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

Abstract

The invention specifically relates to a risk assessment method based on the combination of a fuzzy analytic hierarchy process and TOPSIS, and relates to the technical field of tunnel engineering construction safety. Constructing an index system; performing fuzzy hierarchy analysis; and TOPSIS evaluation is carried out. According to the invention, various means of drilling coring, image acquisition, water pressure monitoring and settlement measurement are adopted, so that geological, hydrological and construction process data are comprehensively obtained; in the geological deviation coefficient calculation, the rock mass integrity and the fracture development degree are depicted by using the proportion of the rock core length to the footage and the fracture projection area; hydrological deviation coefficients are subjected to shallow hole and deep hole water pressure fluctuation analysis, influence degrees and quantitative face values are combined, and a time-space evolution rule of water pressure abnormity is captured in a multi-dimensional mode; complex risk factors are converted into measurable indexes, deep analysis of tunnel construction risks is achieved, scientific and comprehensive data support is provided for risk assessment, and the accuracy and reliability of assessment results are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering construction safety, and in particular to a risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS. Background Art

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

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

[0004] Therefore, a risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS is needed to deal with the above problems. Summary of the Invention

[0005] The purpose of the present invention is to solve the above problems and propose a risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS includes: Data collection and analysis: Obtain geological and hydrological information data and construction process information data during tunnel construction, and analyze them separately to obtain the geological and hydrological deviation coefficient and construction separation coefficient; Index system construction: Combine geological, hydrological and construction factors to establish a multi-level and multi-dimensional evaluation index set; Fuzzy Hierarchy Analysis: Through fuzzy mathematical theory, it solves the subjective problem of indicator weight assignment in traditional hierarchical analysis method; TOPSIS evaluation: Based on the multi-criteria decision-making theory, by constructing ideal solutions and negative ideal solutions, the distance between each evaluation object and the optimal / worst state is quantified, thereby achieving risk level ranking.

[0007] Preferably, the information collected in the data collection and analysis includes: Acquisition of geological data: measuring the total length of the rock section in the core by coring the core, and image information of the borehole surface; Acquisition of hydrological data: obtaining rock seepage information and its corresponding water pressure information at the borehole; Acquisition of construction process information data: Acquisition of vault settlement data based on the three-dimensional coordinates of monitoring points.

[0008] Preferably, the process of obtaining the geological and hydrological deviation coefficient includes: The geological deviation coefficient is obtained after analyzing the geological data; The hydrological deviation coefficient is obtained after analyzing the hydrological data; The geological deviation coefficient and the hydrological deviation coefficient are weighted and calculated to obtain the geological and hydrological deviation coefficient.

[0009] Preferably, the process of obtaining the geological deviation coefficient includes: Select a preset number of bedrocks from the bedrock through which the tunnel axis passes as test bedrocks; Obtain the core length of each test bedrock and the total footage of the drill bit in sequence, and divide the core length by the total footage of the drill bit to obtain the rock index; Preset the rock index threshold, subtract the rock index threshold from the rock index, remove the values ​​greater than 0 in the obtained values, and take the absolute value of the values ​​less than 0 in the obtained values ​​to obtain the rock difference; Arrange the rock differences corresponding to each test bedrock in descending order according to their numerical values, and sequentially obtain images of the borehole surface corresponding to each rock difference, which are recorded as test images; After preprocessing each test image, the corresponding rock crack features are extracted from it; The rock in the test image is projected onto the projection surface in parallel according to its outer contour; at the same time, its crack features are also projected onto the projection surface; Obtain the position of each crack on the projection surface, connect the two ends of each crack with a straight line to form a closed figure with the crack, and calculate the area of ​​the closed figure; Extract the area of ​​the largest closed figure from each closed area in the test image as the closed projection area of ​​the test image; A closed projection area threshold is preset, and the closed projection area threshold is subtracted from the closed projection area of ​​each test image. Values ​​less than 0 are removed from the obtained values, and values ​​greater than 0 are recorded as closed projection area differences; Arrange all closed projection surface differences in descending order according to their numerical values, and extract the four largest closed projection surface differences and their corresponding rock positions; The rock boreholes corresponding to the four largest closed projection surface differences are taken as endpoints, and the endpoints of the four rocks are connected in sequence to obtain a tetrahedron. The volume of the tetrahedron is calculated and recorded as the geological deviation coefficient.

