Low-carbon stability conjoint analysis method and system for underground engineering

Abstract

The invention discloses a low-carbon stability conjoint analysis method and system for underground engineering, and particularly relates to the technical field of engineering modeling optimization, and the method comprises the steps: obtaining rock-soil mechanical parameters, construction parameters and full-life-cycle carbon emission factor data of a target area; dividing a collaborative optimization region based on parameter fusion and identifying a conflict section; constructing a mechanical stability numerical model and a carbon emission quantitative model, outputting double indexes, and generating a double-target optimization decision space through dynamic weight distribution; traversing and screening candidate schemes meeting a safety threshold and carbon emission constraints, and extracting an anti-interference Pareto solution set in combination with geological sensitivity simulation; a low-carbon-stability balance scheme is generated through reverse coupling verification, and full-life-cycle low-carbon stability optimization support is provided for complex geological engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering modeling optimization, and more specifically, to a method and system for joint analysis of low-carbon stability of underground engineering. Background Art

[0002] The current stability evaluation system for underground engineering mainly focuses on the independent analysis of a single physical field (such as a mechanical field, a seepage field, or a temperature field). Through means such as numerical simulation, geotechnical parameter tests, and monitoring data feedback, the safety thresholds of engineering structures under loads, deformations, or geological disturbances are verified. The existing technologies focus on mechanical property optimization and disaster prevention and control. For example, the finite element method is used to predict the deformation of surrounding rocks and the design of bolt support strength, etc. However, there is a lack of systematic quantification of carbon emission behaviors during the entire life cycle of engineering construction and operation, and the low-carbon goal is not incorporated into the stability analysis framework. This fragmented research mode makes it difficult to coordinate engineering design with environmental sustainability goals.

[0003] In the prior art, the analysis of a single physical field will lead to the disconnection between the stability evaluation of underground engineering and the carbon emission control goal. Specifically, the traditional method cannot establish a dynamic relationship between structural safety and carbon emission reduction requirements. For example, contradictions such as the substitution of low-carbon materials may cause insufficient strength and energy-saving processes may exacerbate geological disturbances. This isolated analysis mode makes engineering solutions result in resource waste, environmental protection risks, or an increase in long-term operation and maintenance costs. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for joint analysis of low-carbon stability of underground engineering to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for joint analysis of low-carbon stability of underground engineering includes the following steps:

[0007] S1: Obtain geotechnical mechanical parameters, construction parameters, and data of carbon emission factors throughout the life cycle of the target area;

[0008] S2: Divide the collaborative optimization area based on geotechnical mechanical parameters and construction parameters, and identify the conflict sections between stability and carbon emissions;

[0009] S3: Establish a numerical model of mechanical stability and a carbon emission quantification model for the conflict sections, and respectively output mechanical response indicators and carbon emission indicators;

[0010] S4: Input the mechanical response indicators and carbon emission indicators into the coupling analysis framework, and generate a dual-objective optimization decision space through dynamic weight allocation;

[0011] S5: Traverse the bi-objective optimization decision space and screen candidate solutions that simultaneously meet the mechanical safety threshold and carbon emission constraints;

[0012] S6: Generate low-carbon - stability balance solutions based on the candidate solutions.

[0013] In a preferred embodiment, S1 includes:

[0014] Extract geotechnical mechanical parameters based on geological exploration data and in-situ tests, including the elastic modulus of surrounding rock, the geostress gradient, and the distribution of the seepage field;

[0015] Extract construction parameters from the construction design documents, including the energy consumption data of construction machinery and the material transportation path, and associate the construction process time sequence;

[0016] Collect the carbon emission factor data throughout the life cycle, including the carbon emission coefficient per unit mass in the material production stage, the carbon emission coefficient of construction machinery per unit time energy consumption, and the carbon emission coefficient per unit distance of the transportation path;

[0017] Perform spatial interpolation on the geostress gradient and the distribution of the seepage field to generate continuously distributed geotechnical parameter field data;

[0018] Map the energy consumption data of construction machinery to construction nodes according to the process time sequence, and perform path - energy consumption correlation calculation with the material transportation path to output the dynamic carbon emission sequence during the construction stage;

[0019] Integrate the geotechnical parameter field data, the dynamic carbon emission sequence, and the carbon emission coefficient of material production to form a data set of carbon emission factors throughout the life cycle.

[0020] In a preferred embodiment, S2 includes:

[0021] Perform dimensionless normalization on the geotechnical mechanical parameters and the energy consumption data of construction machinery and the material transportation path in the construction parameters to generate a standardized parameter vector;

[0022] Based on the standardized parameter vector, use a clustering algorithm to divide geographical grid units with similar geotechnical mechanical characteristics and construction carbon emission characteristics to form multiple candidate collaborative optimization regions;

[0023] For each candidate collaborative optimization region, calculate its surrounding rock stability safety factor and carbon emission intensity index respectively. The safety factor is determined based on the ratio of the elastic modulus of the surrounding rock to the geostress gradient, and the carbon emission intensity index is determined based on the weighted sum of the energy consumption data of construction machinery and the carbon emissions of the material transportation path;

[0024] Mark the candidate collaborative optimization regions with a safety factor lower than the preset stability threshold and a carbon emission intensity index higher than the preset carbon emission threshold as conflict sections;

[0025] Perform a spatial continuity analysis on adjacent conflict sections, merge the conflict sections that meet the spatial adjacency conditions, and generate a final set of conflict sections.

[0026] In a preferred embodiment, S3 includes:

[0027] Based on the geomechanical parameters of the conflict section, use the finite element method to construct a numerical model of mechanical stability. After applying the construction load boundary conditions, calculate the displacement of the grid nodes and the stress of the supporting structure, and output the area with excessive displacement and the safety factor as mechanical response indicators;

[0028] Based on the construction machinery energy consumption data and the full-life cycle carbon emission factor data in the construction parameters, construct a carbon emission quantification model with construction processes as nodes, accumulate the carbon emissions of mechanical energy consumption, material transportation, and material production at each node, and output the cumulative carbon emissions and carbon emission intensity during the construction stage as carbon emission indicators;

[0029] Input the area with excessive displacement, the safety factor, the cumulative carbon emissions, and the carbon emission intensity into the coupling analysis framework for dual-index joint evaluation;

[0030] Conduct a spatial correlation analysis on the area with excessive displacement and the cumulative carbon emissions to generate a dual-index correlation matrix;

[0031] Based on the correlation matrix, screen the grid cells where both displacement and carbon emissions exceed the limit, and integrate them into a set of high-conflict cells.

[0032] In a preferred embodiment, S4 includes:

[0033] According to the safety factor and carbon emission intensity of the area with excessive displacement, based on the statistical proportional relationship between the safety factor and carbon emission intensity in the historical engineering data, determine the initial weight of mechanical stability and the initial weight of low-carbon constraint respectively;

[0034] Combine the deviation degree of the safety factor from the preset stability threshold and the deviation degree of the carbon emission intensity from the preset carbon emission threshold in the current engineering scenario to calculate the weight adjustment coefficient of mechanical stability and the weight adjustment coefficient of low-carbon constraint;

[0035] Overlay the initial weight and the adjustment coefficient to generate the dynamic weight of mechanical stability and the dynamic weight of low-carbon constraint for each grid cell;

[0036] Based on the dynamic weights, combine the geomechanical parameters and construction parameters of the conflict section for multi-objective constraint matching, and generate an optimization decision space with the optimization priority of safety factor and the optimization priority of carbon emission intensity as the two axes;

[0037] Map the optimization decision space to the geographical grid cells to form a grid-based decision space data set.

