Gypsum rock tunnel water disaster risk evaluation and prevention and control scheme selection method

Abstract

The invention relates to the technical field of tunnel engineering safety prevention and control, and provides a gypsum rock tunnel water disaster risk evaluation and prevention and control scheme selection method which comprises the following steps: step 1-3, based on parameters such as rock mass structural integrity, stratum karst development degree, water yield property, groundwater corrosivity, gypsum rock mineral composition, expansibility and corrosion property, establishing a grading evaluation system, dividing corresponding grades and determining influence indexes R1-R8; 4, adopting an AHP method, an entropy weight method and a CRITIC method to perform multi-dimensional weight calculation on the subjectivity, objectivity and conflict influence indexes, correcting abnormal weights in combination with expert experience and verification rules, generating a comprehensive score, dividing risk grades, and matching graded prevention and control measures; and 5, optimizing and adjusting prevention and control measures in real time through a parameter risk level dynamic judgment and construction method matching mechanism. According to the method, multi-source parameter dynamic empowerment can be fused, and a full-life-cycle prevention and control method with accuracy, timeliness and self-adaptability is provided for tunnel engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering safety prevention and control, and in particular to a method for evaluating water hazard risks in gypsum rock tunnels and selecting a prevention and control plan. Background Art

[0002] Tunnel engineering in gypsum rock is highly geologically sensitive. When anhydrite hydrates with water, its volume theoretically expands by up to 61%. If constrained by surrounding rock, the measured expansion pressure exceeds 10 MPa, 90 times that of marl. Dehydration at high temperatures can cause gypsum rock to shrink and crack, significantly weakening its mechanical strength. Furthermore, gypsum rock dissolution enriches groundwater with sulfate ions, often exceeding 5000 mg / L. This concentration far exceeds the severe corrosion threshold of 1500 mg / L stipulated in the current "Code for Geotechnical Engineering Investigation," accelerating sulfate attack and damage to concrete linings. Consequently, the unique hydraulic, mechanical, and chemical multi-field coupled catastrophic effects of gypsum rock can easily trigger multidimensional degradation of tunnel structures, forming a hazard chain with significant temporal and spatial variations.

[0003] On the other hand, current water hazard risk assessment and prevention and control technologies have multi-dimensional systemic defects: in the risk assessment process, they mostly rely on fixed thresholds and manual experience to divide risk levels, which makes it difficult to adapt to the evolution of geological conditions and the dynamic fluctuation characteristics of monitoring data. In particular, the characteristics of gypsum rock (dissolution and expansion) can easily cause water hazards to have a chain reaction on the structure, causing the evaluation results to lag behind the actual risk changes; traditional methods fail to effectively integrate the subjective and objective empowerment systems, resulting in a disconnect between weight distribution and actual engineering needs; emergency method matching relies too much on manual experience, and lacks the ability to accurately recommend chemical methods.

[0004] Furthermore, existing technologies focus primarily on risk control during construction, lacking a dynamic mechanism for ensuring structural durability during operation, leading to a gap in risk management throughout the entire lifecycle. Therefore, there is an urgent need to develop more timely, accurate, and systematic methods for water hazard risk assessment and prevention. Summary of the Invention

[0005] In order to solve the problems existing in the background technology, the present invention proposes a method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels, which can integrate dynamic weighting of multi-source parameters, construct a comprehensive scoring model of water hazard risk with temporal and spatial evolution, realize dynamic correction of parameter weights and real-time adjustment of prevention and control levels, and combine the priority rules of prevention and control measures with the intelligent matching technology of the emergency plan library to form a closed-loop optimization system covering the surrounding rock stability control during the construction period and the structural durability guarantee during the operation period, thereby realizing adaptive prevention and control of water hazard risks throughout the life cycle of the project.

[0006] To achieve the above objectives, the present invention adopts the following scheme: a method for evaluating the risk of water hazards in gypsum rock tunnels and selecting a prevention and control plan, comprising the following steps: Step 1: Collect hydrogeological data on the gypsum strata at the tunnel site, conduct on-site investigations and combine them with laboratory tests to classify the rock mass structural integrity level and determine the impact index R1, the karst development level of the strata and determine the impact index R2, and the water-rich level of the strata and determine the impact index R3; Step 2: On-site sampling. Based on multiple indoor test results, the groundwater corrosion level on the lining concrete is classified and the impact index R4 is determined. The mineral composition level of the gypsum rock is classified and the impact index R5 is determined. The expansion level of the gypsum rock is classified and the impact index R6 is determined. The dissolution level of the gypsum rock is classified and the impact index R7 is determined. Step 3: Establish the expansion damage index and dissolution degradation index, classify the gypsum rock structure degradation intensity level based on the two indices, and determine the impact index R8; Step 4: Divide the impact indices R1-R8 determined in steps 1-3 into subjective index, objective index, and conflict index, perform multi-dimensional weight calculations, and modify abnormal weights based on expert experience and verification rules to generate a comprehensive score and divide the risk level to match graded prevention and control measures. Step 5: Optimize and adjust prevention and control measures in real time through dynamic determination of parameter risk levels and matching mechanism of working methods.