[0010] Preferably, the process of obtaining the hydrological deviation coefficient includes: Determine the location of water seepage rocks in the tunnel and mark the rocks as marked rocks; A container is placed under each marked rock to collect water seepage from the marked rock, and the water seepage volume is read at preset time intervals to obtain the water seepage volume in each time interval and the total water seepage volume collected in the container; Extract the water seepage of the marked rock at each time interval and arrange them according to the size of the water seepage to obtain the maximum water seepage in a single time interval; obtain the maximum water seepage of each marked rock in a single time interval in turn, arrange them in descending order according to the value, and extract the largest k maximum water seepage; Obtain the total water seepage of the container under each marked rock, sort the total water seepage of each marked rock in descending order, and extract the m largest total water seepages; The marked rocks corresponding to the k maximum water seepage amounts and the m maximum total water seepage amounts are counted, and after the corresponding marked rocks are determined, they are recorded as analysis rocks.

[0011] Preferably, the method further includes: Drill shallow holes in all analyzed rocks to obtain the surface fracture water pressure of the analyzed rocks; obtain the surface fracture water pressure of the analyzed rocks at preset time intervals, extract the maximum water pressure and the minimum water pressure, and calculate the difference between the maximum water pressure and the minimum water pressure to obtain the water pressure difference; The corresponding water column pressure is obtained based on the drilling depth, and the water pressure difference is calculated from the corresponding water column pressure to obtain the corresponding water pressure difference; Preset the allowable fluctuation range of the water pressure correspondence difference, and record the water pressure correspondence difference that is not within the allowable fluctuation range of the water pressure correspondence difference as the water pressure shallow deviation; obtain the water pressure shallow deviation of all analyzed rocks; Deep holes were drilled in shallow locations for all analyzed rocks to monitor deep structural water; After analyzing the water pressure at the deep hole, the water pressure depth deviation of all analyzed rocks was obtained; The impact degree is obtained by counting the number of analyzed rocks with shallow water pressure deviation and deep water pressure deviation and dividing it by the total number of analyzed rocks. Count all the analysis rocks corresponding to the shallow water pressure deviation and the deep water pressure deviation. If the shallow water pressure deviation and the deep water pressure deviation correspond to the same analysis rock, then record the analysis rock as the key rock. Extract all key rock positions and their corresponding drill hole positions, use the drill hole positions as endpoints, and connect all endpoints in sequence with straight lines to form a closed figure with the largest area that can be formed; Calculate the area of ​​a closed figure and record it as a quantitative face value; The hydrological deviation coefficient is obtained by comprehensively processing the impact degree and the quantitative face value; After normalizing the impact degree and the quantitative face value, the impact degree and the quantitative face value are used as the major and minor semi-axis of the ellipse respectively, and an ellipse model is established. The area of ​​the ellipse model is calculated and recorded as the hydrological deviation coefficient.

[0012] Preferably, the process of obtaining the construction separation coefficient includes: A preset number of monitoring points are set up in the tunnel. The three-dimensional coordinates of the monitoring points are measured by emitting infrared light, and the vault settlement value is calculated based on the elevation difference between the two measurements. A settlement threshold is preset, and the arch settlement value is compared with the settlement threshold. If the arch settlement value is greater than the settlement threshold, the monitoring point is marked and recorded as a monitoring time-effectiveness point; Starting from the moment when the vault settlement value is obtained, a 4-hour countdown monitoring is performed on the monitoring time-limiting point. The corresponding moment after 4 hours is recorded as the end moment. The completion time point of the support reinforcement at the monitoring time-limiting point is obtained, and the completion time point of the support reinforcement is subtracted from the end moment. The values ​​less than 0 are eliminated, and the values ​​greater than 0 are recorded as the deviation duration. Obtain the deviation duration corresponding to each monitoring point, and extract the maximum deviation duration, which is recorded as the construction separation coefficient.

