A foundation pit slope risk assessment method, system, computer device and medium

Abstract

The application provides a foundation pit slope risk assessment method, system, computer device and medium, belongs to the field of geotechnical engineering risk assessment, and comprises the following steps: collecting indexes of a foundation pit slope to be evaluated; calculating the influence weight of each index by analyzing and calculating the influence contribution of the relative safety response amount of each index; dividing the risk level of each index to obtain a risk threshold; selecting an unascertained measure function family based on the attribute of each index, and inputting the risk threshold to obtain the unascertained measure function of each index relative to multiple different risk levels; determining an unascertained measure evaluation matrix according to the numerical value of each index and the unascertained measure function; merging the influence weight of each index and the unascertained measure evaluation matrix to obtain a comprehensive unascertained measure vector; determining the risk level of the slope to be evaluated by a confidence recognition criterion, and then determining the risk assessment result of the foundation pit slope to be evaluated. The method improves the credibility of the risk assessment conclusion.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering risk assessment, specifically involving a method, system, computer equipment, and medium for assessing the risk of foundation pit slopes. Background Technology

[0002] With the continuous increase in the intensity of urban underground space development, deep foundation pit construction is widely used in projects such as subway stations, underground complexes, and underground utility tunnels. Deep foundation pit projects are generally characterized by large excavation depths, sensitive surrounding environments, complex geological and hydrological conditions, and strong construction disturbances. Instability of the foundation pit slopes or walls may lead to surface subsidence outside the pit, damage to adjacent buildings and municipal pipelines, and even casualties and significant economic losses. Therefore, scientific risk assessment and dynamic early warning for deep foundation pits are of great significance.

[0003] To address these needs, existing researchers use the Analytic Hierarchy Process (AHP) for risk assessment, decomposing the slope risk assessment objectives into multiple layers, including the target layer, criterion layer, and indicator layer. Assessors then use a 1-9 scale to compare the importance of factors within the same layer pairwise, constructing a judgment matrix. The judgment matrix is ​​then tested for consistency, and the weights of each indicator are calculated using the eigenvalue method. Finally, the indicator scores are weighted and summed to obtain the risk assessment result and determine the risk level. However, this method of importance assessment relies on expert judgment, and differences in the perception of indicator importance among different experts can directly lead to inconsistencies in the judgment matrix. Furthermore, the assessment process lacks objective data support, has poor traceability, and low reliability. Summary of the Invention

[0004] To address the issue of low reliability in existing risk assessments, this invention provides a method, system, computer equipment, and medium for assessing the risk of foundation pit slopes.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for assessing the risk of foundation pit slopes includes: Collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated. Using the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, the influence contribution of each indicator relative to the safety response quantity is calculated through contribution rate analysis to obtain the influence weight of each indicator. Each indicator is classified into risk levels, resulting in multiple risk thresholds for each indicator with different risk levels. Based on the correlation between each indicator and the safety response quantity, a family of uncertain measure functions is selected. The risk thresholds for multiple risk levels are then substituted into the family of uncertain measure functions corresponding to each indicator, resulting in uncertain measure functions for each indicator relative to multiple risk levels. The uncertain measure evaluation matrix of each indicator is determined based on the value of each indicator and the uncertain measure function. The influence weight of each indicator is combined with the uncertain measure evaluation matrix to obtain the comprehensive uncertain measure vector of the foundation pit slope to be evaluated. The risk level of the slope to be assessed is determined by the confidence level identification criterion and the comprehensive unknown measure vector. Based on the risk level of the slope to be assessed and the comprehensive unknown measure vector, the risk assessment result of the foundation pit slope to be assessed is determined.

[0006] Optionally, the method for assessing the risk of foundation pit slopes provided by the present invention further includes: A foundation pit model is constructed using numerical simulation to evaluate the foundation pit slope. Multiple values ​​are set for each indicator. Multiple parameter combinations are obtained by combining the multiple values ​​corresponding to each indicator through an orthogonal experimental table. Each parameter combination is substituted into the foundation pit model to obtain the safety response quantity corresponding to each parameter combination. The sum of squared deviations of each indicator is calculated based on the mean of the safety response quantity under multiple different values ​​of each indicator; The influence weight of an indicator is determined by the proportion of the sum of squared deviations of each indicator to the sum of squared deviations of all indicators.