[0007] Optionally, step 1 specifically includes the following steps: Step 1.1: Collect hydrogeological data on the gypsum rock formation in the tunnel site to determine the rock mass development and groundwater flow conditions of the gypsum rock formation; Step 1.2: Based on the development of gypsum rock, data statistics are collected and the rock mass structural integrity is divided into five levels: intact, relatively intact, relatively broken, broken, and severely broken, with reference to existing standards. The impact index R1∈[0,1] is determined. Step 1.3: Obtain data on cave density, cave size, groundwater flow, and typical cave characteristics through field investigation and laboratory tests. Based on existing standards, classify the karst development degree of the formation into three levels: weak development, moderate development, and strong development, and determine the impact index R2∈[0,1]; Step 1.4: Obtain data on water inflow per unit day, rock permeability, geological structure, and groundwater dynamic characteristics through field investigation and indoor tests. Refer to existing standards to classify the formation water richness into three levels: poor water, moderate water rich, and strong water rich, and determine the impact index R3∈[0,1].

[0008] Optionally, step 2 specifically includes the following steps: Step 2.1: Conduct tests on sulfate ion concentration and pH value of water samples in the tunnel site area, and classify the corrosion level of groundwater on lining concrete into four levels: slight corrosion, weak corrosion, moderate corrosion, and strong corrosion, referring to existing standards, and determine the impact index R4∈[0,1]; Step 2.2: Conduct mineral composition and crystal form testing on gypsum rock samples to obtain the single crystal morphology, gypsum content, and particle size range of the gypsum rock, which will be used to classify the mineral composition of the gypsum rock and determine the impact index R5; Step 2.3: Conduct gypsum rock expansion characteristic tests including free expansion test, saturated water absorption test, confined expansion test, and expansion pressure test, classify the gypsum rock expansion grade into non-expansion, slight expansion, weak expansion, and strong expansion, and determine the influence index R6∈[0,1]; In step 2.4, long-term static water dissolution and dynamic water dissolution tests were carried out to obtain the mass loss rate and sulfate ion release rate under different dissolution conditions, which were used to classify the dissolution grades of gypsum rocks into slight dissolution, weak dissolution, moderate dissolution, and strong dissolution, and to determine the influence index R7∈[0,1].

[0009] Optionally, step 3 specifically includes the following steps: Step 3.1: Perform a free expansion test on the gypsum rock specimen and then conduct a mechanical strength test to obtain the uniaxial saturated compressive strength, i.e., the expansion damage strength (EDS); Step 3.2: Perform a hydrostatic dissolution test on the gypsum rock specimen and then conduct a mechanical strength test to obtain the uniaxial saturated compressive strength, i.e., the dissolution degradation strength (SDS); Step 3.3: Based on the expansion damage strength EDS and dissolution degradation strength SDS, the strength grade of gypsum rock is divided into five grades: hard, relatively hard, relatively soft, soft, and extremely soft, and the gypsum rock structural degradation strength influence index R8∈[0,1] is determined.

[0010] Optionally, step 4 specifically includes the following steps: In step 4.1, for the subjective influence indices R1, R5, and R8, the AHP is used to provide the initial structured weights, and the weights are modified based on the field experience of the abnormal data in combination with the expert experience; Step 4.2: For the objectivity impact indices R3 and R7, the entropy weight method is used to calculate the weight of each impact index; Step 4.3: Calculate the conflict weights of the conflict coupling impact indices R2, R4, and R6 using the CRITIC method; In step 4.4, based on the weights generated in steps 4.1-4.3, the comprehensive water hazard score is calculated; based on the dynamic threshold interval and geological zoning characteristics, the comprehensive score is mapped into a five-level risk system, and graded prevention and control measures are established.

[0011] Optionally, step 4.1 specifically includes the following steps: Step 4.1.1: Use the nine-level scaling method to assign importance scales to the influence indices R1, R5, and R8, construct a group judgment matrix, eliminate abnormal judgment data with logical conflicts through consistency testing, and retain the decision matrices that pass the test; Step 4.1.2: Preset the threshold values of the parameters of the impact indices R1, R5, and R8, and compare the measured data with the threshold range in real time, marking abnormal data that exceeds the threshold range; Step 4.1.3: Dynamically calibrate the abnormal data weights through the Delphi multi-round correction mechanism for the anomaly data; In step 4.1.4, the obtained weight vector is evaluated to determine the weights of the influence indices R1, R5, and R8.

[0012] Optionally, step 4.2 specifically includes the following steps: Step 4.2.1, eliminate the dimension difference of the source data of R3 and R7 to generate a dimensionless data set; Step 4.2.2, calculate the weights of the impact indices R3 and R7; Step 4.2.3, calculate the weights of impact indices R3 and R7; Step 4.2.4, set the verification rules. If the output result of the entropy weight method violates the set verification rules, the expert intervention mechanism will be automatically triggered to manually calibrate the weight distribution logic.

[0013] Optionally, step 4.3 specifically includes the following steps: Step 4.3.1, standardize the data of impact indices R2, R4, and R6; Step 4.3.2: Calculate the standard deviation of each impact index based on the standardized data; Step 4.3.3, calculate the conflict coefficient and the conflict comprehensive score between any two conflict impact indices using the Pearson correlation coefficient method; Step 4.3.4: Calculate the information content of each impact index based on the standard deviation and the comprehensive conflict score, perform normalization, and generate a conflict weight vector; Step 4.3.5, set the verification rules.

