Risk assessment method based on combination of fuzzy analytic hierarchy process and topsis

Abstract

The application is a risk assessment method based on fuzzy analytic hierarchy process and TOPSIS, and relates to the technical field of tunnel engineering construction safety, comprising: data collection and analysis; index system construction; fuzzy analytic hierarchy process; TOPSIS evaluation. In the application, drilling core, image collection, water pressure monitoring and settlement measurement are used to comprehensively obtain geological, hydrological and construction process data; in the calculation of the geological deviation coefficient, the core length and footage ratio and the fracture projection area are used to describe the rock mass integrity and the fracture development degree; the hydrological deviation coefficient is obtained by combining the shallow hole and deep hole water pressure fluctuation analysis, the influence degree and the quantitative face value, so as to capture the space-time evolution law of the water pressure anomaly in multiple dimensions; the complex risk factors are converted into measurable indexes, the depth analysis of the tunnel construction risk is realized, scientific and comprehensive data support is provided for the risk assessment, and the accuracy and reliability of the evaluation result are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of tunnel construction safety technology, and in particular to a risk assessment method based on the combination of fuzzy hierarchical analysis and TOPSIS. Background Technology

[0002] In tunnel construction, risk assessment is a crucial step in ensuring project safety. Tunnel construction often faces risks such as complex geological conditions (e.g., faults, fracture zones), unstable hydrological environments (e.g., water inrush, mudslides), and deficiencies in construction management (e.g., untimely support, inadequate monitoring).

[0003] Existing risk assessment methods have many shortcomings. For example, assessment using a single indicator cannot fully reflect the combined impact of risk factors; the traditional analytic hierarchy process is highly subjective in determining indicator weights, making it difficult to accurately quantify the uncertainty of expert experience; and some assessment methods lack consideration for the dynamic changes of risk factors, resulting in delayed assessment results that cannot provide timely and effective support for construction decisions.

[0004] Therefore, a risk assessment method combining fuzzy hierarchical analysis and TOPSIS is needed to address the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to propose a risk assessment method based on the combination of fuzzy hierarchical analysis and TOPSIS in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Risk assessment methods based on the combination of fuzzy hierarchical analysis and TOPSIS include:

[0008] Data acquisition and analysis: Geological and hydrological information data and construction process information data are acquired during tunnel construction, and the geological and hydrological deviation coefficient and construction deviation coefficient are obtained after analysis.

[0009] Indicator system construction: A multi-level and multi-dimensional set of evaluation indicators is established by combining geological, hydrological, and construction factors.

[0010] Fuzzy Hierarchical Analysis: It solves the subjectivity problem of assigning index weights in the traditional hierarchical analysis method by using fuzzy mathematics theory.

[0011] TOPSIS assessment: Based on multi-index decision theory, it constructs ideal and negative ideal solutions to quantify the distance between each assessment object and the best / worst state, thereby achieving risk level ranking.

[0012] Preferably, the information collected in the data acquisition and analysis includes:

[0013] Geological data acquisition: measuring the total length of rock segments in the core through drilling; and obtaining image information of the borehole surface;

[0014] Hydrological data acquisition: Obtaining information on rock seepage and corresponding water pressure at borehole locations;

[0015] Acquisition of construction process information data: Obtain arch settlement data based on the three-dimensional coordinates of monitoring points.

[0016] Preferably, the process of obtaining the geological and hydrological deviation coefficient includes:

[0017] Geological deviation coefficients are obtained after analyzing geological data;

[0018] The hydrological deviation coefficient is obtained after analyzing the hydrological data;

[0019] The geological deviation coefficient and the hydrological deviation coefficient are weighted and calculated to obtain the geological-hydrological deviation coefficient.

[0020] Preferably, the process of obtaining the geological deviation coefficient includes:

[0021] A predetermined number of bedrock rocks were selected as test bedrock rocks from the bedrock traversed along the tunnel axis.

[0022] The core length of each bedrock test and the total drilling length of the drill bit are obtained sequentially. The rock index is obtained by dividing the core length by the total drilling length.

[0023] A preset rock index threshold is set. The rock index is subtracted from the rock index threshold. Values ​​greater than 0 in the resulting value are removed. The absolute value of the values ​​less than 0 in the resulting value is taken to obtain the rock difference.