[0038] In a preferred embodiment, S5 includes:

[0039] Based on the optimization priorities of mechanical stability and low-carbon constraint optimization in the optimized decision space, generate a set of candidate solutions covering different combinations of support structure parameters, construction process timings, and material selections;

[0040] According to the dynamic weights of each unit in the grid-based decision space data set, iteratively adjust the support structure parameters in the candidate solutions, and preferentially adjust the support parameters corresponding to the units where the dynamic weight of mechanical stability is higher than the preset weight threshold;

[0041] Conduct multi-level constraint checks on the candidate solutions after iterative adjustment, including primary constraint verification and secondary constraint verification, and screen the solutions that pass both levels of verification;

[0042] Based on the process timing correlation in the construction parameters, perform construction feasibility ranking on the verified solutions, and eliminate invalid solutions with process conflicts or resource overlimits;

[0043] Arrange the remaining solutions in descending order of the weighted comprehensive score of safety factor and carbon emission intensity, and select the top-ranked solutions as the final candidate solution set.

[0044] In a preferred embodiment, S6 includes:

[0045] S601: Conduct geological environment sensitivity simulation on the candidate solutions to evaluate the impact of parameter fluctuations on the dual objectives, and extract the anti-interference Pareto solution set;

[0046] S602: Generate a low-carbon - stability balance solution based on the Pareto solution set, and confirm the joint compliance of the revised solution through reverse coupling verification.

[0047] In a preferred embodiment, S601 includes:

[0048] Based on the fluctuation range of surrounding rock parameters in the geological exploration data, generate a random perturbation data set of surrounding rock elastic modulus, in-situ stress gradient, and seepage field distribution through probability distribution simulation;

[0049] Input the perturbation data set into the mechanical stability numerical model and the carbon emission quantification model, and calculate the safety factor fluctuation range and carbon emission intensity fluctuation range of each candidate solution respectively;

[0050] Screen the candidate solutions where the sensitivity of the safety factor to surrounding rock parameter perturbations and the sensitivity of the carbon emission intensity to construction parameter perturbations are both lower than the preset sensitivity threshold;

[0051] Calculate the Pareto front solutions for the screened candidate solutions, and retain the non-inferior solutions where the lower limit of the safety factor fluctuation range is not lower than the preset stability threshold and the upper limit of the carbon emission intensity fluctuation range is not higher than the preset carbon emission threshold;

[0052] Based on the overlap degree and distribution density of the fluctuation ranges of the non-inferior solutions, calculate their fluctuation qualification rates and parameter deviation tolerance degrees, and extract the solution set with a qualification rate not lower than the preset ratio and a deviation tolerance degree not lower than the preset tolerance threshold as the anti-interference Pareto solution set.

[0053] In a preferred embodiment, S602 includes:

[0054] Based on the candidate solutions of the anti-interference Pareto solution set, combined with the engineering constraints in the construction parameters, generate an initial balance plan for the support structure parameters, material selection, and construction process;

[0055] Gradually correct the unit parameters with a safety factor fluctuation qualification rate or carbon emission intensity deviation tolerance degree lower than the preset threshold, and give priority to correcting the unit with the lowest qualification rate;

[0056] Input the corrected plan into the mechanical stability numerical model and the carbon emission quantification model to verify that the safety factor is greater than or equal to the preset stability threshold and the carbon emission intensity is less than or equal to the preset carbon emission threshold;

[0057] Check whether the material usage, mechanical energy consumption, and transportation path of the corrected plan are compatible with the construction resources and equipment configuration;

[0058] Output the plan that passes the verification and is resource-compatible as the final balance plan, otherwise iterate and correct until compliance is met.

[0059] On the other hand, the present invention provides a low-carbon stability joint analysis system for underground engineering, including:

[0060] Data parameter acquisition module: Obtain the geotechnical mechanical parameters, construction parameters, and full-life cycle carbon emission factor data of the target area;

[0061] Conflict section identification module: Divide the collaborative optimization area based on the geotechnical mechanical parameters and construction parameters, and identify the conflict sections between stability and carbon emission;

[0062] Model construction and analysis module: Establish a mechanical stability numerical model and a carbon emission quantification model for the conflict sections, and respectively output mechanical response indicators and carbon emission indicators;

[0063] Dynamic weight allocation module: Input the mechanical response indicators and carbon emission indicators into the coupled analysis framework, and generate a two-objective optimization decision space through dynamic weight allocation;

[0064] Candidate solution screening module: Traverse the bi-objective optimization decision space to screen candidate solutions that simultaneously meet the mechanical safety threshold and carbon emission constraints;

[0065] Disturbance-resistant solution set extraction module: Conduct geological environment sensitivity simulation on candidate solutions to evaluate the impact of parameter fluctuations on the bi-objectives, and extract the disturbance-resistant Pareto solution set;

[0066] Compliant solution generation module: Generate a low-carbon-stability balance solution based on the Pareto solution set, and confirm the joint compliance of the revised solution through reverse coupling verification.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. By integrating geotechnical mechanics parameters, construction parameters, and full-life-cycle carbon emission factor data, a coupled analysis framework for quantifying the dual models of mechanical stability and carbon emissions is constructed, dynamically associating the traditional fragmented mechanical safety evaluation and carbon emission control; by dividing the collaborative optimization region and identifying conflict sections, the contradictory problems such as insufficient strength caused by the replacement of low-carbon materials and increased geological disturbance caused by energy-saving processes are solved, significantly improving the collaborative efficiency between the safety and environmental sustainability of the engineering solution;

[0069] 2. Through the dynamic weight allocation model, the optimization priority is adjusted in real time by combining the safety factor and the deviation threshold of carbon emission intensity to generate a bi-objective decision space that takes into account both stability and low-carbon requirements; further, through geological environment sensitivity simulation and disturbance-resistant Pareto solution set extraction, high-robustness solutions that still meet the dual constraints under parameter perturbations are screened, effectively avoiding the risk of solution failure caused by parameter uncertainty in traditional methods; at the same time, reverse coupling verification is used to ensure a balance between the construction resource matching and long-term operation and maintenance feasibility of the revised solution. Description of the Drawings

[0070] Figure 1 It is a flowchart of a low-carbon stability joint analysis method for an underground project of the present invention;

[0071] Figure 2 It is a structural schematic diagram of a low-carbon stability joint analysis system for an underground project of the present invention;

[0072] Figure 3 It is a flowchart of the low-carbon stability joint analysis of an underground project of the present invention;

[0073] Figure 4 It is a carbon-stability dual-field coupling heat map of an underground project of the present invention. Detailed Embodiments

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0075] Embodiment 1: Figure 1 A low-carbon stability joint analysis method for an underground project of the present invention is given, which includes the following steps:

[0076] S1: Obtain the geotechnical mechanical parameters, construction parameters, and full-life cycle carbon emission factor data of the target area.

[0077] S2: Divide the collaborative optimization area based on the geotechnical mechanical parameters and construction parameters, and identify the conflict sections between stability and carbon emissions.

[0078] S3: Establish a mechanical stability numerical model and a carbon emission quantification model for the conflict sections, and respectively output the mechanical response index and the carbon emission index.

[0079] S4: Input the mechanical response index and the carbon emission index into the coupling analysis framework, and generate a dual-objective optimization decision space through dynamic weight allocation.

[0080] S5: Traverse the dual-objective optimization decision space, and screen out the candidate solutions that simultaneously meet the mechanical safety threshold and the carbon emission constraint.

[0081] S6: Generate a low-carbon-stability balance solution based on the candidate solutions:

[0082] S601: Conduct a geological environment sensitivity simulation on the candidate solutions to evaluate the impact of parameter fluctuations on the dual objectives, and extract the anti-interference Pareto solution set.

[0083] S602: Generate a low-carbon-stability balance solution based on the Pareto solution set, and verify and confirm the joint compliance of the corrected solution through reverse coupling.