[0013] Preferably, the fuzzy hierarchical analysis specifically includes: Experts were invited to give fuzzy scores on the relative importance of each factor in the criterion layer and the indicator layer to reflect the uncertainty of expert judgment; The rationality of the judgment matrix is ​​verified by fuzzy mathematics methods to ensure the self-consistency of the weight assignment logic; The weight coefficient of each indicator is determined by using the fuzzy weight solution algorithm, so that the weight can reflect the priority of the hierarchy and accommodate the fuzzy characteristics of engineering practice. The final weight vector will be used as the basis for weighting indicators in TOPSIS evaluation.

[0014] Preferably, the TOPSIS evaluation specifically includes: Standardize the geological and hydrological deviation coefficients and construction separation coefficients to eliminate dimensional differences; Combined with the weights obtained by fuzzy hierarchical analysis, the standardized indicators are weighted to construct a weighted decision matrix; Then, determine the ideal solution and the negative ideal solution: extract the optimal value and the worst value of each indicator in the weighted matrix to form the ideal solution vector and the negative ideal solution vector; The distance between each assessment 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. Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The risk assessment coefficient is matched with the value range of the three groups of threshold values ​​to obtain the risk level corresponding to the risk assessment coefficient; the risk levels include low risk, medium risk and high risk.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention comprehensively acquires geological, hydrological and construction process data through multiple means such as coring, image acquisition, water pressure monitoring and settlement measurement. In the calculation of geological deviation coefficient, the ratio of core length to footage and the projected area of ​​cracks are used to characterize the integrity of the rock mass and the degree of crack development. The hydrological deviation coefficient captures the spatiotemporal evolution of water pressure anomalies in multiple dimensions by combining shallow and deep hole water pressure fluctuation analysis, influence degree and quantitative face value. Complex risk factors are converted into measurable indicators to achieve in-depth analysis of tunnel construction risks, providing scientific and comprehensive data support for risk assessment and significantly improving the accuracy and reliability of assessment results.