[0007] Optionally, the method for assessing the risk of foundation pit slopes provided by the present invention further includes: Optionally, when the foundation pit model does not converge, the influence weight of each indicator is determined by the entropy weight method. Optionally, the foundation pit slope risk assessment method provided by this invention further includes:

[0008] Based on the standard limit ratio method, multiple risk thresholds are generated proportionally from the preset control value of each indicator.

[0009] Optionally, the method for assessing the risk of foundation pit slopes provided by the present invention further includes: When the correlation attribute is positively correlated with the safety factor or negatively correlated with the failure probability, the ascending half-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions of the index. When the correlation attribute is that the indicator is negatively correlated with the safety factor or positively correlated with the failure probability, the descending half-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions of the indicator. When the correlation attribute is that the indicator is moderately correlated with the safety factor or failure probability, the trapezoidal distribution or normal distribution is used as the family of unknown measure functions of the indicator.

[0010] Optionally, the risk assessment results also include key risk indicators. The risk assessment method for foundation pit slopes provided by this invention further includes: Each indicator is sorted according to its corresponding influence weight, and the indicators with the highest influence weight in the sorting results are selected as key risk indicators.

[0011] Optionally, the method for assessing the risk of foundation pit slopes provided by the present invention further includes: Obtain engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and mechanical monitoring data indicators for the next time series of the foundation pit slope to be evaluated. Determine the comprehensive unknown measure vector of the foundation pit slope to be evaluated in the next time series based on multiple indicators for the next time series. When the comprehensive unknown measure vector of the current time series and the comprehensive unknown measure vector of the next time series of the foundation pit slope to be evaluated are used to determine the change in the comprehensive unknown measure vector, an early warning signal for the foundation pit slope to be evaluated is generated when the change in the comprehensive unknown measure vector is higher than the preset early warning threshold.

[0012] This invention also provides a foundation pit slope risk assessment system, comprising: The indicator acquisition module is used to collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and mechanical monitoring data indicators of the foundation pit slope to be evaluated. The weight calculation module is used to calculate the influence contribution of each indicator relative to the safety response quantity by taking the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, and to obtain the influence weight of each indicator by contribution rate analysis. The unknown measure calculation module is used to classify the risk level of the slope to be evaluated and obtain multiple risk thresholds with different risk levels. Based on the correlation attribute between each indicator and the safety response quantity, an unknown measure function family is selected. The risk thresholds with multiple risk levels are substituted into the unknown measure function family corresponding to each indicator to obtain the unknown measure function of each indicator relative to multiple different risk levels. The unknown measure evaluation matrix of each indicator is determined according to the value of each indicator and the unknown measure function. The influence weight of each indicator is combined with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be evaluated. The assessment result generation module is used to determine the risk level of the slope to be assessed by using the confidence level identification criterion and the comprehensive unknown measure vector, and to determine the risk assessment result of the foundation pit slope to be assessed based on the risk level of the slope to be assessed and the comprehensive unknown measure vector.

[0013] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in a method for assessing the risk of foundation pit slope.

[0014] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any step of a method for assessing the risk of a foundation pit slope.

[0015] The method for risk assessment of foundation pit slope provided by this invention has the following beneficial effects: Because this invention employs contribution rate analysis, using the objective safety response quantity of the slope safety factor or instability probability as a benchmark, it quantifies the specific impact contribution of each assessment indicator, thereby obtaining influence weights entirely based on data and models. This completely abandons the traditional method of relying on expert subjective experience to judge the importance of indicators, fundamentally solving the defects of insufficient consistency and traceability of assessment results caused by subjectivity. Furthermore, this invention can construct an uncertain measure function for each indicator and synthesize a comprehensive measure vector, then determine the risk level based on the confidence level identification criterion. This achieves the quantitative processing of uncertainty in the process of multi-source indicator information fusion, ensuring that the final risk assessment results have a solid objective data foundation and clear mathematical logic support, significantly improving the credibility of the risk assessment conclusions. Attached Figure Description

[0016] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of a foundation pit slope risk assessment method provided in an embodiment of the present invention; Figure 2 This is an example of the overall process provided in the embodiments of the present invention; Figure 3 This is an example of a risk assessment index system for foundation pit slopes provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an unknown measure function provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the contribution rate weight distribution provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the comprehensive measurement vector and risk level determination provided in the embodiments of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0019] Existing risk assessment methods often employ expert scoring, hierarchical analysis, or fuzzy comprehensive evaluation, which tend to rely on experience in determining weights, resulting in insufficient consistency and traceability of assessment results. Furthermore, they are difficult to quantitatively characterize the differences in sensitivity of system security response to changes in factors, which is not conducive to identifying key risk factors.