[0084] Figure 3The flow chart of the combined analysis of low-carbon stability of underground engineering in the present invention is given. It clarifies the engineering safety threshold and carbon emission reduction target through "defining the objectives and scope" (corresponding to the S1 data collection stage), constructs a multi-source database relying on "geological, construction parameters, and carbon emission factor data" to support the identification of conflict sections in the "inventory analysis" link (S2). In the core modeling stage, the "mechanical numerical model" and the "carbon emission quantification model" perform parallel operations (S3), respectively outputting mechanical response indicators such as the surrounding rock displacement field and support stress, as well as the dynamic sequence of carbon emissions in the material production, mechanical construction, and transportation links, forming a multi-physical field coupling analysis system with "dual-disk drive". Through "impact assessment", dynamic weight allocation is achieved (S4), and the mechanical safety factor and carbon emission intensity are incorporated into a unified decision-making space for multi-objective optimization and screening (S5). Finally, in the "interpretation stage", a "low-carbon - stability balance plan" is generated (S6), completing the full-chain verification from parameter input to optimized output, deeply integrating the traditional "life cycle assessment (LCA)" framework with the stability analysis of underground engineering. Through the real-time interactive verification of the mechanical model and the carbon emission model, a visual decision-making path is provided for engineering sustainability.

[0085] Figure 4 The carbon-stability dual-field coupling heat map of the underground engineering in the present invention is given. Through the thermodynamic color scale mapping technology, the spatial coupling relationship between the carbon emission intensity and mechanical stability in the engineering area is visually presented. The red gradient area (marked with "decreased stability") corresponds to the high-risk section where the safety factor of the surrounding rock is lower than the threshold (such as the conflict section identified in S2), and the blue gradient area (marked with "increased carbon emissions") represents the area where carbon emissions exceed the standard due to intensive transportation of construction materials or high energy consumption of machinery, echoing the calculation results of the carbon emission quantification model in S3; the balance point (the intersection mark in the figure) represents the low-carbon - stability coordination solution selected through the multi-objective optimization and screening in S5, and its coordinate position reflects the balanced decision-making of parameters such as support strength and material carbon footprint in S4; the color band transition feature reveals the spatial distribution law of the anti-interference Pareto solution set in S6 - the overlapping area of red and blue verifies the negative correlation of "high carbon emissions exacerbating geological disturbance", while the transition zone around the balance point points to the feasible space for the substitution of low-carbon materials and the collaborative optimization of support parameters; integrating the traditional mechanical safety assessment (such as displacement nephogram) and the carbon emission analysis throughout the life cycle (such as the dynamic sequence in the construction stage) into a unified geographical space expression not only verifies the engineering value of the "dual-objective optimization decision-making space" but also provides an intuitive basis for the construction party to locate the key areas for carbon emission reduction (such as preferentially transforming the overlapping grid units of red and blue).

[0086] Obtain the geotechnical mechanical parameters, construction parameters, and carbon emission factor data throughout the life cycle of the target area, including:

[0087] Extract geotechnical mechanical parameters based on geological exploration data and on-site tests, including the elastic modulus of the surrounding rock, the in-situ stress gradient, and the distribution of the seepage field.

[0088] Based on the borehole data, core sampling results, and in-situ test data provided in the geological exploration report, the geotechnical mechanical parameters of the target area are extracted. The geotechnical mechanical parameters include the elastic modulus of the surrounding rock, the in-situ stress gradient, and the distribution of the seepage field. Among them, the elastic modulus of the surrounding rock is determined by laboratory uniaxial compression tests, the in-situ stress gradient is obtained through in-situ tests such as the hydraulic fracturing method or the stress relief method, and the distribution of the seepage field is calculated based on the borehole water level monitoring data and the pumping test results.

[0089] Extract construction parameters from the construction design documents, including the energy consumption data of construction machinery and the material transportation routes, and associate them with the construction process time sequence.

[0090] Extract the type, power, and operating time data of construction machinery from the construction design documents, and record the starting point, ending point, and transportation distance information of the material transportation routes. At the same time, according to the construction progress plan document, associate the operating time of the construction machinery and the material transportation routes with the corresponding construction process time sequence nodes. For example, in the tunnel excavation stage, the energy consumption data of construction machinery includes the power and daily operating duration of the roadheader, the material transportation route is the round-trip route from the muck yard to the excavation face, and map this data to the time sequence node of the excavation process.

[0091] Collect the carbon emission factor data throughout the life cycle, including the carbon emission coefficient per unit mass in the material production stage, the carbon emission coefficient per unit time of construction machinery energy consumption, and the carbon emission coefficient per unit distance of the transportation route.

[0092] Collect the carbon emission coefficient per unit mass in the material production stage from the product carbon footprint report provided by the material supplier. For example, the carbon emission coefficient per unit mass of concrete is obtained by summing up the carbon emissions during the production of cement clinker, aggregate mining, and transportation, and the carbon emission coefficient per unit mass of steel is converted based on the energy consumption data of the blast furnace steelmaking process. At the same time, obtain the carbon emission coefficient per unit time of its energy consumption from the energy consumption monitoring system of the construction machinery. For example, the carbon emission coefficient per unit time of fuel consumption of a diesel generator set is calculated based on the carbon oxidation rate of diesel combustion; the carbon emission coefficient per unit distance of the transportation route is determined based on the type of transportation vehicle (such as truck load, fuel efficiency) and the terrain slope of the route.

[0093] Perform spatial interpolation on the in-situ stress gradient and the seepage field distribution to generate continuous distribution data of geotechnical parameters.

[0094] When performing spatial interpolation on the in-situ stress gradient and seepage field distribution in geotechnical mechanical parameters, the inverse distance weighted interpolation method is used to generate continuously distributed geotechnical parameter field data. Specifically, the measured values of the in-situ stress gradient at discrete borehole points are used as input data. The weights are calculated based on the spatial distances between each borehole point and the target grid nodes. The closer the borehole point, the greater the weight. Finally, the in-situ stress value of each grid node is obtained through weighted summation. The interpolation method for the seepage field distribution is similar, and the continuous seepage field is calculated based on the borehole water level data.

[0095] Map the construction machinery energy consumption data to construction nodes according to the process time sequence, and perform path-energy consumption correlation calculation with the material transportation path to output the dynamic carbon emission sequence during the construction stage.

[0096] When mapping the construction machinery energy consumption data to construction nodes according to the process time sequence, the operation time and energy consumption data of the machinery are allocated to the corresponding nodes according to the process time sequence in the construction progress plan. For example, during the construction stage of the support structure, the operation time and energy consumption data of the concrete spraying machine are allocated to the start and end time nodes of this process.

[0097] At the same time, perform path-energy consumption correlation calculation on the material transportation path: calculate the carbon emission of a single transportation according to the distance of the transportation path and the carbon emission coefficient per unit distance of the vehicle, and then combine the transportation frequency of this path in the process time sequence to generate the dynamic carbon emission sequence during the construction stage.

[0098] Integrate the geotechnical parameter field data, dynamic carbon emission sequence, and material production carbon emission coefficient to form a full-life cycle carbon emission factor data set.

[0099] Integrate the geotechnical parameter field data, dynamic carbon emission sequence during the construction stage, and material production carbon emission coefficient into a full-life cycle carbon emission factor data set. The data set includes the following fields:

[0100] Geotechnical parameter field data: in-situ stress gradient and seepage field distribution of grid nodes;

[0101] Construction dynamic carbon emission sequence: carbon emissions from machinery energy consumption and material transportation at each construction node;

[0102] Material production carbon emission coefficient: carbon emission coefficient per unit mass of materials such as concrete and steel.

[0103] Through database table association technology, the above data are associated and stored according to the spatial position (grid node) and time node (construction stage) to form a structured data set that can be used for subsequent model analysis.

[0104] Based on geotechnical mechanical parameters and construction parameters, divide the collaborative optimization area and identify the conflict sections of stability and carbon emissions, including:

[0105] Perform dimensionless normalization on the geotechnical mechanical parameters, the construction machinery energy consumption data and the material transportation path in the construction parameters to generate a standardized parameter vector.

[0106] Perform dimensionless normalization on the obtained geotechnical mechanical parameters (including the elastic modulus of surrounding rock, the in-situ stress gradient and the seepage field distribution) and construction parameters (including the construction machinery energy consumption data and the material transportation path information).