[0016] 2. This invention uses the fuzzy analytic hierarchy process (FAHP) to introduce triangular or trapezoidal fuzzy numbers to address the uncertainty of expert experience. Through consistency matrix correction and weight solution algorithms, it effectively reduces the interference of subjective factors and makes the indicator weight assignment more in line with engineering practice. Combined with TOPSIS evaluation, it fully utilizes expert knowledge and experience while achieving precise quantification with the help of mathematical models, providing engineers with an intuitive and clear basis for judging risk status and facilitating the formulation of targeted hierarchical management and control strategies. This greatly enhances the systematicness and operability of tunnel construction risk assessment, effectively ensuring construction safety and project quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0018] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0019] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0020] See also Figure 1 As shown, the present invention provides a technical solution: The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS includes: Data collection and analysis: Obtain geological and hydrological information data and construction process information data during tunnel construction, and analyze them separately to obtain the geological and hydrological deviation coefficient and construction separation coefficient; Acquisition of geological data: Drilling and coring (diameter ≥ 75 mm), measuring the total length of rock sections ≥ 10 cm in the core, and image information of the drill hole surface; Acquisition of hydrological data: obtaining rock seepage information and its corresponding water pressure information at the borehole; Acquisition of information data during the construction process: Acquisition of vault settlement data based on the three-dimensional coordinates of monitoring points; The process of obtaining the geohydrological deviation coefficient includes: The geological deviation coefficient is obtained after analyzing the geological data; include: Select a preset amount of bedrock as test bedrock from the bedrock that the tunnel axis passes through, especially in sections where the surrounding rock grade may be Grade III or above (requiring support), or in areas with faults or lithologic abrupt zones; Obtain the core length of each test bedrock and the total footage of the drill bit in sequence, and divide the core length by the total footage of the drill bit to obtain the rock index; Preset the rock index threshold, subtract the rock index threshold from the rock index, remove the values ​​greater than 0 in the obtained values, and take the absolute value of the values ​​less than 0 in the obtained values ​​to obtain the rock difference; Arrange the rock differences corresponding to each test bedrock in descending order according to their numerical values, and sequentially obtain images of the borehole surface corresponding to each rock difference, which are recorded as test images; After preprocessing each test image, the corresponding rock crack features are extracted from it; The rock in the test image is projected onto the projection surface in parallel according to its outer contour; at the same time, its crack features are also projected onto the projection surface; Obtain the position of each crack on the projection surface, connect the two ends of each crack with a straight line to form a closed figure with the crack, and calculate the area of ​​the closed figure; Extract the area of ​​the largest closed figure from each closed area in the test image as the closed projection area of ​​the test image; A closed projection area threshold is preset, and the closed projection area threshold is subtracted from the closed projection area of ​​each test image. Values ​​less than 0 are removed from the obtained values, and values ​​greater than 0 are recorded as closed projection area differences; Arrange all closed projection surface differences in descending order according to their numerical values, and extract the four largest closed projection surface differences and their corresponding rock positions; Take the rock boreholes corresponding to the four largest closed projection surface differences 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; The hydrological deviation coefficient is obtained after analyzing the hydrological data; include: Determine the location of water seepage rocks in the tunnel and mark the rocks as marked rocks; A container is placed under each marked rock to collect water seepage from the marked rock, and the water seepage volume is read at preset time intervals to obtain the water seepage volume in each time interval and the total water seepage volume collected in the container; Extract the water seepage volume of the marked rock at each time interval and arrange them according to the water seepage volume to obtain the maximum water seepage volume within a single time interval; obtain the maximum water seepage volume of each marked rock within a single time interval in turn, arrange them in descending order according to the value, and extract the largest k maximum water seepage volumes, where k is greater than 3 and is determined by the management personnel based on the actual tunnel conditions; Obtain the total water seepage volume of the container under each marked rock, sort the total water seepage volume of each marked rock in descending order, and extract the m largest total water seepage volumes. Similarly, m is greater than 3, and the specific number is determined by the management personnel based on the actual tunnel conditions; The marked rocks corresponding to the k largest water seepage amounts and the m largest total water seepage amounts are counted, and after the corresponding marked rocks are determined, they are recorded as analysis rocks; Drill shallow holes (2-5 meters) in all analyzed rocks to obtain the surface fracture water pressure of the analyzed rocks; obtain the surface fracture water pressure of the analyzed rocks at preset time intervals, extract the maximum and minimum water pressures from them, and calculate the difference between the maximum and minimum water pressures to obtain the water pressure difference; Obtain the corresponding water column pressure based on the borehole depth (e.g., if the hole depth is 10m, the water pressure is ≈ 0.1MPa). Calculate the difference between the water pressure difference and the corresponding water column pressure to obtain the corresponding water pressure difference. Preset the allowable fluctuation range of the water pressure correspondence difference, and record the water pressure correspondence difference that is not within the allowable fluctuation range of the water pressure correspondence difference as the water pressure shallow deviation; obtain the water pressure shallow deviation of all analyzed rocks; Deep holes (10-30 m) were drilled in shallow locations for all analyzed rocks to monitor deep structural water (e.g., fault zone water pressure); After analyzing the water pressure at the deep hole, the water pressure depth deviation of all analyzed rocks was obtained; The method includes: obtaining and analyzing the deep fracture water pressure of the rock at a preset time interval, extracting the maximum water pressure and the minimum water pressure therefrom, and calculating the difference between the maximum water pressure and the minimum water pressure to obtain the water pressure difference; The corresponding water column pressure is obtained based on the drilling depth, and the water pressure difference is calculated from the corresponding water column pressure to obtain the corresponding water pressure difference; Preset the allowable fluctuation range of the water pressure correspondence difference, and record the water pressure correspondence difference that is not within the allowable fluctuation range of the water