[0020] In addition, deep foundation pit monitoring data has uncertain characteristics such as random fluctuations, measurement errors, missing data and sudden changes in working conditions. Traditional deterministic threshold discrimination is difficult to express the confidence level of belonging to a certain risk level, and the ability to integrate multi-source information is insufficient, resulting in insufficient utilization of objective data.

[0021] Considering that contribution rate analysis can quantify the impact of each factor on the safety response through orthogonal experiments, numerical simulations, or statistical analysis, thereby achieving objective determination of weights and reflecting the sensitivity of factors; and that the unknown measure theory can express the degree of membership of the index value to each risk level in the form of a measure, such as confidence level, which is suitable for handling uncertain information and achieving level determination through confidence level identification criteria, this invention provides a risk assessment method for foundation pit slope engineering based on contribution rate and unknown measure model. This method solves the problems of strong subjectivity in weight determination, difficulty in quantifying sensitivity differences, and insufficient integration of uncertain data in the existing technology, and achieves objective, quantitative, and traceable assessment of foundation pit slope instability risk, and outputs key risk factors for risk prevention and dynamic early warning.

[0022] Example 1 This invention provides a method for risk assessment of foundation pit slopes, specifically as follows: Figure 1 As shown, it includes the following steps: Step 11: Collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated.

[0023] Specifically, such as Figure 2 As shown, firstly, measured data of multiple indicators of the foundation pit slope to be evaluated are collected, and the attribute type of the indicator is labeled. For example, the smaller the indicator, the safer it is; the larger the indicator, the safer it is; or the moderate attribute, the indicator has no significant impact on the magnitude of the safety response. Thus, a foundation pit slope engineering risk assessment index system is constructed for the foundation pit slope to be evaluated.

[0024] Among them, such as Figure 3As shown, based on the physical meaning of each indicator, primary indicators can be categorized and constructed, including engineering geological conditions, hydrogeological conditions, construction factors or construction data, and mechanical monitoring data. Corresponding secondary indicators can be set under each primary indicator. For example, the internal friction angle, cohesion, and density of soil and rock can be used as secondary indicators under engineering geological conditions; the groundwater level variation and permeability coefficient can be used as secondary indicators under hydrogeological conditions; the single excavation depth, excavation speed, and support structure construction quality can be used as secondary indicators under construction factors; and the horizontal displacement rate at the slope crest, the deep horizontal displacement rate, and the anchor cable axial force loss rate can be used as secondary indicators under monitoring data. The above data can be obtained from the investigation report, test results, construction records, and monitoring system data of the slope to be evaluated, etc., and this invention does not impose any limitations.

[0025] Step 12: Using the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, calculate the influence contribution of each indicator relative to the safety response quantity through contribution rate analysis, and obtain the influence weight of each indicator.

[0026] The influence weights can be determined through the following steps: Step 121: Construct a foundation pit model of the foundation pit slope to be evaluated through numerical simulation.

[0027] Step 122: Set multiple values ​​for each indicator, combine the multiple values ​​corresponding to each indicator using an orthogonal experimental table to obtain multiple parameter combinations, substitute each parameter combination into the foundation pit model to obtain the safety response quantity corresponding to each parameter combination.

[0028] Step 123: Calculate the sum of squared deviations of each indicator based on the mean of the safety response quantity under multiple different values ​​of each indicator.

[0029] Step 124: Determine the influence weight of each indicator by the proportion of the sum of squared deviations of each indicator to the sum of squared deviations of all indicators.

[0030] Specifically, once the measured values ​​of each secondary indicator are determined, a safety factor is used. or probability of instability As a safety response quantity, the contribution of each indicator to the response quantity is calculated through contribution rate analysis to obtain the contribution rate, which is then normalized to form a weight vector. The normalization process is shown in formula (1): (1) Where n is the total number of secondary indicators, Let i be the weight vector of the i-th secondary indicator. This represents the set of weight vectors for all secondary indicators. For example, a foundation pit model of the slope to be evaluated can be constructed through numerical simulation. n secondary indicators are selected as factors, and for each factor, three or more values ​​are determined as levels based on measured values. Parameter combinations are formed using orthogonal experimental tables. Then, each combination is substituted into the foundation pit model to calculate the corresponding safety response quantity, and the sum of squared deviations for each factor is calculated. , by sum of squared deviations Calculate contribution rate As shown in formula (2):

[0031] (2) Next, the contribution rate of each indicator is normalized to obtain the weight vector of that indicator. As shown in formula (3): (3) Furthermore, when the safe response quantity of the foundation pit slope to be evaluated cannot be determined, the alternative impact weights can be determined through the following steps: Step 125: When the foundation pit model does not converge, determine the influence weight of each indicator by means of the coefficient of variation method or the entropy weight method.