[0107] The specific method of normalization is: taking the maximum and minimum values of each parameter as the reference range, and mapping all parameter values to the interval from 0 to 1 through linear transformation.

[0108] For example, the measured range of the elastic modulus of surrounding rock is from 10 GPa to 50 GPa, and the normalized value is converted to (measured value - 10) / (50 - 10); the measured range of the in-situ stress gradient is from 0.5 MPa / m to 2.5 MPa / m, and the normalized value is converted to (measured value - 0.5) / (2.5 - 0.5).

[0109] The construction machinery energy consumption data and the carbon emissions of the material transportation path are normalized by the same method, and finally a standardized parameter vector containing all parameters is generated.

[0110] Based on the standardized parameter vector, use the clustering algorithm to divide the geographical grid units with similar geotechnical mechanical characteristics and construction carbon emission characteristics to form multiple candidate collaborative optimization regions.

[0111] Specifically, divide the target area into several grid units of equal size (such as square grids with a side length of 50 meters). The geotechnical mechanical characteristics of each grid unit are characterized by the standardized values of the elastic modulus of surrounding rock, the in-situ stress gradient and the seepage field distribution, and the construction carbon emission characteristics are characterized by the standardized values of the construction machinery energy consumption data and the carbon emissions of the material transportation path within the grid unit.

[0112] Calculate the similarity of the geotechnical mechanical characteristics and the construction carbon emission characteristics of each grid unit through the clustering algorithm (such as the K-means algorithm), and merge the grid units with similarity higher than the preset threshold into the same candidate collaborative optimization region. For example, a grid unit in a certain area with a standardized value of the elastic modulus of surrounding rock of 0.8, an in-situ stress gradient of 0.3, a seepage field distribution of 0.6, and a construction machinery energy consumption of 0.7 and a carbon emission of the transportation path of 0.5 will be classified into the same candidate region.

[0113] For each candidate collaborative optimization region, calculate its surrounding rock stability safety factor and carbon emission intensity index respectively. The safety factor is determined based on the ratio of the elastic modulus of surrounding rock to the in-situ stress gradient, and the carbon emission intensity index is determined based on the weighted sum of the construction machinery energy consumption data and the carbon emissions of the material transportation path.

[0114] The calculation method of the safety factor of surrounding rock stability is as follows: Divide the average value of the elastic modulus of the surrounding rock of all grid units in the candidate area by the average value of the in-situ stress gradient to obtain the safety factor of this area. For example, if the average value of the elastic modulus of the surrounding rock in a certain candidate area is 35 GPa and the average value of the in-situ stress gradient is 1.8 MPa / m, then the safety factor is 35 / 1.8 ≈ 19.44.

[0115] The calculation method of the carbon emission intensity index is as follows: Weighted sum the standardized values of the energy consumption data of construction machinery (such as 0.7) and the standardized values of the carbon emissions of the material transportation path (such as 0.5) in the candidate area according to the preset weights (such as the energy consumption weight 0.6 and the transportation weight 0.4) to obtain the carbon emission intensity index value of 0.7×0.6 + 0.5×0.4 = 0.62.

[0116] Mark the candidate collaborative optimization areas with a safety factor lower than the preset stability threshold and a carbon emission intensity index higher than the preset carbon emission threshold as conflict sections.

[0117] Mark the candidate collaborative optimization areas with a safety factor lower than the preset stability threshold (such as safety factor < 20) and a carbon emission intensity index higher than the preset carbon emission threshold (such as carbon emission intensity > 0.6) as conflict sections.

[0118] For example, if the safety factor of a certain candidate area is 19.44 (lower than 20) and the carbon emission intensity is 0.62 (higher than 0.6), it is determined as a conflict section.

[0119] Conduct a spatial continuity analysis on adjacent conflict sections, merge the conflict sections that meet the spatial adjacency conditions, and generate the final set of conflict sections.

[0120] Specifically, check whether the geographical grid units of each conflict section are adjacent in space (such as sharing edges or corners). If there are adjacent conflict sections, merge them into the same conflict section set. For example, two conflict sections A and B contain 5 and 3 grid units respectively. If the grid units of A are adjacent to those of B, then merge A and B into a large conflict section containing 8 grid units. Through the merging operation, the complete set of conflict sections is finally generated for subsequent model construction and optimization.

[0121] It should be noted that the specific values of the preset stability threshold and the preset carbon emission threshold are determined based on the historical engineering data of the target area, industry specifications, and the feasibility analysis of the dual-objective constraints. For example, by statistically analyzing the distribution range of the safety factors of similar underground projects (such as the safety factor of more than 90% of the projects ≥ 20), the lower limit value is selected as the stability threshold; the carbon emission threshold is converted into the upper limit of the regional carbon emission intensity index according to the carbon emission limit per unit of engineering quantity in the national low-carbon construction standard (such as the carbon emission during concrete construction per cubic meter ≤ 300 kgCO2). After the threshold is set, it needs to pass expert review and simulation verification to ensure its feasibility in meeting both engineering safety and low-carbon constraints.

[0122] A numerical model of mechanical stability and a carbon emission quantification model are established for the conflict section, and the mechanical response index and the carbon emission index are respectively output, including:

[0123] Based on the geotechnical mechanical parameters of the conflict section, a numerical model of mechanical stability is constructed by using the finite element method. After applying the construction load boundary conditions, the displacement of the grid nodes and the stress of the support structure are calculated, and the displacement exceeding the standard area and the safety factor are output as the mechanical response index.

[0124] Based on the geotechnical mechanical parameters of each grid element in the conflict section (including the elastic modulus of the surrounding rock, the in-situ stress gradient, and the seepage field distribution), a numerical model of mechanical stability is constructed by using the finite element method. The conflict section is divided into refined grid elements with a side length of 10 meters, and each element is assigned the corresponding elastic modulus of the surrounding rock (determined by laboratory uniaxial compression test, unit: GPa), the in-situ stress gradient (obtained by in-situ hydraulic fracturing test, unit: MPa / m), and the seepage field distribution (calculated based on the borehole water level monitoring data, unit: m 3 / s). Construction load conditions are applied at the model boundary, including:

[0125] Excavation unloading force: Calculated according to the excavation volume and the density of the surrounding rock. For example, when the excavation volume is 100 m 3 , the density of the surrounding rock is 2.5 tons per cubic meter, then the unloading force is 100 cubic meters × 2.5 tons per cubic meter × gravitational acceleration 9.8 m / s2, and the calculation result is 2450 kN;

[0126] Reaction force of the support structure: Determined according to the design bearing capacity of the support structure type (such as anchor bolts, steel arch frames). For example, the design reaction force of a single anchor bolt is 200 kN, and a total of 50 are arranged, then the total reaction force is 200 kN × 50 = 10,000 kN;

[0127] Groundwater seepage pressure: Calculated based on the seepage field distribution and the permeability coefficient. For example, the seepage pressure gradient is 0.1 MPa / m, and the acting area is 50 square meters, then the seepage pressure is 0.1 MPa / m × 50 m = 5 MPa.

[0128] The stiffness coefficient of each grid cell is determined by the elastic modulus of the surrounding rock and the geometric dimensions (length, width, and height) of the cell. Specifically, the stiffness coefficient is equal to the elastic modulus of the surrounding rock multiplied by the cell length and width, and then divided by the cell height. For example, if the elastic modulus of the surrounding rock of a certain cell is 30 GPa, the length is 10 m, the width is 10 m, and the height is 10 m, then the stiffness coefficient is 30 GPa × 10 m × 10 m / 10 m = 300 GPa·m.

[0129] The displacement of each grid node (in millimeters) is calculated by a finite element solver. The calculation formula is that the displacement is equal to the equivalent load divided by the stiffness coefficient.