[0024] The rock differences corresponding to each test bedrock are sorted in descending order of numerical value, and the images of the borehole surfaces corresponding to each rock difference are obtained in sequence and recorded as test images.

[0025] After preprocessing each test image, the corresponding rock crack features are extracted from them;

[0026] The rock in the test image is projected onto the projection plane in parallel according to its outer contour; at the same time, its crack features are also projected onto the projection plane.

[0027] Obtain the position of each crack on the projection surface, connect the two ends of each crack with straight lines to form a closed figure with the crack, and calculate the area of ​​the closed figure.

[0028] Extract the area of ​​the largest closed shape from each closed area in the test image, and use it as the closed projected area of ​​the test image.

[0029] A threshold for the closed projection area is preset. The closed projection area of ​​each test image is subtracted from the threshold. Values ​​less than 0 are removed from the obtained values, and values ​​greater than 0 are recorded as the closed projection area difference.

[0030] Sort all the closed projection surface differences in descending order of their numerical values, and extract the four largest closed projection surface differences and their corresponding rock locations.

[0031] Take the rock boreholes corresponding to the four largest differences in closed projection surfaces as endpoints, connect the endpoints of the four rocks in sequence to obtain a tetrahedron, calculate the volume of the tetrahedron, and record it as the geological deviation coefficient.

[0032] Preferably, the process of obtaining the hydrological deviation coefficient includes:

[0033] The location of the water-seeping rocks inside the tunnel was identified, and the rocks were marked and designated as marked rocks;

[0034] Containers are placed under each marked rock to collect seepage water from the marked rock, and the seepage water volume is read at preset time intervals to obtain the seepage water volume in each time interval and the total seepage water volume collected in the container.

[0035] Extract the seepage volume of the marked rocks at each time interval and arrange them according to the size of the seepage volume to obtain the maximum seepage volume in a single time interval; sequentially obtain the maximum seepage volume of each marked rock in a single time interval, arrange them in descending order of numerical value, and extract the k largest maximum seepage volumes.

[0036] Obtain the total seepage volume of the container below each marked rock, sort the total seepage volumes of each marked rock in descending order, and extract the m largest total seepage volumes;

[0037] The marker rocks corresponding to the k maximum seepage volumes and the m maximum total seepage volumes are statistically analyzed, and after determining the corresponding marker rocks, they are recorded as the analytical rocks.

[0038] Preferably, the method further includes:

[0039] Shallow holes are drilled in all the rocks to obtain the surface fissure water pressure of the rocks; the surface fissure water pressure of the rocks is obtained at preset time intervals, and the maximum and minimum water pressures are extracted from them. The difference between the maximum and minimum water pressures is calculated to obtain the water pressure difference.

[0040] The water column pressure corresponding to the borehole depth is obtained, and the difference between the water pressure difference and the corresponding water column pressure is calculated to obtain the corresponding water pressure difference.

[0041] The allowable fluctuation range of water pressure correspondence difference is preset, and water pressure correspondence difference that is not within the allowable fluctuation range is recorded as shallow water pressure deviation; the shallow water pressure deviation of all analyzed rocks is obtained;

[0042] Deep holes were drilled in all the analyzed rocks at shallow holes to monitor deep tectonic water;

[0043] The water pressure deviation of all analyzed rocks was obtained after analyzing the water pressure at the deep borehole.

[0044] The influence degree is obtained by dividing the number of analyzed rocks with all shallow and deep water pressure deviations by the total number of analyzed rocks.

[0045] The analysis rocks corresponding to all shallow and deep water pressure deviations are counted. If the shallow and deep water pressure deviations correspond to the same analysis rock, then the analysis rock is marked as the key rock.

[0046] Extract all key rock locations and their corresponding borehole locations, and use the borehole locations as endpoints. Connect all the endpoints sequentially with straight lines to obtain a closed figure, which is the largest area closed figure that can be formed.

[0047] Calculate the area of ​​the closed figure and record it as the quantified surface value;

[0048] The hydrological deviation coefficient is obtained by comprehensively processing the impact degree and the quantified surface value;

[0049] After normalizing the influence degree and the quantified surface value, the influence degree and the quantified surface value are used as the major and minor semi-axes of the ellipse, respectively, to establish an elliptical model. The area of ​​the elliptical model is calculated and denoted as the hydrological deviation coefficient.

