underground engineering scenarios such as tunnel construction and foundation pit excavation. By adapting to the risk sources and evaluation indicators of different projects, it achieves cross-scenario reuse of technical achievements, possessing significant industry promotion value and technological radiation effects.

[0044] Example 2 Please refer to Figure 6 This embodiment 2 provides a dynamic risk assessment system for shield tunneling-soil-pile foundation collaboration, including: The indicator system construction unit is used to identify, based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, to identify all kinds of core risk sources caused by shield tunneling construction involving strata, pile foundation, construction process and external environment; and to construct a hierarchical collaborative evaluation indicator system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters. The multi-source data acquisition and preprocessing unit is used to simultaneously acquire ground condition data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data; the data acquisition frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; and the acquired multi-source data is preprocessed to generate standardized and effective datasets. The risk quantification unit is used to construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact, and to quantify the dynamic interaction mechanism among various factors. The weight of each evaluation index is determined by the combined weighting method, and the standardized dataset is substituted into the coupling relationship model to dynamically calculate the synergistic risk quantification value, thereby completing the quantitative characterization of the risk state under synergistic effect. The risk level determination unit is used to preset multi-level risk level classification standards based on industry standards, engineering design requirements and risk tolerance levels; and dynamically determine the current risk level and generate early warning information based on the real-time calculated collaborative risk quantification value.

[0045] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement any step of a dynamic risk assessment method for shield tunneling-stratum-pile foundation coordination.

[0046] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0048] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic risk assessment method for the coordinated operation of shield tunneling, strata, and pile foundation, characterized in that, include: S1. Based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, identify all kinds of core risk sources caused by shield tunneling involving strata, pile foundation, construction process and external environment; construct a hierarchical collaborative evaluation index system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters; S2. Synchronously collect ground condition data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data; the data collection frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; the collected multi-source data is preprocessed to generate a standardized and effective dataset. S3. Construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact to quantify the dynamic interaction mechanism among various factors; use a combined weighting method to determine the weights of each evaluation index, and substitute the standardized dataset into the coupling relationship model to dynamically calculate the quantified value of collaborative risk, thereby completing the quantitative characterization of risk status under collaborative action. S4. Based on industry standards, engineering design requirements, and risk tolerance levels, pre-determine multi-level risk classification standards; Based on the real-time calculated collaborative risk quantification value, the current risk level is dynamically determined and early warning information is generated.

2. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The secondary classification indicators in S1 specifically include: Formation characteristic indicators are selected from one or more combinations of formation deformation-related parameters, formation stress state parameters, and groundwater state parameters. Pile foundation response indicators are selected from one or more combinations of pile foundation displacement parameters, pile foundation mechanical response parameters, and pile foundation structural integrity parameters. Construction control indicators are selected from one or more combinations of shield tunneling parameters, support parameters, and construction process-related parameters. External impact indicators are selected from one or more combinations of deformation parameters of surrounding buildings, response parameters of underground pipelines, and surface environmental change parameters.

3. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 2, characterized in that, The comprehensive risk index of the first-level collaborative risk in S1 is a comprehensive quantitative index formed by weighted aggregation of monitoring parameter data of the second-level classification indicators. It is used to characterize the overall risk status under the synergistic effect of multiple factors such as shield tunneling, stratum deformation, pile foundation response and environmental impact. Its aggregation logic covers the coupling risk of construction and stratum, the linkage risk of stratum and pile foundation, the feedback risk of pile foundation and stratum, and the environmental risk derived from the synergistic effect.

4. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The process of dynamically adjusting the data acquisition frequency in S2 according to the construction progress and risk status is as follows: The real-time distance of the shield tunneling distance monitoring section is defined as follows: Real-time distance Positioning data is acquired through the shield tunneling control system; a preset distance threshold is defined as follows. The value is taken as a multiple of the shield tunneling influence radius; the collaborative risk quantification value is defined as... Collaborative risk quantification value The risk level was calculated using a multi-field coupling model of shield tunneling, ground deformation, pile foundation response, and environmental impact; a preset risk level threshold value was defined. This serves as a boundary standard for adjusting monitoring frequency based on risk status; the construction progress impact coefficient is defined as... The risk state influence coefficient is defined by the complexity of engineering geological conditions. The deformation resistance of the column base structure is determined by its ability to resist deformation. Data acquisition frequency The dynamic adjustment is determined by the following formula: ， in, The hyperbolic tangent function is used to... and The value is mapped to The interval allows for smooth adjustment of the data acquisition frequency based on construction progress and risk status; when the real-time distance... hour, The closer the construction progress is to the monitoring section, the higher the data collection frequency should be compared to the baseline value. When the collaborative risk quantification value Approaching hour, The closer the risk level is to 1, the higher the data collection frequency should be compared to the baseline value. Ultimately, this allows the data acquisition frequency to be dynamically matched with the intensity of the synergistic effect between the tunnel boring machine, the stratum, and the pile foundation, ensuring both the timeliness of monitoring during high-risk phases and avoiding excessive data collection during non-risk phases.

5. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The standardized effective dataset in S2 is presented in the form of a data matrix. The row dimension of the data matrix corresponds to the monitoring time series, and the column dimension corresponds to all the specific monitoring parameters of the three levels in the hierarchical collaborative evaluation index system. Each matrix element is the standardized processing result of the corresponding monitoring parameter under a single monitoring time series. The row index of the data matrix is ​​the monitoring timestamp, which corresponds one-to-one with the time nodes of the shield tunneling positioning data and the stratum-pile foundation synergy, ensuring the consistency of the data time sequence; the column index is the monitoring parameter identifier, which contains the secondary classification index category to which the parameter belongs and the parameter name information, realizing the association between the parameter and the evaluation index system. The standardization results of each element in the matrix are determined as follows: Based on the preset judgment threshold of the corresponding monitoring parameters, a normalization algorithm is used to convert the raw collected data into dimensionless values, and the range of these dimensionless values ​​is mapped to... The interval is defined as follows: 0 corresponds to the ideal safe value of the monitoring parameter, and 1 corresponds to the warning threshold value of the monitoring parameter.

6. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The multi-field coupling relationship model in S3 is specifically as follows: The multi-field coupling relationship model is constructed through a three-step progressive logic of "parameter correlation quantification - synergistic effect aggregation - risk evolution characterization", as follows: The first step is parameter correlation quantization to characterize the fundamental interaction relationships between pairs of fields: ， ， ， ， in, , , , These are the pairwise coupling coefficients for shield tunneling-soil deformation, soil deformation-pile foundation response, pile foundation response-environmental impact, and environmental impact-shield tunneling, respectively. Their values ​​are mapped to the [0,1] interval to quantify the interaction strength between the two field parameters. , , , These are the comprehensive parameters of shield tunneling construction, comprehensive parameters of ground deformation, comprehensive parameters of pile foundation response, and comprehensive parameters of environmental impact in the standardized effective dataset. Their values ​​directly correspond to the three-level monitoring parameters of the hierarchical evaluation index system. The second step involves synergistic effect aggregation, integrating the pairwise interactions of multiple fields to form a comprehensive coupling strength: ， in, The multi-field integrated coupling strength is obtained by taking the square root of the average of the squares of the pairwise coupling coefficients, which preserves the independent contribution of each field coupling while reflecting the overall characteristics of the synergistic effect. The third step is to characterize the risk evolution and output a collaborative risk quantification value by combining time-series dynamic characteristics: ， in, To coordinate risk quantification values, directly connect to the subsequent risk level determination process; It is a time partial derivative operator that characterizes the temporal rate of change of the composite parameters.

7. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The calculation process for the weights of each evaluation index in S3 is as follows: The first step is to calculate the dynamic volatility of the indicator and quantify the risk sensitivity of a single indicator: ， in, For the first The dynamic fluctuation of each of the three-level monitoring indicators; For this indicator in the 1st Standardized time-series data; , These are the standardized maximum and minimum values ​​of the indicator during the monitoring period; This is the standardized mean of the indicator; The second step is to calculate the degree of synergistic correlation between indicators, and to quantify the strength of interdependence among indicators: ， in, For the first The degree of synergistic correlation among the indicators; This refers to the total number of three-level monitoring indicators in the hierarchical evaluation indicator system. For the first The first indicator and the first Covariance of each indicator; , These are the variances of the two indicators, respectively. The third step is to calculate the final weight of the indicators and integrate sensitivity and synergy to form a comprehensive weight: ， in, For the first The final weights of the three-level monitoring indicators range from (0,1); The total number of evaluation indicators participating in the weight calculation; the numerator is the product of volatility and correlation, which comprehensively reflects the "independent sensitivity" and "co-dependency" of the indicators; the denominator is the sum of the numerator terms corresponding to all indicators, which realizes the normalization of weights.

8. The dynamic risk assessment method for shield tunneling-soil-pile foundation coordination according to claim 1, characterized in that, The process of dynamically determining the current risk level in S4 is as follows: First, a baseline risk level matching process is performed. This step serves as the foundation for level determination and directly links the collaborative risk quantification results with preset threshold standards, specifically through a formula. Implementation, in which Based on the baseline risk level, It is the real-time collaborative risk quantification value output by the multi-field coupling relationship model. These are preset multi-level risk level thresholds; these thresholds are not set independently, but are adapted to the judgment thresholds of each monitoring parameter in the hierarchical collaborative evaluation index system, and strictly follow industry standards and engineering design requirements. The threshold comparison function... By and Compare each item individually to accurately output the corresponding baseline risk level; Based on the baseline level, a risk evolution rate is introduced for dynamic correction, making the level determination more closely reflect the real-time trend of risk changes. The core of the correction process is the calculation and verification of the risk evolution rate, which is expressed by the formula... Received, among which This is the risk quantification value from the previous monitoring period. The interval between adjacent monitoring sessions is directly linked to the dynamically adjusted acquisition frequency in the data acquisition process, ensuring that the rate calculation reflects the real-time nature of data acquisition. Then With respect to the preset rate threshold Substitute into the correction formula Through the verification function Adjust the baseline level upwards or downwards to obtain the corrected level. ; Finally, the final risk level is determined through boundary verification to ensure the rationality and standardization of the assessment results; The approved formula is ,in , These are the preset minimum and maximum values ​​for the level. Boundary constraint calculations are used to ensure the final level. It is within a reasonable range.

9. A dynamic risk assessment system for shield tunneling-soil-pile foundation collaboration, characterized in that, include: The indicator system construction unit is used to identify, based on the basic data of engineering geology, hydrogeology, pile foundation-related structures and surrounding environment of the shield tunneling construction area, to identify all kinds of core risk sources caused by shield tunneling construction involving strata, pile foundation, construction process and external environment; and to construct a hierarchical collaborative evaluation indicator system including a first-level collaborative risk comprehensive index, a second-level classification index and a third-level specific monitoring parameters. The multi-source data acquisition and preprocessing unit is used to simultaneously acquire ground state data, pile foundation structure response data, shield tunneling construction parameter data, and surrounding environmental impact data. The data collection frequency is dynamically adjusted according to the construction progress and risk status to meet the needs of real-time evaluation; the collected multi-source data is preprocessed to generate a standardized and effective dataset. The risk quantification unit is used to construct a multi-field coupling relationship model of shield tunneling construction, ground deformation, pile foundation response, and environmental impact, and to quantify the dynamic interaction mechanism among various factors. The weight of each evaluation index is determined by the combined weighting method, and the standardized dataset is substituted into the coupling relationship model to dynamically calculate the synergistic risk quantification value, thereby completing the quantitative characterization of the risk state under synergistic effect. The risk level determination unit is used to pre-set multi-level risk level classification standards based on industry standards, engineering design requirements, and risk tolerance levels. Based on the real-time calculated collaborative risk quantification value, the current risk level is dynamically determined and early warning information is generated.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor using a dynamic risk assessment method for shield tunneling-soil-pile foundation coordination as described in any one of claims 1-8.

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