[0130] The equivalent load is calculated by superimposing the excavation unloading force, the support reaction force, and the seepage pressure. For example, if the equivalent load of a certain node is 2450 kN + 10,000 kN + 5 MPa × 50 m² = 12,450 kN, and the stiffness coefficient is 300 GPa·m, then the displacement is 12,450 kN / 300 GPa·m = 41.5 mm. If it exceeds the preset displacement threshold (e.g., 5 mm), mark the cell where the node is located as the area with excessive displacement.

[0131] The safety factor is determined according to the ratio of the actual stress of the support structure to the allowable tensile strength of the material. For example, if the actual stress of a certain support structure is 150 MPa and the allowable tensile strength of the material is 250 MPa, then the safety factor is 250 MPa / 150 MPa ≈ 1.67.

[0132] Based on the construction machinery energy consumption data and the full - life - cycle carbon emission factor data in the construction parameters, a carbon emission quantification model with construction processes as nodes is constructed. The carbon emissions of machinery energy consumption, material transportation, and material production at each node are accumulated, and the cumulative carbon emissions and carbon emission intensity during the construction stage are output as carbon emission indicators.

[0133] It is calculated by multiplying the mechanical power (in kilowatts) by the operating time (in hours), and then by the carbon emission coefficient per unit energy consumption (for example, the carbon emission coefficient of a diesel generator set is 0.8 kg CO₂ / kWh). For example, if the mechanical power of a certain process is 500 kW and it operates for 10 hours, then the carbon emission is 500 kW × 10 hours × 0.8 kg CO₂ / kWh = 4,000 kg CO₂;

[0134] It is calculated by multiplying the transportation distance (in kilometers) by the carbon emission coefficient per unit distance (for example, for truck transportation it is 2 kg CO₂ / km), and then by the number of transportation times. For example, if the transportation distance is 50 km and the number of transportation times is 10, then the carbon emission is 50 km × 2 kg CO₂ / km × 10 times = 1,000 kg CO₂;

[0135] It is calculated by multiplying the material consumption (in tons) by the carbon emission coefficient per unit mass (for example, 300 kg of carbon dioxide per ton for concrete). For example, if the concrete consumption is 100 tons, the carbon emission is 100 tons × 300 kg of carbon dioxide per ton = 30,000 kg of carbon dioxide.

[0136] Sum up the above three items to obtain the cumulative carbon emissions during the construction stage. For example, the cumulative carbon emissions of a certain process are 4,000 + 1,000 + 30,000 = 35,000 kg of carbon dioxide.

[0137] Input the displacement exceeding standard area, safety factor, cumulative carbon emissions and carbon emission intensity into the coupling analysis framework for dual-index joint evaluation.

[0138] Input the displacement exceeding standard area, safety factor, and cumulative carbon emissions into the coupling analysis framework, and establish a spatial mapping relationship between the displacement exceeding standard units and the high-carbon emission process nodes. For example, match the positions of the displacement exceeding standard units with the positions of the high-carbon emission process nodes to generate a coupling data set.

[0139] For example, a certain displacement exceeding standard unit is located in the excavation process area, and the carbon emission of this process is 35,000 kg of carbon dioxide, and the construction area is 200 square meters. Then the carbon emission intensity is 35,000 kg of carbon dioxide / 200 square meters = 175 kg of carbon dioxide per square meter.

[0140] Conduct a spatial correlation analysis on the displacement exceeding standard area and the cumulative carbon emissions to generate a dual-index correlation matrix.

[0141] When conducting a spatial correlation analysis on the displacement exceeding standard area and the cumulative carbon emissions, calculate the spatial distribution overlap degree of the displacement exceeding standard units and the high-carbon emission units. If more than 70% of the displacement exceeding standard units are simultaneously high-carbon emission units, it is considered that there is a significant spatial correlation between displacement and carbon emission, and a dual-index correlation matrix is generated.

[0142] Based on the correlation matrix, screen the grid units where both displacement and carbon emission exceed the limit, and integrate them into a high-conflict unit set.

[0143] Based on the correlation matrix, screen the grid units that simultaneously meet the displacement exceeding the standard (displacement > 5 mm) and carbon emission exceeding the limit (carbon emission intensity > 300 kgCO2 / m 2 ) and integrate them into a high-conflict unit set. Merge the spatially adjacent high-conflict units (such as units sharing a common side or corner) to form a continuous high-conflict section, and generate a high-conflict unit set for adjacent units for subsequent optimization decisions.

[0144] Input the mechanical response index and the carbon emission index into the coupling analysis framework, and generate a dual-objective optimization decision space through dynamic weight allocation, including:

[0145] Based on the safety factor and carbon emission intensity of the displacement over-standard area, and based on the statistical proportional relationship between the safety factor and carbon emission intensity in historical engineering data, the initial weight of mechanical stability and the initial weight of low-carbon constraint are determined respectively.

[0146] For example, by analyzing similar underground engineering cases, it is statistically found that when the safety factor is lower than 2.0, the probability of structural instability risk is 30%; when the carbon emission intensity exceeds 200 kg CO₂ / m², the probability of environmental protection violations is 40%. According to the risk probability ratio, the initial weight is allocated as 30% for the mechanical stability weight and 40% for the low-carbon constraint weight, and through normalization, the sum of the two is 100%, that is, the initial weight of mechanical stability is 30 / (30 + 40) = 42.8%, and the initial weight of low-carbon constraint is 40 / (30 + 40) = 57.2%.

[0147] Combined with the deviation degree of the safety factor from the preset stability threshold and the deviation degree of the carbon emission intensity from the preset carbon emission threshold in the current engineering scenario, calculate the adjustment coefficient of the mechanical stability weight and the adjustment coefficient of the low-carbon constraint weight.

[0148] Among them, the deviation degree is defined as the relative difference between the actual value and the threshold:

[0149] Deviation degree of mechanical stability: If the actual safety factor of a grid cell is 1.5 and the preset stability threshold is 2.0, then the deviation degree is (2.0 - 1.5) / 2.0 = 25%;

[0150] Deviation degree of low-carbon constraint: If the actual carbon emission intensity of a grid cell is 250 kg CO₂ / m² and the preset carbon emission threshold is 200 kg CO₂ / m², then the deviation degree is (250 - 200) / 200 = 25%.

[0151] The adjustment coefficient is positively correlated with the deviation degree, and the specific proportional relationship is set according to engineering experience. For example, the adjustment coefficient of mechanical stability is set to 50% of the deviation degree, and the adjustment coefficient of low-carbon constraint is set to 80% of the deviation degree. Taking the above deviation degree of 25% as an example, the adjustment coefficient of mechanical stability is 25% × 50% = 12.5%, and the adjustment coefficient of low-carbon constraint is 25% × 80% = 20%.

[0152] Superimpose the initial weight and the adjustment coefficient to generate the dynamic weight of mechanical stability and the dynamic weight of low-carbon constraint for each grid cell.

[0153] For example, the initial weight of a grid cell is 42.8% for mechanical stability and 57.2% for low-carbon constraint, and the adjustment coefficients are 12.5% and 20% respectively. Then the dynamic weights are: dynamic weight of mechanical stability = 42.8% + 12.5% = 55.3%; dynamic weight of low-carbon constraint = 57.2% + 20% = 77.2%.

[0154] Since the sum of weights needs to meet the normalization requirement (the sum is 100%), the above dynamic weights are corrected as follows:

[0155] The corrected weight of mechanical stability = 55.3 / (55.3 + 77.2) = 41.7%;

[0156] The corrected weight of low-carbon constraint = 77.2 / (55.3 + 77.2) = 58.3%.

[0157] Based on the dynamic weights, combined with the geomechanical parameters and construction parameters of the conflict section, multi-objective constraint matching is carried out to generate an optimization decision space with the optimization priority of safety factor and the optimization priority of carbon emission intensity as the two axes.