[0050] Preferably, the process of obtaining the construction separation coefficient includes:

[0051] A predetermined number of monitoring points are set up inside the tunnel. The three-dimensional coordinates of the monitoring points are measured by emitting infrared light. The arch settlement value is calculated by the elevation difference between the two measurements.

[0052] A settlement threshold is preset, and the settlement value of the arch is compared with the settlement threshold. If the settlement value of the arch is greater than the settlement threshold, the monitoring point is marked and recorded as the monitoring time point.

[0053] Starting from the moment the arch settlement value is obtained, a 4-hour countdown monitoring is performed on the monitoring time point. The time corresponding to 4 hours later is recorded as the end time. The completion time of the support reinforcement of the monitoring time point is obtained. The completion time of the support reinforcement is subtracted from the end time. Values ​​less than 0 are discarded. Values ​​greater than 0 are recorded as deviation time.

[0054] Obtain the deviation duration corresponding to each monitoring point, and extract the largest deviation duration, which is recorded as the construction deviation coefficient.

[0055] Preferably, the fuzzy hierarchical analysis specifically includes:

[0056] Experts were invited to assign fuzzy scores to the relative importance of each factor in the criteria and indicator layers, reflecting the uncertainty of expert judgment.

[0057] The rationality of the judgment matrix is ​​verified by fuzzy mathematics to ensure that the weight assignment logic is consistent.

[0058] The weight coefficients of each index are determined by using a fuzzy weight solution algorithm, so that the weights can reflect the priority of the hierarchical structure and also accommodate the fuzzy characteristics in engineering practice.

[0059] The resulting weight vector will serve as the basis for weighting indicators in the TOPSIS evaluation.

[0060] Preferably, the TOPSIS evaluation specifically includes:

[0061] Standardize the geological and hydrological deviation coefficient and the construction deviation coefficient to eliminate dimensional differences;

[0062] By combining the weights obtained from fuzzy hierarchical analysis, the standardized indicators are weighted to construct a weighted decision matrix;

[0063] Then, determine the ideal solution and the negative ideal solution: extract the optimal and worst values ​​of each index from the weighted matrix to form the ideal solution vector and the negative ideal solution vector;

[0064] The distance between each evaluation object and the ideal solution and the negative ideal solution is calculated using the Euclidean distance formula, and then the risk assessment coefficient is obtained. The closer the risk assessment coefficient is to 1, the higher the risk.

[0065] Three threshold ranges are preset, and each threshold range corresponds to a risk level. The risk assessment coefficient is matched with the three threshold ranges to obtain the risk level corresponding to the risk assessment coefficient; the risk levels include low risk, medium risk and high risk.

[0066] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0067] 1. This invention comprehensively acquires geological, hydrological, and construction process data through multiple methods, including core drilling, image acquisition, water pressure monitoring, and settlement measurement. In calculating the geological deviation coefficient, it utilizes the core length and drilling footage ratio, as well as the projected area of ​​cracks, to characterize the integrity of the rock mass and the degree of fracture development. The hydrological deviation coefficient combines shallow and deep borehole water pressure fluctuation analysis, influence degree, and quantified surface values ​​to capture the spatiotemporal evolution of water pressure anomalies from multiple dimensions. This transforms complex risk factors into measurable indicators, enabling in-depth analysis of tunnel construction risks and providing scientific and comprehensive data support for risk assessment, significantly improving the accuracy and reliability of the assessment results.

[0068] 2. This invention utilizes fuzzy hierarchical analysis, introducing triangular or trapezoidal fuzzy numbers to address the uncertainty of expert experience. Through consistency matrix correction and weighting algorithms, it effectively reduces the interference of subjective factors, making the index weight assignments more aligned with engineering realities. Combined with TOPSIS assessment, it fully leverages expert knowledge and experience while employing mathematical models to achieve precise quantification, providing engineers with an intuitive and clear basis for risk status judgment. This facilitates the development of targeted hierarchical control strategies, greatly enhancing the systematicness and operability of tunnel construction risk assessment, and effectively ensuring construction safety and project quality. Attached Figure Description

[0069] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0070] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0071] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0072] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0073] Please see Figure 1 As shown, the present invention provides a technical solution:

[0074] Risk assessment methods based on the combination of fuzzy hierarchical analysis and TOPSIS include:

[0075] Data acquisition and analysis: Geological and hydrological information data and construction process information data are acquired during tunnel construction, and the geological and hydrological deviation coefficient and construction deviation coefficient are obtained after analysis.