[0014] Optionally, step 5 specifically includes: Step 5.1: Set independent risk level thresholds for individual parameters. When the combination of multiple parameters reaches the preset risk level, trigger the superposition of prevention and control measures and adjust the weight distribution to update the comprehensive score and adjust the prevention and control measures; In step 5.2, based on historical engineering cases and real-time monitoring data, high-frequency parameter combinations and corresponding emergency construction methods are statistically analyzed to form a basic construction method association list and a construction method matching list, and the execution priority is divided according to the matching degree.

[0015] The beneficial effects of the present invention are as follows: first, based on the subjective and objective fusion weight distribution mechanism, the AHP method, entropy weight method, CRITIC method and expert experience method are comprehensively used to construct a multi-source parameter dynamic weighting model, and real-time monitoring data and geological zoning characteristics are combined to form a comprehensive scoring system for water hazard risks that evolves in time and space.

[0016] Moreover, through the dynamic threshold interval correction and weight anomaly feedback mechanism, adaptive optimization of parameter weights and hierarchical dynamic assessment of risk levels can be achieved; the weight anomaly feedback mechanism can trigger the weight correction algorithm according to real-time data deviations to ensure dynamic matching of the assessment model with the geological environment, effectively solving the shortcomings of static thresholds and manual experience, and significantly improving the accuracy and timeliness of engineering response measures under complex geological conditions.

[0017] In addition, by integrating the newly built historical case library with real-time monitoring data, intelligent matching rules for parameter combinations and emergency methods were established. Combined with the priority determination of prevention and control measures and the self-learning optimization technology of the emergency plan library, a closed-loop system covering the surrounding rock stability control during the construction period and the structural durability guarantee during the operation period was formed, providing a full-cycle, intelligent solution for the prevention and control of water hazards in gypsum rock formations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A technical roadmap for the water hazard risk assessment and prevention and control solution selection method of the present invention; Figure 2 The figure is a flow chart of the method for water hazard risk assessment and prevention and control scheme selection of the present invention. DETAILED DESCRIPTION

[0019] In order to make the present invention clearer and more understandable, the present invention is described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiment given is only one implementation method and does not represent all embodiments.

[0020] The solubility and expansion properties of gypsum rock and the corrosive properties of groundwater make gypsum rock tunnel projects highly sensitive to groundwater hazards. This can easily lead to multi-dimensional degradation of the tunnel structure, forming a disaster chain with significant temporal and spatial variations. Based on this, this embodiment provides a method for assessing the risk of water hazards in gypsum rock tunnels and selecting a prevention and control plan, including the following steps: Step 1: Collect hydrogeological data of the gypsum rock formations in the tunnel site, conduct on-site investigations and combine them with indoor tests to classify the rock mass structural integrity level and determine the impact index R1, classify the karst development level of the formation and determine the impact index R2, and classify the water-rich level of the formation and determine the impact index R3. Specifically, it includes: Step 1.1: Collect hydrogeological data of the gypsum rock formation in the tunnel site area to determine the rock mass development and groundwater flow conditions of the gypsum rock formation, mainly including the development degree of gypsum rock structural surfaces, the degree of structural surface bonding, the main structural surface types, and the corresponding structural types.

[0021] In step 1.2, data statistics are collected based on the development of the gypsum rock mass. The structural integrity of the gypsum rock mass is classified and the impact index R1 is determined, referring to the current standard "Engineering Rock Mass Classification Standard." The impact index R1 is assigned a standardized value from 0 to 1 (0 represents the worst and 1 represents the best). In this example, the structural integrity of the gypsum rock mass is divided into five levels: intact, relatively intact, relatively broken, broken, and severely broken, as shown in Table 1.

[0022] Table 1 Evaluation of structural integrity grade of gypsum rock mass

[0023] In step 1.3, the density, size, groundwater flow, and typical cave characteristics of the caves were obtained through field investigations and laboratory tests. The existing standard, "Technical Specification for Design and Construction of Highway Karst Tunnels" (JTG / T 3373-2024), was used to classify the stratum karst development grade and determine the impact index R2. The impact index R2 is assigned a standardized value of 0 to 1 (0 represents the worst and 1 represents the best). In this example, the stratum karst development grade is divided into three levels: weak development, moderate development, and strong development, as shown in Table 2.

[0024] Table 2 Evaluation of karst development grade of gypsum rock strata

[0025] In step 1.4, the water inflow per unit day, rock permeability, geological structure, and groundwater dynamic characteristics are obtained through field investigations and indoor tests. The existing standard, "Code for Design of Railway Tunnels" (TB 10003-2016), is used to classify the formation water-richness level and determine the impact index R3. The impact index R3 is assigned a standardized value from 0 to 1 (0 represents the worst and 1 represents the best). In this embodiment, the formation water-richness level is divided into three levels: poor water, moderate water, and strong water, as shown in Table 3.