[0032] Specifically, when the safety response quantity cannot be obtained for contribution rate analysis, such as when the slope of the foundation pit to be evaluated is in the early stage of construction and the data is insufficient to construct a foundation pit model, or when the geological and hydrological conditions of the foundation pit slope to be evaluated are too complex and the constructed foundation pit model cannot accurately describe its nonlinear relationship, resulting in non-convergence of numerical simulation iterations or serious distortion of results, this invention can also use the objective entropy weight method to calculate the alternative weight vector. And through the alternative weight vector Further analysis will be conducted.

[0033] For example, first, obtain the actual measurement data from the early monitoring of the foundation pit to be evaluated, or historical data of foundation pit slope samples similar to the foundation pit to be evaluated, to construct an initial data matrix. For example, if a total of m samples are obtained, and each sample contains n evaluation indicators, the initial matrix is... ,in, This represents the measured value of the j-th indicator for the i-th sample.

[0034] Next, the initial matrix is ​​standardized to obtain the standardized matrix. The specific standardized formulas are shown in formulas (4) and (5): (4) (5) in, Let j be the standardized measured value of the j-th index of the i-th sample in the standardization matrix. The standardized formula for a positive correlation indicator, i.e., the larger the value, the lower the risk of the foundation pit slope, is shown in formula (4). The standardized formula for a negatively correlated indicator, i.e., the smaller the value, the lower the risk of the foundation pit slope, is shown in formula (5). The maximum value of the j-th indicator among all samples. Let j be the minimum value of the j-th indicator among all samples. Then, calculate the feature weight of each indicator. As shown in formula (6):

[0035] (6) And combine the characteristic weight of each indicator Calculation indicators The information entropy is shown in formula (7): (7) For formula (7) Consider the existence For the case where it is 0, the corresponding provisions are specified. Replace it with 0, and finally, determine the influence weight of the replacement indicator based on the information entropy of each indicator. As shown in formula (8): (8) Then, a substitution weight vector is constructed from the influence weights of the substitutions for each indicator. This is then used for subsequent merging calculations of unknown measure vectors.

[0036] Step 13: Classify the risk level of each indicator to obtain multiple risk thresholds for each indicator with different risk levels; select a family of unknown measure functions based on the correlation attribute between each indicator and the safety response quantity, and substitute the multiple risk thresholds with different risk levels into the family of unknown measure functions corresponding to each indicator to obtain the unknown measure functions of each indicator relative to multiple different risk levels; determine the unknown measure evaluation matrix of each indicator based on the value of each indicator and the unknown measure function; merge the influence weight of each indicator with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be evaluated.

[0037] The risk threshold can be determined through the following steps: Step 131: Based on the standard limit ratio method, multiple risk thresholds are generated proportionally from the preset control value of each indicator.

[0038] Specifically, after calculating the weight vector for each indicator, thresholds are set for multiple risk levels for each indicator to obtain the risk range of that indicator. For example, four threshold nodes are set for each indicator according to low risk, lower risk, medium risk, higher risk, and high risk, thus forming a five-level risk level space. The threshold nodes can be generated by using the standard alarm value or control value of the indicator as a benchmark and generating four threshold nodes according to a preset ratio coefficient, or by using the quantiles of historical engineering or slope engineering data of the indicator over a period of time, and then adjusting the threshold according to the importance level of the project to determine the threshold nodes.

[0039] The family of unknown measure functions can be selected through the following steps: Step 132: When the correlation attribute is positively correlated with the safety factor or negatively correlated with the failure probability, the ascending half-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions for the index.

[0040] Step 133: When the correlation attribute is that the index is negatively correlated with the safety factor or positively correlated with the failure probability, the descending half-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions for the index.