[0076] Geological data acquisition: Through core drilling (diameter ≥75mm), the total length of rock segments with a length ≥10cm in the core was measured; and image information of the borehole surface was obtained;

[0077] Hydrological data acquisition: Obtaining information on rock seepage and corresponding water pressure at borehole locations;

[0078] Acquisition of construction process information data: Obtaining arch settlement data based on the three-dimensional coordinates of monitoring points;

[0079] The process of obtaining the geological-hydrological deviation coefficient includes:

[0080] Geological deviation coefficients are obtained after analyzing geological data;

[0081] include:

[0082] The bedrock that the tunnel axis passes through, especially the section where the surrounding rock grade may be Class III or above (requiring support), or the area where there are faults or lithological change zones, will be selected as a predetermined number of bedrock as test bedrock.

[0083] The core length of each bedrock test and the total drilling length of the drill bit are obtained sequentially. The rock index is obtained by dividing the core length by the total drilling length.

[0084] A preset rock index threshold is set. The rock index is subtracted from the rock index threshold. Values ​​greater than 0 in the resulting value are removed. The absolute value of the values ​​less than 0 in the resulting value is taken to obtain the rock difference.

[0085] The rock differences corresponding to each test bedrock are sorted in descending order of numerical value, and the images of the borehole surfaces corresponding to each rock difference are obtained in sequence and recorded as test images.

[0086] After preprocessing each test image, the corresponding rock crack features are extracted from them;

[0087] The rock in the test image is projected onto the projection plane in parallel according to its outer contour; at the same time, its crack features are also projected onto the projection plane.

[0088] Obtain the position of each crack on the projection surface, connect the two ends of each crack with straight lines to form a closed figure with the crack, and calculate the area of ​​the closed figure.

[0089] Extract the area of ​​the largest closed shape from each closed area in the test image, and use it as the closed projected area of ​​the test image.

[0090] A threshold for the closed projection area is preset. The closed projection area of ​​each test image is subtracted from the threshold. Values ​​less than 0 are removed from the obtained values, and values ​​greater than 0 are recorded as the closed projection area difference.

[0091] Sort all the closed projection surface differences in descending order of their numerical values, and extract the four largest closed projection surface differences and their corresponding rock locations.

[0092] Take the rock boreholes corresponding to the four largest differences in closed projection surfaces as endpoints, connect the endpoints of the four rocks in sequence to obtain a tetrahedron, calculate the volume of the tetrahedron, and record it as the geological deviation coefficient.

[0093] The hydrological deviation coefficient is obtained after analyzing the hydrological data;

[0094] include:

[0095] The location of the water-seeping rocks inside the tunnel was identified, and the rocks were marked and designated as marked rocks;

[0096] Containers are placed under each marked rock to collect seepage water from the marked rock, and the seepage water volume is read at preset time intervals to obtain the seepage water volume in each time interval and the total seepage water volume collected in the container.

[0097] Extract the seepage volume of the marked rocks at each time interval and arrange them according to the size of the seepage volume to obtain the maximum seepage volume in a single time interval; sequentially obtain the maximum seepage volume of each marked rock in a single time interval, arrange them in descending order of value, and extract the k largest maximum seepage volumes, where k is greater than 3, and the specific k is determined by the management personnel based on the actual tunnel conditions.

[0098] Obtain the total seepage volume of the container below each marked rock, sort the total seepage volume of each marked rock in descending order, and extract the m largest total seepage volumes. Similarly, m is greater than 3, and the specific value is determined by the management personnel based on the actual tunnel conditions.

[0099] The marker rocks corresponding to the k maximum seepage volumes and the m maximum total seepage volumes are statistically analyzed, and after determining the corresponding marker rocks, they are recorded as the analyzed rocks.

[0100] Shallow holes (2-5 meters) are drilled in all the rocks to obtain the surface fissure water pressure of the rocks; the surface fissure water pressure of the rocks is obtained at preset time intervals, and the maximum and minimum water pressures are extracted from them. The difference between the maximum and minimum water pressures is calculated to obtain the water pressure difference value.

[0101] The water column pressure is obtained based on the borehole depth (e.g., if the borehole depth is 10m, the water pressure is approximately 0.1MPa). The difference between the water pressure difference and the corresponding water column pressure is calculated to obtain the corresponding water pressure difference.