[0158] Optimization priority of mechanical stability: According to the dynamic weights, prioritize optimizing the areas where the safety factor is lower than the threshold and the weights are higher. For example, for the elements with a weight of 41.7%, if their safety factor is 1.5, optimization is required by increasing the support strength (such as increasing the bolt density by 20%) or adjusting the excavation sequence (such as staged excavation);

[0159] Optimization priority of low-carbon constraint: According to the dynamic weights, prioritize optimizing the areas where the carbon emission intensity exceeds the limit and the weights are higher. For example, for the elements with a weight of 58.3%, if their carbon emission intensity is 250 kg CO₂ / m², optimization is required by replacing low-carbon materials (such as using concrete with a 30% reduction in carbon emission factor) or optimizing the transportation route (such as shortening the transportation distance by 10 km).

[0160] Map the optimization decision space to the geographical grid cells to form a grid-based decision space data set.

[0161] Each grid cell contains the following fields:

[0162] Mechanical stability optimization measures: including the proportion of increased support strength and the adjustment plan of excavation procedures;

[0163] Low-carbon constraint optimization measures: including material replacement strategies and transportation route optimization plans;

[0164] Dual-objective weight allocation: Record the corrected weight of mechanical stability (41.7%) and the weight of low-carbon constraint (58.3%).

[0165] For example, if the dual-objective weights of a grid cell are 41.7% and 58.3%, the optimization decision plan is as follows:

[0166] Mechanical stability optimization: Increase the bolt density to 2 per square meter (originally 1.5), and excavate in two stages;

[0167] Low-carbon constraint optimization: Concrete with a carbon emission coefficient of 210 kg CO₂ / m² (originally 300 kg CO₂ / m²) is adopted, and the transportation route is shortened from 50 km to 40 km.

[0168] Traverse the double-objective optimization decision space and screen candidate solutions that simultaneously meet the mechanical safety threshold and carbon emission constraints, including:

[0169] Based on the optimization priorities of mechanical stability and low-carbon constraint optimization in the optimization decision space, generate a set of candidate solutions covering different combinations of support structure parameters, construction process sequences, and material selections.

[0170] For example, the support structure parameters include bolt density (such as 1.5 bolts per square meter, 2.0 bolts per square meter), steel arch frame spacing (such as 0.5 m, 1.0 m); the construction process sequence includes the number of segments in the excavation stage (such as excavation in 3 segments, excavation in 5 segments); the material selection includes concrete type (such as C30, C40) and steel grade (such as Q235, Q345). Generate an initial set of candidate solutions through permutation and combination. For example, bolt density of 1.5 bolts per square meter + steel arch frame spacing of 0.5 m + excavation in 3 segments + C30 concrete, a total of 20 candidate solutions are formed.

[0171] According to the dynamic weights of each unit in the grid-based decision space data set, iteratively adjust the support structure parameters in the candidate solutions, and preferentially adjust the support parameters corresponding to the units where the dynamic weight of mechanical stability is higher than the preset weight threshold.

[0172] According to the dynamic weights of each unit in the grid-based decision space data set (such as mechanical stability weight of 41.7% and low-carbon constraint weight of 58.3%), iteratively adjust the support structure parameters in the candidate solutions. The adjustment rule is: preferentially adjust the support parameters corresponding to the units where the dynamic weight of mechanical stability is higher than the preset weight threshold (for example, >50%). For example, if the mechanical weight of a grid unit is 55.3%, exceeding the threshold of 50%, then increase the bolt density of this unit from 1.5 bolts per square meter to 2.0 bolts per square meter, and reduce the steel arch frame spacing from 1.0 m to 0.5 m; if the weight is lower than the threshold, keep the original parameters.

[0173] Conduct multi-level constraint checks on the candidate solutions after iterative adjustment, including primary constraint verification and secondary constraint verification, and screen the solutions that pass both levels of verification.

[0174] Conduct multi-level constraint checks on the candidate solutions after iterative adjustment:

[0175] Primary constraint verification: Check whether the safety factor of all grid units in the solution reaches the preset stability threshold (for example, ≥2.0). For example, if the safety factor of a solution after adjustment is 1.8, it is determined as not passing.

[0176] Secondary constraint verification: Check whether the carbon emission intensity of all grid cells in the plan is lower than the preset carbon emission threshold (e.g., ≤200 kg CO₂ / m²). For example, if the carbon emission intensity after adjusting a certain plan is 210 kg CO₂ / m², it is determined as not passing.

[0177] Based on the process sequence relevance in the construction parameters, perform a construction feasibility ranking on the plans that pass the verification, and eliminate the invalid plans with process conflicts or resource overlimits.

[0178] After screening out the plans that pass both levels of verification, further perform a construction feasibility ranking based on the process sequence relevance in the construction parameters. Specifically, it includes:

[0179] Process conflict check: If the process sequence of a certain plan causes the overlapping of equipment usage time (e.g., the same excavator needs to be used for excavation and support in adjacent time periods), it is marked as invalid;

[0180] Resource overlimit check: If the material usage or mechanical energy consumption of a certain plan exceeds the construction resource budget (e.g., the upper limit of daily concrete supply is 500 tons, and the demand of a certain plan is 600 tons), it is marked as invalid.

[0181] Arrange the remaining plans in descending order of the weighted comprehensive score of the safety factor and the carbon emission intensity, and select the top several plans as the final candidate plan set.

[0182] After eliminating the invalid plans, rank the remaining plans according to the weighted comprehensive score of the safety factor and the carbon emission intensity.

[0183] Specifically, the calculation formula for the comprehensive score is:

[0184]

[0185] Among them, Score is the comprehensive score, W s is the weight of mechanical stability (e.g., 0.6), W c is the weight of low-carbon constraint (e.g., 0.4), S actual is the actual safety factor, S th is the stability threshold (2.0), C actual is the actual carbon emission intensity, C th is the carbon emission threshold (200 kg CO₂ / m²).

[0186] Finally, select the top N (e.g., the top 5) plans with the comprehensive score as the final candidate plan set, and select the optimal implementation plan according to the project budget and resource constraints.

[0187] Perform geological environment sensitivity simulation on candidate solutions to evaluate the impact of parameter fluctuations on the dual objectives, and extract the anti-interference Pareto solution set, including:

[0188] Based on the fluctuation range of surrounding rock parameters in geological exploration data, generate a random perturbation data set of the elastic modulus of the surrounding rock, the in-situ stress gradient, and the seepage field distribution through probability distribution simulation.

[0189] Based on the measured fluctuation range of the elastic modulus of the surrounding rock, the in-situ stress gradient, and the seepage field distribution in geological exploration data, generate a random perturbation data set through probability distribution simulation. For example, the measured value of the elastic modulus of the surrounding rock is from 30 GPa to 50 GPa. Assuming it follows a normal distribution with a standard deviation of 5 GPa, 1000 groups of random perturbation data are generated through the Monte Carlo method. Each group of data includes the elastic modulus of the surrounding rock, the in-situ stress gradient, and the seepage field distribution, ensuring that the perturbed parameter values are within the measured fluctuation range.

[0190] Input the perturbation data set into the numerical model of mechanical stability and the carbon emission quantification model, and calculate the fluctuation range of the safety factor and the fluctuation range of the carbon emission intensity for each candidate solution respectively.

[0191] For example, the safety factor of a certain candidate solution is 2.2 without perturbation, and the calculated result after perturbation is from 2.0 to 2.4; the carbon emission intensity is 180 kg CO₂ / m² without perturbation, and it is from 170 to 190 kg CO₂ / m² after perturbation.

[0192] Screen out the candidate solutions whose sensitivities of the safety factor to the perturbation of surrounding rock parameters and the carbon emission intensity to the perturbation of construction parameters are both lower than the preset sensitivity threshold.

[0193] Use the sensitivity index analysis method to evaluate the impact of parameter fluctuations on the dual objectives. The sensitivity index is defined as the ratio of the change rate of the target value to the perturbation rate of the parameter.