[0026] Table 3 Evaluation of water-richness level of gypsum rock formations

[0027] Step 2: Collect samples of gypsum rock and groundwater on site, conduct multiple indoor tests, classify the corrosion level of groundwater on lining concrete and determine the impact index R4, classify the mineral composition level of gypsum rock and determine the impact index R5, classify the expansion level of gypsum rock and determine the impact index R6, and classify the dissolution level of gypsum rock and determine the impact index R7. Specifically including: In step 2.1, the sulfate ion concentration and pH value of the water sample at the tunnel site are tested. The existing specification "Code for Geotechnical Engineering Investigation" (GB 50021-2009) is referred to to classify the corrosion level of groundwater on lining concrete and determine the impact index R4. The impact index R4 is assigned a standardized value of 0 to 1 (0 represents the worst and 1 represents the best). In this embodiment, the corrosion level of groundwater on lining concrete is divided into four levels: slight corrosion, weak corrosion, moderate corrosion, and severe corrosion, as described in Table 4.

[0028] Table 4 Evaluation of groundwater corrosion level on lining concrete

[0029] In step 2.2, the mineral composition and crystal form of the gypsum rock sample were tested. The gypsum rock sample was first ground and sieved to obtain gypsum rock powder. Then, XRD combined with SEM was used to test the single crystal morphology, gypsum content, and particle size range of the gypsum rock powder to classify the mineral composition grade of the gypsum rock and determine the influence index R5, as shown in Table 5.

[0030] Table 5 Evaluation of mineral composition grade of gypsum rock

[0031] In step 2.3, gypsum rock expansion characteristic tests, including a free expansion test, a saturated water absorption test, a confined expansion test, and an expansion pressure test, are conducted to classify the gypsum rock expansion grade and determine the impact index R6. The gypsum rock expansion characteristic tests all follow the existing standard "Standard for Geotechnical Test Methods" (GB / T 50123-2019). The impact index R6 is assigned a standardized value of 0 to 1 (0 represents the worst and 1 represents the best). In this embodiment, the gypsum rock expansion grade includes four grades: non-expansion, slight expansion, weak expansion, and strong expansion, as shown in Table 6.

[0032] Table 6 Evaluation of gypsum rock expansion capacity grade

[0033] Step 2.4: Conduct gypsum rock dissolution characteristic tests, including long-term static water dissolution and dynamic water dissolution, to obtain the gypsum rock mass loss rate and sulfate ion release rate under static water and dynamic water dissolution, respectively, to classify the gypsum rock dissolution grade and determine the impact index R7. In this embodiment, the gypsum rock dissolution grade includes four levels: slight dissolution, weak dissolution, moderate dissolution, and strong dissolution. The impact index R7 is assigned a standardized value of 0 to 1 (0 represents the worst and 1 represents the best). The method specifically includes: Step 2.4.1: Conduct a long-term hydrostatic corrosion test to obtain the mass loss rate under hydrostatic corrosion D and sulfate ion release rate; The long-term hydrostatic corrosion test steps are as follows: (1) Cut the gypsum rock sample into a standard cylindrical specimen of Φ50×100 mm. Dry the specimen in an oven at 48°C for 48 h, then take it out and cool it to room temperature. Record the initial dry mass. m 1; (2) Immerse the specimen in 10 times the volume of deionized water for 7 days, and set up 5 parallel samples; (3) Take out the specimen and absorb the free water on the surface of the specimen with an absorbent paper towel until there is no obvious free water dripping from the specimen, then measure the wet mass. m 2. Then dry the specimen in a 48℃ oven for 48h, then take it out and cool it to room temperature, and record the final dry mass. m 3. Calculate the hydrostatic mass loss rate using formula (1) D And take the mean, the formula is: (1) (4) Take 10 ml of the immersion solution to measure the sulfate ion concentration, and take samples three times to calculate the average value.

[0034] Step 2.4.2: Conduct a hydrodynamic corrosion test to obtain the mass loss rate under hydrodynamic corrosion. D ′ and sulfate ion release rate; The steps of the dynamic water corrosion test are as follows: (1) Prepare five groups of standard cylindrical specimens with the same specifications as those used in the long-term static water dissolution test. Place the specimens in a closed dissolution chamber and circulate deionized water at a constant flow rate of 0.5 m / s for 7 days. The water temperature in the dissolution chamber is maintained at 25±1°C. After the test, remove the specimens, remove the free water on the surface, and measure the wet mass 𝑚2′. Then, dry the specimens in a 48°C oven for 48 h, then take them out and cool them to room temperature to measure the dry mass 𝑚3′. Use formula (2) to calculate the mass loss rate of dynamic water dissolution D ′, the formula is: (2) (2) Collect 10 ml of solution from the outlet of the dissolution chamber every 24 hours, measure the average sulfate ion concentration three times, and draw a concentration-time curve.

[0035] Step 2.4.3, by obtaining the mass loss rate of gypsum rock under static water and dynamic water dissolution ( D and D ′) and sulfate ion release rate to quantify the enhancing effect of water velocity on dissolution characteristics.

[0036] In step 2.4.4, based on the mass loss rate and sulfate precipitation capacity, the dissolution grade of gypsum rock was divided into four levels: slight dissolution, weak dissolution, moderate dissolution, and strong dissolution. The impact index R7 was determined to evaluate the degree of dissolution, as shown in Table 7.