[0041] Step 134: When the correlation attribute indicates that the index has a moderate correlation with the safety factor or failure probability, the trapezoidal distribution or normal distribution is used as the family of unknown measure functions for the index.

[0042] Specifically, once the threshold node for each indicator is determined, a family of unknown measurement functions is selected based on the attribute of each indicator. For example, indicators that indicate greater safety with smaller thresholds use a descending half-trapezoidal distribution or a piecewise linear function; those that indicate greater safety with larger thresholds use an ascending half-trapezoidal distribution or a piecewise linear function; and moderate indicators use a trapezoidal distribution or a normal distribution. The threshold node for each indicator is then substituted into the family of unknown measurement functions to construct the measurement function for that indicator for level five risk. Furthermore, any index i must satisfy... For example, a descending trapezoidal unknown measure function is constructed for the horizontal displacement rate index at the top of the slope, and piecewise linear interpolation is performed using adjacent risk level threshold intervals to determine the measure of the index for each risk level.

[0043] Next, the measured values ​​of each indicator will be... Substitute into the measurement function of this index In this process, the unknown measure evaluation matrix of the indicator is obtained. As shown in formula (9): (9) And an evaluation matrix based on the unknown measure of the different risk levels of this indicator. Determine the evaluation matrix of the unknown measure of this indicator. As shown in formula (10): (10) Finally, the evaluation matrix of the unknown measure for each indicator is... The combined unknown measure vector of the slope to be evaluated is obtained by weighted summation with the corresponding weight vector. As shown in formula (11): (11) in, Evaluation matrix of unknown measures for each indicator The result is a weighted sum of the sum and the corresponding weight vector.

[0044] Step 14: Determine the risk level of the slope to be assessed using the confidence level identification criterion and the comprehensive unknown measure vector. Based on the risk level of the slope to be assessed and the comprehensive unknown measure vector, determine the risk assessment result of the foundation pit slope to be assessed. Furthermore, the risk assessment result also includes key risk indicators, which are determined by ranking each indicator according to its corresponding influence weight, and then selecting the indicators with the highest influence weight from the ranking results.

[0045] Specifically, once the comprehensive unknown measure vector is determined, a confidence level identification criterion is introduced, based on the confidence level. Determine the risk level As shown in formula (12): (12) Among them, confidence level The preferred value is 0.6, but it can also be set by those skilled in the art based on actual needs. Furthermore, the weight can be adjusted... The top N secondary indicators with the highest weights are sorted and output as key risk management indicators, i.e., critical risk factors, where N is an integer greater than or equal to 1, which can be set by those skilled in the art based on actual needs. Finally, the risk levels are determined. Comprehensive Measure Vector and by weight The key risk factors obtained through sorting are used to generate a risk assessment report, which is then displayed. In addition, the risk assessment report may include basic information about the foundation pit, measured data for each indicator, indicator weight distribution, evaluation matrix of unknown measures for single indicators, comprehensive unknown measure vector, risk level conclusion, and key risk factors and recommended measures.

[0046] Step 15: Obtain the engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and mechanical monitoring data indicators for the next time series of the foundation pit slope to be evaluated. Determine the comprehensive unknown measure vector of the foundation pit slope to be evaluated in the next time series based on multiple indicators for the next time series. When the comprehensive unknown measure vector of the foundation pit slope to be evaluated in the current time series and the comprehensive unknown measure vector of the next time series are used to determine the change in the comprehensive unknown measure vector, when the change in the comprehensive unknown measure vector is higher than the preset warning threshold, generate a warning signal for the foundation pit slope to be evaluated.

[0047] Specifically, this invention can also provide risk warnings for the slope of the foundation pit to be assessed through dynamic evaluation. For example, it repeatedly calculates the comprehensive unknown measure vector according to a preset time series cycle and compares the changes in the comprehensive unknown measure vector between two adjacent cycles. As shown in formula (13): (13) When the comprehensive unknown measure vector of period t Combined with the unknown measure vector of period t-1 Change Greater than the preset threshold At that time, or the risk level Compared to the previous cycle when the adjustment was raised, an early warning was triggered, and the changes in key risk factors were output.