pressure correspondence difference as the water pressure depth deviation; obtain the water pressure depth deviation of all analyzed rocks; The impact degree is obtained by counting the number of analyzed rocks with shallow water pressure deviation and deep water pressure deviation and dividing it by the total number of analyzed rocks. Count all the analysis rocks corresponding to the shallow water pressure deviation and the deep water pressure deviation. If the shallow water pressure deviation and the deep water pressure deviation correspond to the same analysis rock, then record the analysis rock as the key rock. Extract all key rock positions and their corresponding drill hole positions, use the drill hole positions as endpoints, and connect all endpoints in sequence with straight lines to form a closed figure with the largest area that can be formed; Calculate the area of ​​a closed figure and record it as a quantitative face value; The hydrological deviation coefficient is obtained by comprehensively processing the impact degree and the quantitative face value; After normalizing the impact degree and the quantitative face value, the impact degree and the quantitative face value are used as the major and minor semi-axes of the ellipse, respectively, to establish an ellipse model, and the area of ​​the ellipse model is calculated and recorded as the hydrological deviation coefficient. Through the combined use of shallow and deep borehole water pressure monitoring, the water pressure fluctuation characteristics at the surface and deep layers of the rock can be captured in multiple dimensions. This not only accurately identifies the temporal variation pattern of water pressure anomalies, but also quantifies the impact range and concentration of abnormal water pressure through the spatial distribution of key rocks and the area of ​​closed patterns. The innovative integration of impact and quantitative face value into elliptical model calculations takes into account both the proportion of rock mass involved in abnormal water pressure and its spatial aggregation effect. This allows the hydrological deviation coefficient to 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 responses to hydrological risks. The geological deviation coefficient and the hydrological deviation coefficient are weighted and calculated to obtain the geological and hydrological deviation coefficient; Preset weighting factors of the geological deviation coefficient and the hydrological deviation coefficient, and multiply the geological deviation coefficient and the hydrological deviation coefficient with their corresponding weighting factors to obtain the geological and hydrological deviation coefficient; The process of obtaining the construction separation coefficient includes: A preset number of monitoring points are set up in the tunnel. The three-dimensional coordinates of the monitoring points are measured by emitting infrared light, and the vault settlement value is calculated based on the elevation difference between the two measurements. A settlement threshold is preset, and the arch settlement value is compared with the settlement threshold. If the arch settlement value is greater than the settlement threshold, the monitoring point is marked and recorded as a monitoring time-effectiveness point; Starting from the moment when the vault settlement value is obtained, a 4-hour countdown monitoring is performed on the monitoring time-limiting point. The corresponding moment after 4 hours is recorded as the end moment. The completion time point of the support reinforcement at the monitoring time-limiting point is obtained, and the completion time point of the support reinforcement is subtracted from the end moment. The values ​​less than 0 are eliminated, and the values ​​greater than 0 are recorded as the deviation duration. Obtain the deviation duration corresponding to each monitoring point, and extract the maximum deviation duration, which is recorded as the construction separation coefficient; Indicator system construction: Combining geological, hydrological, and construction factors, a multi-level, multi-dimensional set of evaluation indicators was established. First, geological indicators cover rock integrity (such as rock index and fracture development) and rock mass structural characteristics (such as closed projected area and fracture distribution represented by tetrahedral volume), reflecting the stability of the surrounding rock itself. Hydrological indicators include water seepage, water pressure fluctuation (such as water pressure deviation between shallow and deep holes), water pressure influence range (quantified face value), etc., which reflect the erosion and softening effect of groundwater on surrounding rocks; Construction indicators include vault settlement rate, support response timeliness (such as deviation duration), and construction process compliance, representing the intervention effect of the construction process on risk evolution. Each indicator must be measurable and sensitive. The logical hierarchy should be determined through expert experience and engineering specifications, forming a tree-like structure of "goal layer - criterion layer - indicator layer" to provide a quantitative basis for subsequent risk assessment. Fuzzy Hierarchy Analysis: Through fuzzy mathematical theory, it solves the subjective problem of indicator weight assignment in traditional hierarchical analysis method; Specifically include: Invite experts to give fuzzy scores (e.g., using triangular fuzzy numbers or trapezoidal fuzzy numbers) on the relative importance of each factor in the criterion layer and the indicator layer to reflect the uncertainty of expert judgment; Verify the rationality of the judgment matrix through fuzzy mathematical methods (such as fuzzy consistency matrix correction) to ensure the logical consistency of weight assignment; Use fuzzy weight solving algorithms (such as the fuzzy eigenvector method) to determine the weight coefficients of each indicator, so that the weights can reflect the priority of the hierarchical structure and accommodate the fuzzy characteristics of engineering practice (such as the fuzzy boundaries of surrounding rock grade classification); The final weight vector will serve as the basis for weighting indicators in the TOPSIS evaluation, enhancing the scientific nature of risk assessment; TOPSIS evaluation: Based on multi-criteria decision-making theory, it constructs ideal and negative ideal solutions to quantify the distance between each evaluation object and the optimal / worst state, thereby achieving risk ranking. Specifically include: Standardize (e.g., normalize or normalize) the geological and hydrological deviation coefficients and construction separation coefficients to eliminate dimensional differences; Combined with the weights obtained by fuzzy hierarchical analysis, the standardized indicators are weighted to construct a weighted decision matrix; Then, determine the ideal solution and the negative ideal solution: extract the optimal value (such as minimum sedimentation rate) and the worst value (such as maximum water pressure deviation) of each indicator in the weighted matrix to form the ideal solution vector and the negative ideal solution vector; The distance between each assessment object and the ideal solution and the negative ideal solution is calculated using the Euclidean distance formula, and then the risk assessment coefficient (value range [0,1]) is obtained. The closer the risk assessment coefficient is to 1, the higher the risk. The calculations involved in this process are directly quoted from existing technologies and will not be elaborated here. Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The risk assessment coefficient is matched with the value range of the three groups of threshold values ​​to obtain the risk level corresponding to the risk assessment coefficient; the risk levels include low risk, medium risk and high risk.