[0037] Table 7 Evaluation of gypsum rock dissolution ability grade

[0038] Step 3: Establish the expansion damage index and dissolution degradation index, classify the gypsum rock structure degradation intensity level based on the two indicators, and determine the impact index R8, which includes: Step 3.1: After the gypsum rock specimen is subjected to a free expansion test, a mechanical strength test is performed to obtain the uniaxial saturated compressive strength, i.e., the expansion damage strength (EDS), and to establish a relationship between the mechanical strength and the degree of expansion.

[0039] In step 3.2, the gypsum rock specimens were subjected to hydrostatic dissolution tests and then mechanical strength tests to obtain the uniaxial saturated compressive strength (SDS), also known as the dissolution degradation strength (SDS), and to establish a relationship between mechanical strength and dissolution. It is important to understand that the calculation of uniaxial saturated compressive strength should be conducted in accordance with the "Engineering Rock Mass Classification Standard" (GB / T 50218-2014).

[0040] In step 3.3, based on the expansion damage strength EDS and dissolution degradation strength SDS in steps 3.1 and 3.2, the gypsum rock strength grade is divided into five grades: hard, relatively hard, relatively soft, soft, and very soft. Based on these grades, the gypsum rock structural degradation strength impact index R8 is determined. The impact index R8 is assigned a standardized value of 0 to 1 (0 represents the worst and 1 represents the best), as shown in Table 8.

[0041] Table 8 Evaluation of strength grade of gypsum rock structure degradation

[0042] Step 4: Divide the impact indices R1-R8 determined in steps 1-3 into subjective index, objective index, and conflict index for multi-dimensional weight calculation. Combine expert experience and verification rules to correct abnormal weights, generate a comprehensive score, divide the risk level, and match graded prevention and control measures.

[0043] In this embodiment, 10 groups of generated influence index samples are taken as an example, as shown in Table 9: Table 9 Sample Example

[0044] Step 4.1: For the subjective influence indices R1, R5, and R8, the AHP method plus the expert experience method is used. First, the AHP method is used to provide the initial structured weight values. Then, the weights are modified based on the field experience of the abnormal data combined with the expert experience. Specifically, Step 4.1.1: Use the nine-level scaling method to assign importance scales to the influence indexes R1, R5, and R8, and construct a group judgment matrix; eliminate abnormal judgment data with logical conflicts through consistency testing (CR < 0.1), and retain the decision matrix that passes the test for weight analysis. Taking the data of sample 6 as an example, use the eigenvector method to calculate the initial weight values of R1, R5, and R8 of sample 6, and generate an AHP weight vector that meets the consistency requirements. W AHP =[ w 1; w 5; w 8], the judgment matrix of R1, R5 and R8 of sample 6 is shown in Table 10. Table 10 AHP judgment matrix example

[0045] The column vector of the AHP judgment matrix is normalized, and the calculation formula of the normalized elements is: (3) (4) in, is the element of the normalized AHP judgment matrix; i Representative Bank; j Representative column; is the element of AHP judgment matrix; are the elements of the weight vector; n is the matrix order.

[0046] The calculation of the maximum eigenvalue, consistency index and consistency test value are shown in formulas (5), (6) and (7): (5) (6) (7) in, A norm is the normalized matrix of AHP judgment matrix; lmax is the maximum eigenvalue of the matrix; CI is the consistency indicator; RI Look up the table value for the random consistency indicator. RI Standard table n =3, RI =0.58.

[0047] Substituting R1, R5 and R8 of sample 6 into formulas (3) to (7), we can obtain:

[0048] , so CR<0.1 passes the consistency test.

[0049] Step 4.1.2: preset the threshold values of each parameter of the impact index R1, R5 and R8, and compare the field measured data with the threshold range in real time, and mark the data exceeding ±2 s The abnormal data of the range, the example data is shown in Table 11: Table 11 Example of parameter threshold table (taking the abnormality of a parameter of R8 in sample 6 as an example)

[0050] Step 4.1.3: Dynamically calibrate the weight of abnormal data through the Delphi multi-round correction mechanism for the anomaly data. For example, when the anomaly data is detected to be located in a high-risk geological structure area such as a fault zone or a fold core, the correction process is automatically started. Experts independently determine the AHP weight adjustment requirements based on the rules in Table 12, directly adjust the AHP weight ratio of the corresponding indicator, and generate a weight vector W’ AHP =[ w’ 1; w’ 5; w’ 8], and then substitute the data of sample 6 into step 4.1.1, reconstruct the AHP judgment matrix as shown in Table 13, and generate the weight vector W” AHP =[ w” 1; w” 5; w” 8], we can get: , , so CR<0.1 passes the consistency test.

[0051] Table 12 Example of expert opinion integration table

[0052] In Table 12 above, the expert group determined that the weight of R8 was reduced by 25% (average), and the released weight was distributed to R1 and R5 at 36%:64% (average), generating the revised weight vectorW’ AHP =[0.163; 0.361; 0.475].

[0053] Table 13 Example of the modified AHP judgment matrix

[0054] Step 4.1.4, if the weight vector W” AHP and W’ AHP If the difference is significant, re-score and repeat step 4.1.3. Otherwise, W’ AHP As the benchmark, W” AHP Only used for logical verification (no forced alignment). If the matrix weights are consistent with the Delphi weights (e.g., R5 weights are significantly increased), directly adopt W’ AHP As a result, sample 6 in this embodiment is directly adopted W’ AHP =[0.163; 0.361; 0.475].