[0048] In summary, this invention obtains weights through contribution rate analysis, ensuring that the weights reflect the true sensitivity of factors to the safety response quantity and guaranteeing the objectivity of the weights for each indicator. Furthermore, by expressing the confidence level through an uncertain measure and achieving multi-indicator fusion, it can integrate uncertain information. Moreover, this invention can identify and output key risk factors, facilitating targeted control and ensuring the traceability and controllability of risk assessment. In addition, the overall process, inputs, outputs, and criteria of this invention are clearly defined, facilitating dynamic assessment and early warning by computer systems and making it easy to implement in engineering projects.

[0049] Example 2 Based on Example 1, taking a deep foundation pit project for a subway station in a certain city as an example, this invention also provides a specific risk assessment process for foundation pit slopes. The excavation depth of the deep foundation pit project for the subway station in this city is 18.5m, and the support method is diaphragm wall + internal bracing. The site strata, from top to bottom, consist of miscellaneous fill, silty clay, and silty sand layers, with thin layers of weak soil in some areas. The groundwater level is 2.0-3.5m deep, significantly affected by rainfall, and the surrounding environment is sensitive, being adjacent to municipal roads and pipelines.

[0050] This embodiment selects 11 secondary indicators: A1 internal friction angle, A2 cohesion, A3 unit weight; B1 groundwater level variation, B2 permeability coefficient; C1 single excavation depth, C2 excavation speed, C3 support structure construction quality; D1 slope top horizontal displacement rate, D2 deep horizontal displacement rate, D3 anchor cable axial force loss rate. When excavation reaches the 16m stage, measured values ​​of these 11 secondary indicators are collected, where A1 internal friction angle φ is 22.5°, A2 cohesion c is 18 kPa, and A3 unit weight γ is 18.7 kN / m. 3 The groundwater level variation Δh in B1 is 1.5m, and the permeability coefficient k in B2 is 3.2×10⁻⁶. -4 cm / s, C1 single excavation depth Hs is 2.2m, C2 excavation speed v exc The construction quality Q of the C3 support structure is 82 points, with a slope speed of 1.1 m / d. The horizontal displacement rate v at the top of slope D1 is... top The horizontal displacement rate v at depth D2 is 3.2 mm / d. deep The axial force loss rate η of the D3 anchor cable is 2.6 mm / d and 8.5%.

[0051] Subsequently, a diaphragm wall + internal support foundation pit model was established using numerical simulation, and a safety factor was selected. This is the response quantity. The contribution rate of each indicator is obtained by setting the levels of each factor and calculating the sum of squared deviations using an orthogonal experimental design. Specifically, as follows... Figure 4 As shown, A1 is 25, A2 is 18, A3 is 5, B1 is 12, B2 is 4, C1 is 8, C2 is 6, C3 is 7, D1 is 22, D2 is 10, and D3 is 3. The contribution rate normalization yields the overall weight vector W as (0.208, 0.150, 0.042, 0.100, 0.033, 0.067, 0.050, 0.058, ...

[0052] 0.183,0.083,0.025), among which, w(A1)=0.208, w(A2)=0.150, w(A3)=0.042, w(B1)=0.100, w(B2)=0 .033, w(C1)=0.067, w(C2)=0.050, w(C3)=0.058, w(D1)=0.183, w(D2)=0.083, w(D3)=0.025.

[0053] Subsequently, the unknown measure function for each indicator was selected as follows: Figure 5As shown, specifically, for the five-level risk classification, this embodiment uniformly adopts a family of piecewise linear functions to construct the evaluation matrix. Typically, a semi-trapezoidal distribution is used in the extreme value intervals at both ends, and a triangular distribution is used in the middle transition interval. Taking the internal friction angle (A1) and the horizontal displacement rate at the top of the slope (D1) as examples, firstly, based on the standard limits, statistical values ​​of similar projects, and the control indicators of the deep foundation pit project of the city's subway station, four levels of risk thresholds for the internal friction angle and the horizontal displacement rate at the top of the slope are generated using the standard limit ratio method or the statistical quantile method, and adjusted according to the importance level of the project. For example, the correlation attribute of the horizontal displacement rate at the top of the slope (D1) is that the smaller the value, the safer it is. The four thresholds are set as t1=1.0, t2=2.0, t3=3.5, and t4=5.0, respectively. A five-level measurement function is constructed using a piecewise linear approach; substituting the horizontal displacement rate at the top of the slope (vtop) of 3.2 mm / d into the value, an example measurement vector is obtained. As shown in formula (9):