[0102] The allowable fluctuation range of water pressure correspondence difference is preset, and water pressure correspondence difference that is not within the allowable fluctuation range is recorded as shallow water pressure deviation; the shallow water pressure deviation of all analyzed rocks is obtained;

[0103] For all the analyzed rocks, deep holes (10-30 meters) are drilled at shallow holes to monitor deep tectonic water (such as water pressure in fault zones).

[0104] The water pressure deviation of all analyzed rocks was obtained after analyzing the water pressure at the deep borehole.

[0105] This includes: acquiring the deep fissure water pressure of the rock at preset time intervals, extracting the maximum and minimum water pressures from them, and calculating the difference between the maximum and minimum water pressures to obtain the water pressure difference value;

[0106] The water column pressure corresponding to the borehole depth is obtained, and the difference between the water pressure difference and the corresponding water column pressure is calculated to obtain the corresponding water pressure difference.

[0107] The allowable fluctuation range of water pressure correspondence difference is preset, and water pressure correspondence difference that is not within the allowable fluctuation range is recorded as water pressure depth deviation; the water pressure depth deviation of all analyzed rocks is obtained;

[0108] The influence degree is obtained by dividing the number of analyzed rocks with all shallow and deep water pressure deviations by the total number of analyzed rocks.

[0109] The analysis rocks corresponding to all shallow and deep water pressure deviations are counted. If the shallow and deep water pressure deviations correspond to the same analysis rock, then the analysis rock is marked as the key rock.

[0110] Extract all key rock locations and their corresponding borehole locations, and use the borehole locations as endpoints. Connect all the endpoints sequentially with straight lines to obtain a closed figure, which is the largest area closed figure that can be formed.

[0111] Calculate the area of ​​the closed figure and record it as the quantified surface value;

[0112] The hydrological deviation coefficient is obtained by comprehensively processing the impact degree and the quantified surface value;

[0113] After normalizing the impact degree and the quantified surface value, the impact degree and the quantified surface value are used as the major semi-axis and minor semi-axis of the ellipse, respectively, to establish an elliptical model. The area of ​​the elliptical model is calculated and recorded as the hydrological deviation coefficient.

[0114] By combining shallow and deep borehole water pressure monitoring, the water pressure fluctuation characteristics of the rock surface and deep layers can be captured in multiple dimensions. This not only allows for the accurate identification of the temporal variation patterns of abnormal water pressure, but also enables the quantification of the impact range and concentration of abnormal water pressure by spatially analyzing the distribution of key rocks and the area of ​​enclosed shapes.

[0115] The innovative integration of impact degree and quantitative surface value into the elliptical model calculation takes into account both the proportion of rock involved in abnormal water pressure and its spatial aggregation effect, so that the hydrological deviation coefficient can more comprehensively reflect the complexity and potential hazards of hydrological risks in tunnel construction, providing a reliable and quantitative basis for risk assessment and control, and effectively improving the accuracy and timeliness of tunnel construction in responding to hydrological risks.

[0116] The geological deviation coefficient and the hydrological deviation coefficient are weighted and calculated to obtain the geological-hydrological deviation coefficient.

[0117] Preset the weighting factors for the geological deviation coefficient and the hydrological deviation coefficient, and calculate the geological and hydrological deviation coefficients by multiplying the geological deviation coefficient and the hydrological deviation coefficient with their corresponding weighting factors respectively.

[0118] The process of obtaining the construction separation coefficient includes:

[0119] A predetermined number of monitoring points are set up inside the tunnel. The three-dimensional coordinates of the monitoring points are measured by emitting infrared light. The arch settlement value is calculated by the elevation difference between the two measurements.

[0120] A settlement threshold is preset, and the settlement value of the arch is compared with the settlement threshold. If the settlement value of the arch is greater than the settlement threshold, the monitoring point is marked and recorded as the monitoring time point.

[0121] Starting from the moment the arch settlement value is obtained, a 4-hour countdown monitoring is performed on the monitoring time point. The time corresponding to 4 hours later is recorded as the end time. The completion time of the support reinforcement of the monitoring time point is obtained. The completion time of the support reinforcement is subtracted from the end time. Values ​​less than 0 are discarded. Values ​​greater than 0 are recorded as deviation time.