[0055] Step 4.2: For the objective impact indices R3 and R7, the entropy weight method is used to calculate the weights of each impact index to ensure the objectivity of the weight distribution. Specifically, the following steps are performed: In step 4.2.1, eliminate the dimension differences of the source data affecting the indices R3 and R7 to generate a dimensionless data set.

[0056] (Positive indicator) (8) (Negative indicator) (9) in, For the standardized sample m No. i Dimensionless data of the impact index; For samples m No. i The original data of the impact index; max( x i )、min( x i ) are samples m No. i The maximum and minimum values of the item's impact index range.

[0057] Since all impact index data fall within the interval [0,1], the original data are directly used as the standardized value. Taking sample 6 as an example, the R3 dimensionless parameter is 0.5; the R7 dimensionless parameter is 0.

[0058] Step 4.2.2: Calculate the weights of the influence indices R3 and R7. The calculation formulas for the influence index weights and entropy values are shown in Equations (10) and (11): (10) (11) in, E i For the ten samples i Entropy value of the item influence index; pmi For samples m Middle i The weight of the impact index is pmi =0, pmi ln pmi =0.

[0059] Step 4.2.3: Calculate the weights of the impact indexes R3 and R7, and calculate the index difference coefficient as shown in formula (12): (12), Perform normalization processing to obtain the objective weight as shown in formula (13): (13) in, g i For the ten samples i The coefficient of variation of the impact index of each item.

[0060] Table 14 Example of entropy value and weight table

[0061] Step 4.2.4, set the verification rules, such as: when the surrounding rock is located in a water-rich fault zone, set the weight of R3 w 3>0.6, when in the strong dissolution area of gypsum rock, set the weight of R7 w 7>0.55; if entropy weight method The output result violates the set verification rules, that is, it is detected w 3≤0.6 or w 7≤0.55, the expert intervention mechanism is automatically triggered, and the weight distribution logic is manually calibrated. As shown in Table 15: When applied to the stability assessment of the surrounding rock of a water-rich fault zone in a tunnel, the entropy weight method automatically calculates w 3=0.509, because it is lower than the preset threshold of 0.6, the alarm signal is triggered. When the formation water richness level is water-rich, the impact index increases and the corresponding weight also increases to above 0.6. Therefore, it is calibrated to above 0.6, and a reliable evaluation result is generated after consistency verification.

[0062] Table 15 Example of verification results

[0063] Step 4.2.5, set up the time-varying and event-driven dual-mode compensation mechanism: define the time-varying attenuation factor γ = 1-0.05 × (number of monitoring days / 30) of the impact index R3, and implement a 5% linear attenuation on the initial weight of R3 every 30 days to compensate for the time-varying deviation of the water inflow monitoring data; set up event-driven compensation for the impact index R7. When the dynamic water dissolution rate is detected to increase by more than 20%, the weight enhancement algorithm is automatically triggered to increase the weight of R7. w 7 is increased to 1.2 times the baseline value until the fluctuation rate of the dissolution rate returns to a stable state. The driving compensation formula is: (14) in, is the weight after compensation; is the dissolution rate growth rate.

[0064] Step 4.3: Calculate the conflict weights of the R2, R4, and R6 impact indices using the CRITIC method. Specifically, the following are the steps: In step 4.3.1, the data of influence indices R2, R4, and R6 are standardized. Since the sample data values are all in the range of [0, 1], no additional standardization is required.

[0065] In step 4.3.2, based on the standardized data, calculate the standard deviation of each impact index according to formula (15). The standard deviation formula is as follows: (15) in, is the standard deviation of the influence index of all samples; is the impact index of all samples i The mean of the items; is the dimensionless data of the i-th influence index of the standardized sample m; N is the number of samples. According to Table 9, N The value is 10.

[0066] From this, the standard deviations of the influence indices R2, R4, and R6 are obtained as follows: s 2=0.408, s 4=0.306, s 6=0.330.

[0067] In step 4.3.3, the conflict coefficient between any two conflict impact indices is calculated using the Pearson correlation coefficient method according to formula (16), and the comprehensive conflict score of each impact index is further calculated according to formula (17).

[0068] (16) (17) in, r e-f For all samples e Impact Index and f Pearson correlation coefficient between item impact indices, i.e., conflict coefficient; CR e-f、g For the e Impact Index and f、g The sum of the conflict coefficients between the influence indices.

[0069] In step 4.3.4, based on the standard deviation and the comprehensive conflict score, the information content of each impact index is calculated according to formula (18), and normalized using formula (19) to generate the conflict weight vector. The information content and weight calculation formulas are shown in formulas (18) and (19): (18) (19) in, C i Impact Index i The amount of information; s i Impact Index i The standard deviation of w i Impact Index i The weight of .

[0070] It can be concluded that the weights of impact indices R2, R4 and R6 are: W CRITIC =( w 2, w 4, w 6)=(0.398; 0.311;0.291); Step 4.3.5, set the verification rule: Take the R4 sub-parameters sulfate concentration and pH value of sample 6 as an example. When the correlation coefficient between sulfate concentration and pH value is less than -0.7, w 4. Weights are locked to ≥ 0.4, and newly generated weights need to be normalized, as shown in Table 16 below.