[0194] Specifically, the calculation formula for the sensitivity index is:

[0195]

[0196] Among them, S s is the sensitivity of the safety factor to the elastic modulus of the surrounding rock, S c is the sensitivity of the carbon emission intensity to the seepage field distribution, ΔS is the change amount of the safety factor, ΔE R is the perturbation amount of the elastic modulus of the surrounding rock, ΔC is the change amount of the carbon emission intensity, ΔH F is the perturbation amount of the seepage field distribution.

[0197] For example, when ΔE R = 5 GPa, ΔS = 0.2, then S s=(0.2 / 2.2) / (5 / 30)≈0.12. If the preset sensitivity threshold is 0.15, it is determined that the sensitivity of this solution meets the standard.

[0198] Calculate the Pareto front solution for the screened candidate solutions, and retain the non-inferior solutions whose lower limit of the safety factor fluctuation range is not lower than the preset stability threshold and the upper limit of the carbon emission intensity fluctuation range is not higher than the preset carbon emission threshold.

[0199] Screen the Pareto front solutions for the candidate solutions with qualified sensitivity. The Pareto front is defined as the set of solutions that cannot further optimize one of the objectives without degrading the other objective under the dual objectives of safety factor and carbon emission intensity. For example, if the safety factor of solution A is 2.3 and the carbon emission intensity is 200, and that of solution B is 2.1 and the carbon emission intensity is 180, if the carbon emission intensity of solution B is better than that of A and the safety factor is not lower than the threshold, then solution B is a Pareto non-inferior solution.

[0200] Based on the overlap degree and distribution density of the fluctuation ranges of the non-inferior solutions, calculate their fluctuation qualification rate and parameter deviation tolerance, and extract the solution set with a qualification rate not lower than the preset ratio and a deviation tolerance not lower than the preset tolerance threshold as the anti-interference Pareto solution set.

[0201] Calculate the robustness index based on the overlap degree and distribution density of the fluctuation ranges of the non-inferior solutions:

[0202] Fluctuation qualification rate: Count the proportion of samples where the non-inferior solutions still meet the conditions of safety factor ≥ 2.0 and carbon emission intensity ≤ 200 after perturbation. For example, if a certain solution is qualified 850 times out of 1000 perturbations, the qualification rate is 85%;

[0203] Parameter deviation tolerance: Calculate the tolerance range of the lower limit of the safety factor fluctuation and the upper limit of the carbon emission intensity fluctuation of the non-inferior solutions deviating from the preset threshold.

[0204] Specifically, the calculation formula for the parameter deviation tolerance is:

[0205]

[0206] In the formula, T s and T c are the deviation tolerances of the safety factor and carbon emission intensity respectively, S min is the lower limit of the safety factor fluctuation range, C max is the upper limit of the carbon emission intensity fluctuation range, S th = 2.0, C th = 200.

[0207] For example, for a certain solution, S min = 2.05, C max = 195, then T s=(2.05 - 2.0) / 2.0 = 2.5%, T c =(200 - 195) / 200 = 2.5%. If the preset tolerance threshold is 2%, then this solution meets the standard.

[0208] Finally, extract the solution set with a qualification rate ≥ 80% and a tolerance ≥ 2% as the anti-interference Pareto solution set. For example, it contains 10 highly robust solutions for subsequent reverse coupling verification.

[0209] Generate a low-carbon - stability balance solution based on the Pareto solution set, and confirm the joint compliance of the revised solution through reverse coupling verification, including:

[0210] Based on the candidate solutions in the anti-interference Pareto solution set, combined with the engineering constraints in the construction parameters, generate an initial balance solution for the support structure parameters, material selection, and construction process.

[0211] Select candidate solutions from the anti-interference Pareto solution set. For example, select Solution A with a passing rate of safety factor fluctuation of 85% and a deviation tolerance of carbon emission intensity of 2.5% (support parameters: bolt density 2.0 roots per square meter, steel arch frame spacing 0.5 meters; material selection: C30 concrete; process sequence: excavated in 3 sections). Combined with the engineering constraint conditions in the construction parameters (such as the upper limit of daily concrete supply of 500 tons, maximum number of excavator shifts of 2 units), generate an initial balance solution.

[0212] Perform hierarchical correction on the unit parameters with a passing rate of safety factor fluctuation or a deviation tolerance of carbon emission intensity lower than the preset threshold, and give priority to correcting the unit with the lowest passing rate.

[0213] Perform hierarchical correction on the units in the initial solution with a passing rate of safety factor fluctuation or a deviation tolerance of carbon emission intensity lower than the preset threshold (passing rate threshold 80%, tolerance threshold 2%).

[0214] For example, if the passing rate of a certain unit is 75% and the tolerance is 1.8%, then first adjust its support parameters: increase the bolt density from 1.5 roots per square meter to 2.0 roots per square meter, and reduce the steel arch frame spacing from 1.0 meter to 0.8 meter to improve the stability of the safety factor; at the same time, replace the concrete type from C40 to C30 (carbon emission coefficient reduced by 15%) to improve the tolerance of carbon emission intensity.

[0215] Input the revised solution into the mechanical stability numerical model and the carbon emission quantification model to verify that the safety factor is greater than or equal to the preset stability threshold and the carbon emission intensity is less than or equal to the preset carbon emission threshold.

[0216] Input the revised parameters into the mechanical stability numerical model and the carbon emission quantification model for reverse verification.

[0217] For example, if the calculated safety factor of the revised plan is 2.1 (≥ the preset threshold of 2.0) and the carbon emission intensity is 195 kg CO₂ / m² (≤ the preset threshold of 200), it is determined to pass the verification. If the safety factor of a certain plan after revision is 1.9 or the carbon emission intensity is 210, it is marked as failed and iterative correction is triggered.

[0218] Check whether the material consumption, mechanical energy consumption, and transportation route of the revised plan are compatible with the construction resources and equipment configuration.

[0219] Conduct a construction resource matching check on the plan that has passed the verification:

[0220] Material consumption check: If the daily demand for concrete in the revised plan is 550 tons (exceeding the supply upper limit of 500 tons), adjust the process timing sequence. For example, change the excavation in 3 sections to 4 sections to reduce the daily demand to 480 tons;

[0221] Mechanical energy consumption check: If the demand for excavators in the revised plan is 3 work shifts (exceeding the maximum configuration of 2 work shifts), optimize the transportation route to reduce equipment idle time and reduce the number of work shifts to 2;

[0222] Transportation route compatibility: If the revised transportation distance increases from 50 km to 60 km (exceeding the vehicle's single-day mileage limit), increase the number of transportation vehicles or adjust the route to relay transportation.

[0223] Output the plan that has passed the verification and is resource-compatible as the final balanced plan, otherwise, perform iterative correction until compliance is met.

[0224] If the revised plan passes the verification and is resource-matched (for example, the adjusted daily concrete usage is 480 tons, the number of excavator work shifts is 2, and the transportation distance is 45 km), it is output as the final low-carbon - stability balanced plan; otherwise, repeat the correction and verification steps until the requirements are met.

[0225] For example, Plan B needs to be corrected twice due to excessive carbon emission intensity: increase the fuel efficiency of the transportation vehicle from 8 liters / km to 7 liters / km and re-verify until it is qualified.

[0226] Example 2: Figure 2 The structural schematic diagram of a low-carbon stability joint analysis system for an underground project of the present invention is given. A low-carbon stability joint analysis system for an underground project includes:

[0227] Data parameter acquisition module: Obtain geotechnical mechanics parameters, construction parameters, and full-life cycle carbon emission factor data of the target area.

[0228] Conflict section identification module: Divide the collaborative optimization area based on geotechnical mechanics parameters and construction parameters, and identify the conflict sections between stability and carbon emissions.

[0229] Model construction and analysis module: A numerical model of mechanical stability and a carbon emission quantification model are established for the conflict section, and the mechanical response index and the carbon emission index are output respectively.