[0054] (9) The correlation attribute of the internal friction angle A1 is that the larger the value, the safer it is. The four thresholds are set as s1=18, s2=20, s3=23, and s4=26. A five-level measure function is constructed in a piecewise linear manner. Substituting the internal friction angle φ of A1 as 22.5°, the example measure vector is obtained. As shown in formula (10): (10) The remaining indicators are constructed with measure functions according to their corresponding attributes, and measure values ​​are calculated to form an 11×5 evaluation matrix R, where the sum of measures in each row is 1. Then, the comprehensive unknown measure vector for the deep foundation pit project of the city's subway stations is calculated according to formula (6). With a confidence level of 0.6, the cumulative measures are calculated as follows: u1 = 0.13; u1 + u2 = 0.39; u1 + u2 + u3 = 0.78 ≥ 0.6. Therefore, the risk level is... The risk level is 3, indicating that the deep foundation pit project for the city's subway station is of medium risk. Furthermore, the top three risk factors (A1 internal friction angle, D1 slope top horizontal displacement rate, and B1 groundwater level change amplitude) are ranked by weight among the 11 indicators, and targeted risk management recommendations are generated.

[0055] Example 3 This invention also provides a foundation pit slope risk assessment system, comprising: The indicator acquisition module is used to collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated. The weight calculation module is used to calculate the influence contribution of each indicator relative to the safety response quantity by taking the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, and to obtain the influence weight of each indicator by contribution rate analysis. The unknown measure calculation module is used to classify the risk level of the slope to be evaluated and obtain multiple risk thresholds with different risk levels. Based on the correlation attribute between each indicator and the safety response quantity, an unknown measure function family is selected. The risk thresholds with multiple risk levels are substituted into the unknown measure function family corresponding to each indicator to obtain the unknown measure function of each indicator relative to multiple different risk levels. The unknown measure evaluation matrix of each indicator is determined according to the value of each indicator and the unknown measure function. The influence weight of each indicator is combined with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be evaluated. The assessment result generation module is used to determine the risk level of the slope to be assessed by using the confidence level identification criterion and the comprehensive unknown measure vector, and to determine the risk assessment result of the foundation pit slope to be assessed based on the risk level of the slope to be assessed and the comprehensive unknown measure vector.

[0056] Specifically, this invention can be implemented by a computer program and deployed on a foundation pit risk management platform, including a data input module that receives survey, construction, and monitoring data and processes anomalies and missing data, and outputs data based on contribution rate analysis. Or output alternative weights under the condition of lack. The system comprises a weight calculation module, a threshold and measurement function library module that calls functions based on indicator attributes and configures thresholds, a measurement matrix construction module that forms R and performs consistency verification, a comprehensive evaluation module that calculates U and identifies the output level based on confidence level, and a result output and warning module that generates reports, visualizes the results, and triggers warnings based on changes in ΔU and the level. This system can automatically and cyclically execute the evaluation process by day, by construction step, or by data update frequency. It can also repeatedly execute the evaluation process at preset cycles through a dynamic warning module, calculating the change ΔU of the comprehensive unknown measurement vector between adjacent cycles. When ΔU ≥ θ or the risk level... Compared to the previous cycle when the adjustment was raised, an early warning was triggered, and the changes in key risk factors were output.

[0057] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a method for risk assessment of foundation pit slopes. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0058] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a method for assessing the risk of a foundation pit slope. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0059] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0063] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for risk assessment of foundation pit slopes, characterized in that, include: Collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated. Using the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, the influence contribution of each indicator relative to the safety response quantity is calculated through contribution rate analysis to obtain the influence weight of each indicator. Each indicator is classified into risk levels, resulting in multiple risk thresholds for each indicator with different risk levels; Based on the correlation attribute between each indicator and the safety response quantity, a family of unknown measurement functions is selected. The risk thresholds of multiple different risk levels are substituted into the family of unknown measurement functions corresponding to each indicator to obtain the unknown measurement functions of each indicator relative to multiple different risk levels. The unknown measurement evaluation matrix of the indicator is determined according to the value of each indicator and the unknown measurement function. The influence weight of each indicator is combined with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be evaluated; The risk level of the slope to be assessed is determined by the comprehensive unknown measure vector using the confidence level identification criterion. Based on the risk level of the slope to be assessed and the comprehensive unknown measure vector, the risk assessment result of the foundation pit slope to be assessed is determined.