[0122] Obtain the deviation duration corresponding to each monitoring point, and extract the largest deviation duration, which is recorded as the construction deviation coefficient;

[0123] Indicator system construction: Combining geological, hydrological, and construction factors, a multi-level and multi-dimensional set of evaluation indicators is established; First, geological indicators cover rock integrity (such as rock index and degree of crack development) and rock mass structural characteristics (such as closed projected area and crack distribution represented by tetrahedral volume), reflecting the stability of the surrounding rock itself.

[0124] Hydrological indicators include seepage volume, water pressure fluctuations (such as the water pressure difference between shallow and deep holes), and the range of water pressure influence (quantified surface value), which reflect the erosion and softening effect of groundwater on the surrounding rock.

[0125] Construction indicators include crown settlement rate, support response timeliness (such as deviation duration), and construction process compliance, which characterize the intervention effect of the construction process on risk evolution.

[0126] Each indicator must be measurable and sensitive. The logical hierarchy should be determined through expert experience and engineering specifications to form a tree structure of "target layer - criterion layer - indicator layer" to provide a quantitative basis for subsequent risk assessment.

[0127] Fuzzy Hierarchical Analysis: It solves the subjectivity problem of assigning index weights in the traditional hierarchical analysis method by using fuzzy mathematics theory.

[0128] Specifically, it includes:

[0129] Experts were invited to assign fuzzy scores to the relative importance of each factor in the criteria and indicator layers (e.g., using triangular fuzzy numbers or trapezoidal fuzzy numbers) to reflect the uncertainty of expert judgment.

[0130] The rationality of the judgment matrix is ​​verified by fuzzy mathematics methods (such as fuzzy consistency matrix correction) to ensure that the weight assignment logic is consistent.

[0131] The weight coefficients of each index are determined by using fuzzy weighting algorithms (such as the fuzzy eigenvector method), so that the weights can reflect the priority of the hierarchical structure and also encompass the fuzzy characteristics in engineering practice (such as the fuzzy boundary of the surrounding rock grade classification).

[0132] The resulting weight vector will serve as the basis for weighting indicators in the TOPSIS assessment, enhancing the scientific rigor of risk assessment.

[0133] TOPSIS assessment: Based on multi-index decision theory, it constructs ideal and negative ideal solutions to quantify the distance between each assessment object and the best / worst state, thereby achieving risk level ranking;

[0134] Specifically, it includes:

[0135] Standardize the geological and hydrological deviation coefficients and construction deviation coefficients (e.g., normalize or standardize) to eliminate dimensional differences;

[0136] By combining the weights obtained from fuzzy hierarchical analysis, the standardized indicators are weighted to construct a weighted decision matrix;

[0137] Then, determine the ideal solution and the negative ideal solution: extract the optimal value (such as the minimum settlement rate) and the worst value (such as the maximum water pressure deviation) of each index from the weighted matrix to form the ideal solution vector and the negative ideal solution vector;

[0138] The distance between each evaluation object and the ideal solution and the negative ideal solution is calculated using the Euclidean distance formula, and then the risk assessment coefficient (within the range of [0,1]) is obtained. The closer the risk assessment coefficient is to 1, the higher the risk. The calculations involved in this process are direct references to existing technologies and will not be elaborated here.

[0139] Three threshold ranges are preset, and each threshold range corresponds to a risk level. The risk assessment coefficient is matched with the three threshold ranges to obtain the risk level corresponding to the risk assessment coefficient; the risk levels include low risk, medium risk and high risk.

[0140] Standardization processes eliminate dimensional differences in indicators such as geological and hydrological deviation coefficients and construction deviation coefficients, ensuring data comparability and preventing the impact of different units on the scientific validity of assessment results.

[0141] By combining fuzzy hierarchical analysis weights for weighted processing, the importance of each indicator in risk assessment is fully considered, making the weighted decision matrix more closely reflect the actual risk contribution. By determining the ideal solution and the negative ideal solution, a clear reference benchmark is provided for the assessment object. The distance to the ideal / negative ideal solution is calculated using Euclidean distance, and the complex risk information is transformed into a quantitative risk assessment coefficient in the [0,1] interval, realizing an intuitive expression of the degree of risk.

[0142] Three sets of thresholds are preset to match risk levels, and abstract coefficients are mapped to low, medium and high risk categories. This makes it easier for engineers to quickly identify risk status, accurately formulate graded control strategies, effectively improve the systematicness, objectivity and operability of tunnel construction risk assessment, and ensure construction safety and project quality.