[0021] Standardization eliminates dimensional differences in indicators such as the geological and hydrological deviation coefficient and the construction separation coefficient to ensure data comparability and avoid the impact of different units on the scientific nature of the assessment results; By combining fuzzy hierarchical analysis with weighted processing, the importance of each indicator in risk assessment is fully considered, making the weighted decision matrix more consistent with 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 Euclidean distance is used to calculate the distance to the ideal / negative ideal solution, converting complex risk information into a quantitative risk assessment coefficient in the interval [0,1], achieving an intuitive expression of the risk level. Three sets of thresholds are preset to match risk levels, and abstract coefficients are mapped to low, medium, and high risk categories, making it easier for engineering personnel to quickly identify risk status and accurately formulate hierarchical management and control strategies, effectively improving the systematicity, objectivity, and operability of tunnel construction risk assessment, and ensuring construction safety and project quality.

[0022] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values ​​in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0023] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS is characterized by: include: Data collection and analysis: Obtain geological and hydrological information data and construction process information data during tunnel construction, and analyze them separately to obtain the geological and hydrological deviation coefficient and construction separation coefficient; Index system construction: Combine geological, hydrological and construction factors to establish a multi-level and multi-dimensional evaluation index set; Fuzzy Hierarchy Analysis: Through fuzzy mathematical theory, it solves the subjective problem of indicator weight assignment in traditional hierarchical analysis method; TOPSIS evaluation: Based on the multi-criteria decision-making theory, by constructing ideal solutions and negative ideal solutions, the distance between each evaluation object and the optimal / worst state is quantified, thereby achieving risk level ranking.

2. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 1 is characterized in that: The information collected during data collection and analysis includes: Acquisition of geological data: measuring the total length of the rock section in the core by coring the core, and image information of the borehole surface; Acquisition of hydrological data: obtaining rock seepage information and its corresponding water pressure information at the borehole; Acquisition of construction process information data: Acquisition of vault settlement data based on the three-dimensional coordinates of monitoring points.

3. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 1 is characterized in that: The process of obtaining the geohydrological deviation coefficient includes: The geological deviation coefficient is obtained after analyzing the geological data; The hydrological deviation coefficient is obtained after analyzing the hydrological data; The geological deviation coefficient and the hydrological deviation coefficient are weighted and calculated to obtain the geological and hydrological deviation coefficient.

4. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 3 is characterized in that: The process of obtaining the geological deviation coefficient includes: Select a preset number of bedrocks from the bedrock through which the tunnel axis passes as test bedrocks; Obtain the core length of each test bedrock and the total footage of the drill bit in sequence, and divide the core length by the total footage of the drill bit to obtain the rock index; Preset the rock index threshold, subtract the rock index threshold from the rock index, remove the values ​​greater than 0 in the obtained values, and take the absolute value of the values ​​less than 0 in the obtained values ​​to obtain the rock difference; Arrange the rock differences corresponding to each test bedrock in descending order according to their numerical values, and sequentially obtain images of the borehole surface corresponding to each rock difference, which are recorded as test images; After preprocessing each test image, the corresponding rock crack features are extracted from it; The rock in the test image is projected onto the projection surface in parallel according to its outer contour; its crack features are also projected onto the projection surface; Obtain the position of each crack on the projection surface, connect the two ends of each crack with a straight line to form a closed figure with the crack, and calculate the area of ​​the closed figure; Extract the area of ​​the largest closed figure from each closed area in the test image as the closed projection area of ​​the test image; A closed projection area threshold is preset, and the closed projection area threshold is subtracted from the closed projection area of ​​each test image. Values ​​less than 0 are removed from the obtained values, and values ​​greater than 0 are recorded as closed projection area differences; Arrange all closed projection surface differences in descending order according to their numerical values, and extract the four largest closed projection surface differences and their corresponding rock positions; The rock boreholes corresponding to the four largest closed projection surface differences are taken as endpoints, and the endpoints of the four rocks are connected in sequence to obtain a tetrahedron. The volume of the tetrahedron is calculated and recorded as the geological deviation coefficient.

5. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 4 is characterized in that: The process of obtaining the hydrological deviation coefficient includes: Determine the location of water seepage rocks in the tunnel and mark the rocks as marked rocks; A container is placed under each marked rock to collect water seepage from the marked rock, and the water seepage volume is read at preset time intervals to obtain the water seepage volume in each time interval and the total water seepage volume collected in the container; Extract the water seepage of the marked rock at each time interval and arrange them according to the size of the water seepage to obtain the maximum water seepage in a single time interval; obtain the maximum water seepage of each marked rock in a single time interval in turn, arrange them in descending order according to the value, and extract the largest k maximum water seepage; Obtain the total water seepage of the container under each marked rock, sort the total water seepage of each marked rock in descending order, and extract the m largest total water seepages; The marked rocks corresponding to the k maximum water seepage amounts and the m maximum total water seepage amounts are counted, and after the corresponding marked rocks are determined, they are recorded as analysis rocks.

6. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 5 is characterized in that: Also includes: Shallow holes were drilled in all the analyzed rocks to obtain the surface fracture water pressure of the analyzed rocks; Obtain and analyze the surface fracture water pressure of the rock at preset time intervals, extract the maximum water pressure and the minimum water pressure, and calculate the difference between the maximum water pressure and the minimum water pressure to obtain the water pressure difference; The corresponding water column pressure is obtained based on the drilling depth, and the water pressure difference is calculated from the corresponding water column pressure to obtain the corresponding water pressure difference; The permissible fluctuation range of the water pressure correspondence difference is preset, and the water pressure correspondence difference that is not within the permissible fluctuation range of the water pressure correspondence difference is recorded as a water pressure shallow deviation; Obtain shallow water pressure deviations for all analyzed rocks; Deep holes were drilled in shallow locations for all analyzed rocks to monitor deep structural water; After analyzing the water pressure at the deep hole, the water pressure depth deviation of all analyzed rocks was obtained; The impact degree is obtained by counting the number of analyzed rocks with shallow water pressure deviation and deep water pressure deviation and dividing it by the total number of analyzed rocks. Count all the analysis rocks corresponding to the shallow water pressure deviation and the deep water pressure deviation. If the shallow water pressure deviation and the deep water pressure deviation correspond to the same analysis rock, then record the analysis rock as the key rock. Extract all key rock positions and their corresponding drill hole positions, use the drill hole positions as endpoints, and connect all endpoints in sequence with straight lines to form a closed figure with the largest area that can be formed; Calculate the area of ​​a closed figure and record it as a quantitative face value; The hydrological deviation coefficient is obtained by comprehensively processing the impact degree and the quantitative face value; After normalizing the impact degree and the quantitative face value, the impact degree and the quantitative face value are used as the major and minor semi-axis of the ellipse respectively, and an ellipse model is established. The area of ​​the ellipse model is calculated and recorded as the hydrological deviation coefficient.

7. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 1 is characterized in that: The process of obtaining the construction separation coefficient includes: A preset number of monitoring points are set up in the tunnel. The three-dimensional coordinates of the monitoring points are measured by emitting infrared light, and the vault settlement value is calculated based on the elevation difference between the two measurements. A settlement threshold is preset, and the arch settlement value is compared with the settlement threshold. If the arch settlement value is greater than the settlement threshold, the monitoring point is marked and recorded as a monitoring time-effectiveness point; Starting from the moment when the vault settlement value is obtained, a 4-hour countdown monitoring is performed on the monitoring time-limiting point. The corresponding moment after 4 hours is recorded as the end moment. The completion time point of the support reinforcement at the monitoring time-limiting point is obtained, and the completion time point of the support reinforcement is subtracted from the end moment. The values ​​less than 0 are eliminated, and the values ​​greater than 0 are recorded as the deviation duration. Obtain the deviation duration corresponding to each monitoring point, and extract the maximum deviation duration, which is recorded as the construction separation coefficient.

8. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 1 is characterized in that: Fuzzy hierarchical analysis, specifically including: Experts were invited to give fuzzy scores on the relative importance of each factor in the criterion layer and the indicator layer to reflect the uncertainty of expert judgment; The rationality of the judgment matrix is ​​verified by fuzzy mathematics methods to ensure the self-consistency of the weight assignment logic; The weight coefficient of each indicator is determined by using the fuzzy weight solution algorithm, so that the weight can reflect the priority of the hierarchy and accommodate the fuzzy characteristics of engineering practice. The final weight vector will be used as the basis for weighting indicators in TOPSIS evaluation.

9. The risk assessment method based on the combination of fuzzy analytic hierarchy process and TOPSIS according to claim 1 is characterized in that: TOPSIS evaluation, including: Standardize the geological and hydrological deviation coefficients and construction separation coefficients to eliminate dimensional differences; Combined with the weights obtained by fuzzy hierarchical analysis, the standardized indicators are weighted to construct a weighted decision matrix; Then, determine the ideal solution and the negative ideal solution: extract the optimal value and the worst value of each indicator in the weighted matrix to form the ideal solution vector and the negative ideal solution vector; The distance between each assessment 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. Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The risk assessment coefficient is matched with the value range of the three groups of threshold values ​​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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