2. The method for risk assessment of foundation pit slope according to claim 1, characterized in that, Using the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, the influence contribution of each indicator relative to the safety response quantity is calculated through contribution rate analysis, and the influence weight of each indicator is obtained, including: A foundation pit model of the foundation pit slope to be evaluated is constructed through numerical simulation; Multiple values ​​are set for each indicator. Multiple parameter combinations are obtained by combining the multiple values ​​corresponding to each indicator through an orthogonal experimental table. Each parameter combination is substituted into the foundation pit model to obtain the safety response quantity corresponding to each parameter combination. The sum of squared deviations of each indicator is calculated based on the mean of the safety response quantity under multiple different values ​​of each indicator; The influence weight of an indicator is determined by the proportion of the sum of squared deviations of each indicator to the sum of squared deviations of all indicators.

3. The method for assessing the risk of foundation pit slope according to claim 2, characterized in that, After constructing the foundation pit model of the foundation pit slope to be evaluated through numerical simulation, the method further includes: when the foundation pit model does not converge, determining the influence weight of each indicator by means of entropy weight method.

4. The method for risk assessment of foundation pit slope according to claim 1, characterized in that, Each indicator is classified into risk levels, resulting in multiple risk thresholds for different risk levels for each indicator, including: Based on the standard limit ratio method, multiple risk thresholds are generated proportionally from the preset control value of each indicator.

5. The method for risk assessment of foundation pit slope according to claim 1, characterized in that, Based on the correlation attributes between each indicator and the security response quantity, a family of unknown measure functions is selected, including: When the correlation attribute is positively correlated with the safety factor or negatively correlated with the failure probability, the ascending semi-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions of the index. When the correlation attribute is negatively correlated with the safety factor or positively correlated with the failure probability, the descending half-trapezoidal distribution or piecewise linear function is used as the family of unknown measure functions of the index. When the correlation attribute indicates that the indicator has a moderate correlation with the safety factor or failure probability, the trapezoidal distribution or normal distribution is used as the family of unknown measure functions for the indicator.

6. The method for risk assessment of foundation pit slope according to claim 1, characterized in that, The risk assessment results also include key risk indicators. After merging the influence weight of each indicator with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be assessed, it also includes: Each indicator is sorted according to its corresponding influence weight, and the indicators with the highest influence weight in the sorting results are selected as key risk indicators.

7. The method for risk assessment of foundation pit slope according to claim 1, characterized in that, After determining the risk assessment results of the slope of the foundation pit to be assessed, the following steps are also included: The engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated are obtained for the next time series period. Based on the multiple indicators of the next time series period, the comprehensive unknown measure vector of the foundation pit slope to be evaluated in the next time series period is determined. When the change in the comprehensive unknown measure vector of the current time series and the comprehensive unknown measure vector of the next time series of the foundation pit slope to be evaluated is determined, and the change in the comprehensive unknown measure vector is higher than the preset warning threshold, a warning signal for the foundation pit slope to be evaluated is generated.

8. A foundation pit slope risk assessment system, characterized in that, include: The indicator acquisition module is used to collect engineering geological condition indicators, hydrogeological condition indicators, construction data indicators, and monitoring data indicators characterizing the mechanics and deformation of the foundation pit slope to be evaluated. The weight calculation module is used to calculate the influence contribution of each indicator relative to the safety response quantity by taking the safety factor or instability probability of the foundation pit slope to be evaluated as the safety response quantity, and to obtain the influence weight of each indicator by contribution rate analysis. An uncertain measurement calculation module is used to classify the risk level of the slope to be evaluated and obtain multiple risk thresholds with different risk levels. Based on the correlation attribute between each indicator and the safety response quantity, a family of unknown measurement functions is selected. The risk thresholds of multiple different risk levels are substituted into the family of unknown measurement functions corresponding to each indicator to obtain the unknown measurement functions of each indicator relative to multiple different risk levels. The unknown measurement evaluation matrix of the indicator is determined according to the value of each indicator and the unknown measurement function. The influence weight of each indicator is combined with the unknown measure evaluation matrix to obtain the comprehensive unknown measure vector of the foundation pit slope to be evaluated; The assessment result generation module is used to determine the risk level of the slope to be assessed by the comprehensive unknown measure vector through the confidence identification criterion, and to determine the risk assessment result of the foundation pit slope to be assessed based on the risk level of the slope to be assessed and the comprehensive unknown measure vector.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the foundation pit slope risk assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method for risk assessment of foundation pit slope as described in any one of claims 1 to 7.

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