[0143] The above formulas are derived from software simulations using a large amount of data and are selected to be close to the actual values. The influence weighting factors and specific coefficient values ​​in the formulas are set by those skilled in the art based on the actual situation and can be adjusted and modified in the future.

[0144] The above description of the embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS, characterized in that, The method comprises the following steps: Data acquisition and analysis: obtaining geological and hydrological information data and construction process information data in tunnel construction, and analyzing the data respectively to obtain geological and hydrological deviation coefficients and construction deviation coefficients; Specifically, the method comprises the following steps: Obtaining geological data: obtaining the total length of rock section in the core through drilling and coring, and obtaining image information of the drilling face; Obtaining hydrological data: obtaining water seepage information of the rock and water pressure information corresponding to the drilling; Obtaining construction process information data: obtaining vault settlement data based on the three-dimensional coordinates of the monitoring points; The process of obtaining the geological and hydrological deviation coefficient comprises the following steps: Analyzing the geological data to obtain a geological deviation coefficient; Analyzing the hydrological data to obtain a hydrological deviation coefficient; Calculating the geological deviation coefficient and the hydrological deviation coefficient to obtain the geological and hydrological deviation coefficient; The process of obtaining the geological deviation coefficient comprises the following steps: Selecting a preset number of bedrocks as test bedrocks from the bedrocks through which the tunnel axis passes; Obtaining the core length and the total drilling length of the drill bit of each test bedrock, and dividing the core length by the total drilling length of the drill bit to obtain a rock index; Subtracting a preset rock index threshold value from the rock index, removing the values greater than 0 from the obtained values, and taking the absolute value of the values less than 0 to obtain a rock difference degree; Arranging the rock difference degrees corresponding to the test bedrocks in descending order according to the numerical values, and obtaining the image of the drilling face corresponding to each rock difference degree, which is recorded as a test image; Preprocessing each test image to extract the corresponding rock crack features therefrom; Projecting the rock in the test image onto a projection plane according to the outer contour thereof, and projecting the crack features thereof onto the projection plane at the same time; Obtaining the positions of the cracks on the projection plane, connecting the two ends of each crack with a straight line to form a closed figure with the crack, and calculating the area of the closed figure; Extracting the area of the largest closed figure from each closed area in the test image as the closed projection area of the test image; Subtracting a preset closed projection area threshold value from the closed projection area of each test image, removing the values less than 0 from the obtained values, and recording the values greater than 0 as closed projection face difference values; Arranging all the closed projection face difference values in descending order according to the numerical values, and extracting the four largest closed projection face difference values and the corresponding rock positions therefrom; Connecting the end points of the four rocks corresponding to the four largest closed projection face difference values to obtain a tetrahedron, calculating the volume of the tetrahedron, and recording the volume as the geological deviation coefficient; The process of obtaining the hydrological deviation coefficient comprises the following steps: Determining the rock positions of the water seepage in the tunnel, and marking the rocks to obtain marked rocks; Arranging containers under each marked rock to collect the water seepage of the marked rock, and reading the water seepage amount at a preset time interval to obtain the water seepage amount in each time interval and the total water seepage amount collected in the container; Extract the water infiltration amount of the marked rock at each time interval, arrange them according to the size of the water infiltration amount, and obtain the maximum water infiltration amount in a single time interval; sequentially obtain the maximum water infiltration amount of each marked rock in a single time interval, arrange them in descending order according to the numerical value, and extract the maximum k maximum water infiltration amounts; Obtain the total water infiltration amount of the container under each marked rock, arrange the total water infiltration amount of each marked rock in descending order according to the size, and extract the m largest total water infiltration amounts; Count the marked rocks corresponding to the k maximum water infiltration amounts and the m largest total water infiltration amounts respectively, and determine the corresponding marked rocks, then mark them as analysis rocks; Drill shallow holes on all analysis rocks to obtain the surface fissure water pressure of the analysis rocks; obtain the surface fissure water pressure of the analysis rocks at a preset time interval, and extract the maximum water pressure and the minimum water pressure from them; calculate the difference between the maximum water pressure and the minimum water pressure to obtain the water pressure difference; Based on the drilling depth, obtain the corresponding water column