[0071] Table 16 Example of weight distribution verification table

[0072] In step 4.4, based on the weights generated in steps 4.1-4.3, a comprehensive water hazard score is calculated; based on the dynamic threshold interval and geological zoning characteristics, the score is mapped into a five-level risk system, and a graded early warning and engineering response mechanism is established, as shown in Table 16. The formula for calculating the comprehensive weight is: (20) in, w Summary i For the i The comprehensive weight of the impact index; q is the weight of the impact index of different categories, q ∈[0,1], q AHP + q 熵权 + q CRITIC =1.

[0073] Comprehensive score calculation formula: (twenty one) in, S Provide a comprehensive score for water damage; R i is the impact index, R i ∈[0,1].

[0074] Table 17 Example of graded prevention and control measures

[0075] Step 5: Real-time optimization and adjustment of prevention and control measures through dynamic determination of parameter risk levels and matching mechanism of working methods Measures, including: In step 5.1, set independent risk level thresholds for individual parameters (as shown in the example of risk level table for key parameters in Table 18). When the combination of multiple parameters reaches the preset risk level, trigger the superposition of prevention and control measures and adjust the weight distribution (as shown in the example in Table 19), update the comprehensive score in step 4.4, and adjust the prevention and control measures.

[0076] Table 18 Example of key parameter risk level table

[0077] Table 19 Example of superposition trigger rules

[0078] Use formula (22) to adjust the weight distribution, (twenty two) in, k i is the adjustment coefficient corresponding to the superposition condition; d i It is the condition trigger flag.

[0079] Modify the comprehensive score according to formula (23): (twenty three) in, is the change in weight after adjustment; N is the number of impact indexes.

[0080] In step 5.2, based on historical engineering cases and real-time monitoring data, a database of emergency construction methods with strong parameter correlations was established and updated. Highly correlated water hazard parameter combinations were screened. Through a combination of manual experience and statistical data, a direct correspondence between parameter anomalies and emergency construction methods was established (e.g., "water-rich + dissolution" triggers a combined drainage + grouting solution). This resulted in a list of basic construction method associations, as shown in Table 20. Furthermore, a list of construction method matching degrees was developed based on historical engineering cases, and execution priorities were assigned based on matching degrees (high: matching degree > 0.8; medium: 0.4 < matching degree ≤ 0.8; low: matching degree ≤ 0.4), as shown in Table 21.

[0081] Table 20 Example of a working method related list

[0082] The matching degree of the chemical method is quantified by formula (24) D M , and generate execution priority: (twenty four) in, D M The matching degree of the construction method; α 、 β All are weight coefficients; f is the normalized value of the historical frequency of occurrence of the working method combination; A The probability of successful application of the construction method.

[0083] Table 21 Example of a construction method matching list

Claims

1. A method for evaluating the risk of water hazards in gypsum rock tunnels and selecting a prevention and control plan, characterized in that: The steps include: Step 1: Collect hydrogeological data on the gypsum strata at the tunnel site, conduct on-site investigations and combine them with laboratory tests to classify the rock mass structural integrity level and determine the impact index R1, the karst development level of the strata and determine the impact index R2, and the water-rich level of the strata and determine the impact index R3; Step 2: On-site sampling. Based on multiple indoor test results, the groundwater corrosion level on the lining concrete is classified and the impact index R4 is determined. The mineral composition level of the gypsum rock is classified and the impact index R5 is determined. The expansion level of the gypsum rock is classified and the impact index R6 is determined. The dissolution level of the gypsum rock is classified and the impact index R7 is determined. Step 3: Establish the expansion damage index and dissolution degradation index, classify the gypsum rock structure degradation intensity level based on the two indices, and determine the impact index R8; Step 4: Divide the impact indices R1-R8 determined in steps 1-3 into subjective index, objective index, and conflict index, perform multi-dimensional weight calculations, and modify abnormal weights based on expert experience and verification rules to generate a comprehensive score and divide the risk level to match graded prevention and control measures. Step 5: Optimize and adjust prevention and control measures in real time through dynamic determination of parameter risk levels and matching mechanism of working methods.

2. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 1 is characterized by: The step 1 specifically includes the following steps: Step 1.1: Collect hydrogeological data on the gypsum rock formation in the tunnel site to determine the rock mass development and groundwater flow conditions of the gypsum rock formation; Step 1.2: Based on the development of gypsum rock, data statistics are collected and the rock mass structural integrity is divided into five levels: intact, relatively intact, relatively broken, broken, and severely broken, with reference to existing standards. The impact index R1∈[0,1] is determined. Step 1.3: Obtain data on cave density, cave size, groundwater flow, and typical cave characteristics through field investigation and laboratory tests. Based on existing standards, classify the karst development degree of the formation into three levels: weak development, moderate development, and strong development, and determine the impact index R2∈[0,1]; Step 1.4: Obtain data on water inflow per unit day, rock permeability, geological structure, and groundwater dynamic characteristics through field investigation and indoor tests. Refer to existing standards to classify the formation water richness into three levels: poor water, moderate water rich, and strong water rich, and determine the impact index R3∈[0,1].

3. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 1 is characterized by: The step 2 specifically includes the following steps: Step 2.1: Conduct tests on sulfate ion concentration and pH value of water samples in the tunnel site area, and classify the corrosion level of groundwater on lining concrete into four levels: slight corrosion, weak corrosion, moderate corrosion, and strong corrosion, referring to existing standards, and determine the impact index R4∈[0,1]; Step 2.2: Conduct mineral composition and crystal form testing on gypsum rock samples to obtain the single crystal morphology, gypsum content, and particle size range of the gypsum rock, which will be used to classify the mineral composition of the gypsum rock and determine the impact index R5; Step 2.3: Conduct gypsum rock expansion characteristic tests including free expansion test, saturated water absorption test, confined expansion test, and expansion pressure test, classify the gypsum rock expansion grade into non-expansion, slight expansion, weak expansion, and strong expansion, and determine the influence index R6∈[0,1]; In step 2.4, long-term static water dissolution and dynamic water dissolution tests were carried out to obtain the mass loss rate and sulfate ion release rate under different dissolution conditions, which were used to classify the dissolution grades of gypsum rocks into slight dissolution, weak dissolution, moderate dissolution, and strong dissolution, and to determine the influence index R7∈[0,1].

4. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 1 is characterized by: The step 3 specifically includes the following steps: Step 3.1: Perform a free expansion test on the gypsum rock specimen and then conduct a mechanical strength test to obtain the uniaxial saturated compressive strength, i.e., the expansion damage strength (EDS); Step 3.2: Perform a hydrostatic dissolution test on the gypsum rock specimen and then conduct a mechanical strength test to obtain the uniaxial saturated compressive strength, i.e., the dissolution degradation strength (SDS); Step 3.3: Based on the expansion damage strength EDS and dissolution degradation strength SDS, the strength grade of gypsum rock is divided into five grades: hard, relatively hard, relatively soft, soft, and extremely soft, and the gypsum rock structural degradation strength influence index R8∈[0,1] is determined.

5. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 1 is characterized by: The step 4 specifically includes the following steps: Step 4.1: For the subjective influence indices R1, R5, and R8, the analytic hierarchy process (AHP) is used to provide initial structured weights, and the weights are modified based on the field experience of abnormal data combined with expert experience. Step 4.2: For the objectivity impact indexes R3 and R7, the entropy weight method is used to calculate the weights of the impact indexes R3 and R7; Step 4.3: For the conflict coupling impact indices R2, R4, and R6, the objective weighting method CRITIC is used to calculate the conflict weights of the impact indices R2, R4, and R6; In step 4.4, based on the weights generated in steps 4.1-4.3, the comprehensive water hazard score is calculated; based on the dynamic threshold interval and geological zoning characteristics, the comprehensive score is mapped into a five-level risk system, and graded prevention and control measures are established.

6. A method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 5, characterized in that: The step 4.1 specifically includes the following steps: Step 4.1.1: Use the nine-level scaling method to assign importance scales to the influence indices R1, R5, and R8, construct a group judgment matrix, eliminate abnormal judgment data with logical conflicts through consistency testing, and retain the decision matrices that pass the test; Step 4.1.2: Preset the threshold values of the parameters of the impact indices R1, R5, and R8, and compare the measured data with the threshold range in real time, marking abnormal data that exceeds the threshold range; Step 4.1.3: Dynamically calibrate the abnormal data weights through the Delphi multi-round correction mechanism for the anomaly data; In step 4.1.4, the obtained weight vector is evaluated to determine the weights of the influence indices R1, R5, and R8.

7. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 5 is characterized in that: The step 4.2 specifically includes the following steps: Step 4.2.1, eliminate the dimension difference of the source data of R3 and R7 to generate a dimensionless data set; Step 4.2.2, calculate the weights of the impact indices R3 and R7; Step 4.2.3, calculate the weights of impact indices R3 and R7; Step 4.2.4, set the verification rules. If the output result of the entropy weight method violates the set verification rules, the expert intervention mechanism will be automatically triggered to manually calibrate the weight distribution logic.

8. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 5, characterized in that: The step 4.3 specifically includes the following steps: Step 4.3.1, standardize the data of impact indices R2, R4, and R6; Step 4.3.2: Calculate the standard deviation of each impact index based on the standardized data; Step 4.3.3, calculate the conflict coefficient and the conflict comprehensive score between any two conflict impact indices using the Pearson correlation coefficient method; Step 4.3.4: Calculate the information content of each impact index based on the standard deviation and the comprehensive conflict score, perform normalization, and generate a conflict weight vector; Step 4.3.5, set the verification rules.

9. The method for water hazard risk assessment and prevention and control scheme selection in gypsum rock tunnels according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1: Set independent risk level thresholds for individual parameters. When the combination of multiple parameters reaches the preset risk level, trigger the superposition of prevention and control measures and adjust the weight distribution to update the comprehensive score and adjust the prevention and control measures; In step 5.2, based on historical engineering cases and real-time monitoring data, high-frequency parameter combinations and corresponding emergency construction methods are statistically analyzed to form a basic construction method association list and a construction method matching list, and the execution priority is divided according to the matching degree.

Images