pressure, and calculate the difference between the water pressure difference and the corresponding water column pressure to obtain the water pressure corresponding difference; Pre-set the allowable fluctuation range of the water pressure corresponding difference, and mark the water pressure corresponding difference that is not within the allowable fluctuation range as the water pressure shallow deviation; obtain the water pressure shallow deviation of all analysis rocks; Drill deep holes on all analysis rocks at the shallow holes for monitoring deep structure water; After analyzing the water pressure at the deep holes, obtain the water pressure deep deviation of all analysis rocks; Divide the number of analysis rocks of all water pressure shallow deviations and water pressure deep deviations by the total number of analysis rocks to obtain the influence degree; Count all the analysis rocks corresponding to the water pressure shallow deviation and the water pressure deep deviation, if the water pressure shallow deviation and the water pressure deep deviation correspond to the same analysis rock, mark the analysis rock as the key rock; Extract the positions of all key rocks and their corresponding drilling positions, take the drilling positions as endpoints, and connect all endpoints in sequence with straight lines to obtain a closed figure, and the closed figure is the largest area closed figure that can be formed; Calculate the area of the closed figure and mark it as the quantified face value; After comprehensive processing of the influence degree and the quantified face value, obtain the hydrological deviation coefficient; After normalization processing of the influence degree and the quantified face value, take the influence degree and the quantified face value as the long semi-axis and the short semi-axis of the ellipse respectively, establish an elliptical model, calculate the area of the elliptical model, and mark it as the hydrological deviation coefficient; The process of obtaining the construction deviation coefficient includes: Lay a preset number of monitoring points in the tunnel, measure the three-dimensional coordinates of the monitoring points by emitting infrared light, and calculate the vault settlement value by the height difference of two measurements; Pre-set the settlement threshold, compare the vault settlement value with the settlement threshold, if the vault settlement value is greater than the settlement threshold, mark the monitoring point and mark it as the monitoring time point; Start from the time when the vault settlement value is obtained, monitor the monitoring time point for 4 hours countdown, mark the time corresponding to 4 hours as the end time, obtain the completion time point of the support reinforcement of the monitoring time point, and subtract the end time from the completion time point of the support reinforcement, eliminate the values less than 0 in the obtained values, and mark the values greater than 0 in the obtained values as the deviation duration. The deviation duration corresponding to each monitoring point is obtained, and the maximum deviation duration is extracted, denoted as the construction deviation coefficient index system. The multi-level and multi-dimensional evaluation index set is established by combining geological, hydrological, and construction factors. Fuzzy AHP: Through fuzzy mathematical theory, the subjectivity problem of index weight assignment in traditional AHP is solved. TOPSIS evaluation: Based on multi-index decision-making theory, the distance between each evaluation object and the optimal / best state is quantified by constructing ideal solution and negative ideal solution, so as to realize risk ranking. 2.The risk assessment method based on the combination of fuzzy AHP and TOPSIS according to claim 1, characterized in that, Fuzzy AHP, specifically including: Inviting experts to give fuzzy scores on the relative importance of each factor in the guideline layer and index layer, reflecting the uncertainty of expert judgment. The rationality of the judgment matrix is verified by fuzzy mathematical method to ensure the logical self-consistency of weight assignment. The fuzzy weight solving algorithm is used to determine the weight coefficients of each index, so that the weight can not only reflect the priority of the hierarchical structure, but also contain the fuzzy characteristics in engineering practice. The final weight vector will be used as the basis for index weighting in TOPSIS evaluation. 3.The risk assessment method based on the combination of fuzzy AHP and TOPSIS according to claim 1, characterized in that, TOPSIS evaluation, specifically including: Standardizing the geological and hydrological deviation coefficients and the construction deviation coefficients to eliminate dimensional differences. Combining the weights obtained by fuzzy AHP, the standardized indexes are weighted to construct a weighted decision matrix. Then, the ideal solution and negative ideal solution are determined: the optimal value and the worst value of each index in the weighted matrix are extracted to form the ideal solution vector and the negative ideal solution vector. The risk assessment coefficient is obtained by calculating the distance between each evaluation object and the ideal solution and negative ideal solution through the Euclidean distance formula. The closer the risk assessment coefficient is to 1, the higher the risk. Three groups of threshold value ranges are preset, each corresponding to a risk level. The risk assessment coefficient is matched with the three groups of threshold value ranges to obtain the risk level corresponding to the risk assessment coefficient. The risk levels include low risk, medium risk, and high risk.

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