[0230] Dynamic weight allocation module: The mechanical response index and the carbon emission index are input into the coupled analysis framework, and a two-objective optimization decision space is generated through dynamic weight allocation.

[0231] Candidate solution screening module: Traverse the two-objective optimization decision space to screen candidate solutions that simultaneously meet the mechanical safety threshold and the carbon emission constraint.

[0232] Anti-interference solution set extraction module: Conduct a geological environment sensitivity simulation on the candidate solutions to evaluate the impact of parameter fluctuations on the two objectives, and extract the anti-interference Pareto solution set.

[0233] Compliant solution generation module: Generate a low-carbon - stability balance solution based on the Pareto solution set, and confirm the joint compliance of the revised solution through reverse coupling verification.

[0234] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0235] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0236] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0237] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0238] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0239] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0240] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0241] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0242] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A combined analysis method for low-carbon stability of underground engineering, characterized in that, It includes the following steps: S1: Obtain the geotechnical mechanical parameters, construction parameters, and full-life-cycle carbon emission factor data of the target area; S2: Divide the collaborative optimization area based on the geotechnical mechanical parameters and construction parameters, and identify the conflict sections between stability and carbon emissions; S3: Establish a mechanical stability numerical model and a carbon emission quantification model for the conflict sections, and respectively output the mechanical response index and the carbon emission index; S4: Input the mechanical response index and the carbon emission index into the coupling analysis framework, and generate a dual-objective optimization decision space through dynamic weight allocation; S5: Traverse the dual-objective optimization decision space, and screen the candidate solutions that simultaneously meet the mechanical safety threshold and the carbon emission constraint; S6: Generate a low-carbon-stability balance solution based on the candidate solutions.

2. The low-carbon stability joint analysis method for an underground project according to claim 1, characterized in that S1 includes: Extract the geotechnical mechanical parameters based on the geological exploration data and on-site tests, including the elastic modulus of the surrounding rock, the in-situ stress gradient, and the seepage field distribution; Extract the construction parameters from the construction design documents, including the energy consumption data of the construction machinery and the material transportation path, and associate the construction process time sequence; Collect the full-life-cycle carbon emission factor data, including the carbon emission coefficient per unit mass in the material production stage, the carbon emission coefficient of the construction machinery per unit time energy consumption, and the carbon emission coefficient per unit distance of the transportation path; Perform spatial interpolation processing on the in-situ stress gradient and the seepage field distribution to generate continuously distributed geotechnical parameter field data; Map the energy consumption data of the construction machinery to the construction nodes according to the process time sequence, and perform path-energy consumption correlation calculation with the material transportation path, and output the dynamic carbon emission sequence in the construction stage; Integrate the geotechnical parameter field data, the dynamic carbon emission sequence, and the carbon emission coefficient of the material production to form a full-life-cycle carbon emission factor data set.

3. A low-carbon stability joint analysis method for underground engineering according to claim 1, characterized in that S2 It includes: Perform dimensionless normalization processing on the geotechnical mechanical parameters and the energy consumption data of the construction machinery and the material transportation path in the construction parameters to generate a standardized parameter vector; Based on the standardized parameter vector, use the clustering algorithm to divide the geographical grid units with similar geotechnical mechanical characteristics and construction carbon emission characteristics to form multiple candidate collaborative optimization areas; For each candidate collaborative optimization area, calculate its surrounding rock stability safety factor and carbon emission intensity index respectively. The safety factor is determined based on the ratio of the elastic modulus of the surrounding rock to the in-situ stress gradient, and the carbon emission intensity index is determined based on the weighted sum of the energy consumption data of the construction machinery and the carbon emissions of the material transportation path; Mark the candidate collaborative optimization areas with a safety factor lower than the preset stability threshold and a carbon emission intensity index higher than the preset carbon emission threshold as conflict sections; Perform spatial continuity analysis on adjacent conflict sections, and merge the conflict sections that meet the spatial adjacency conditions to generate the final set of conflict sections.

4. A low-carbon stability joint analysis method for underground engineering according to claim 1, characterized in that S3 includes: Based on the geotechnical mechanical parameters of the conflict section, use the finite element method to construct a mechanical stability numerical model. After applying the construction load boundary conditions, calculate the displacement of the grid nodes and the stress of the support structure, and output the displacement exceeding standard area and the safety factor as the mechanical response index; Based on the construction machinery energy consumption data and the full - life - cycle carbon emission factor data in the construction parameters, a carbon emission quantification model with construction processes as nodes is constructed. The carbon emissions of mechanical energy consumption, material transportation, and material production at each node are accumulated, and the cumulative carbon emissions and carbon emission intensity during the construction stage are output as carbon emission indicators; The displacement - exceeding area, safety factor, cumulative carbon emissions, and carbon emission intensity are input into the coupling analysis framework for dual - index joint evaluation; Perform a spatial correlation analysis on the displacement - exceeding area and cumulative carbon emissions to generate a dual - index correlation matrix; Based on the correlation matrix, screen the grid cells where both displacement and carbon emissions exceed the limit, and integrate them into a set of high - conflict cells.

5. A low-carbon stability joint analysis method for underground engineering according to claim 1, characterized in that S4 includes: According to the safety factor and carbon emission intensity of the displacement - exceeding area, based on the statistical proportional relationship between the safety factor and carbon emission intensity in the historical project data, determine the initial weight of mechanical stability and the initial weight of low - carbon constraint respectively; Combined with the deviation degree of the safety factor from the preset stability threshold and the deviation degree of the carbon emission intensity from the preset carbon emission threshold in the current project scenario, calculate the mechanical stability weight adjustment coefficient and the low - carbon constraint weight adjustment coefficient; Overlay the initial weight and the adjustment coefficient to generate the dynamic weight of mechanical stability and the dynamic weight of low - carbon constraint for each grid cell; Based on the dynamic weights, combined with the geotechnical mechanical parameters and construction parameters of the conflict section, perform multi - objective constraint matching to generate an optimization decision space with the optimization priority of safety factor and the optimization priority of carbon emission intensity as the two axes; Map the optimization decision space to the geographical grid cells to form a grid - based decision space data set.

6. The low-carbon stability joint analysis method for an underground project according to claim 1, wherein S5 Includes: Based on the optimization priority of mechanical stability and the optimization priority of low - carbon constraint in the optimization decision space, generate a set of candidate solutions covering different support structure parameters, construction process timings, and material selection combinations; According to the dynamic weights of each cell in the grid - based decision space data set, iteratively adjust the support structure parameters in the candidate solutions, and preferentially adjust the support parameters corresponding to the cells where the dynamic weight of mechanical stability is higher than the preset weight threshold; Conduct multi - level constraint checks on the iteratively adjusted candidate solutions, including primary constraint verification and secondary constraint verification, and screen the solutions that pass both levels of verification; Based on the process - timing correlation in the construction parameters, sort the feasible construction of the solutions that pass the verification, and eliminate the invalid solutions with process conflicts or resource over - limits; Arrange the remaining solutions in descending order of the weighted comprehensive score of the safety factor and carbon emission intensity, and select the top - ranked multiple solutions as the final candidate solution set.

7. A low-carbon stability joint analysis method for underground engineering according to claim 1, characterized in that S6 Includes: S601: Conduct a geological environment sensitivity simulation on the candidate solutions to evaluate the impact of parameter fluctuations on the dual objectives, and extract the anti - interference Pareto solution set; S602: Generate a low - carbon - stability balance solution based on the Pareto solution set, and confirm the joint compliance of the revised solution through reverse - coupling verification.

8. A low-carbon stability joint analysis method for underground engineering according to claim 7, characterized in that, S601 includes: Based on the fluctuation range of surrounding - rock parameters in the geological exploration data, generate a random perturbation data set of surrounding - rock elastic modulus, in - situ stress gradient, and seepage field distribution through probability distribution simulation; Input the disturbed dataset into the numerical model of mechanical stability and the carbon emission quantification model, and calculate the fluctuation range of the safety factor and the fluctuation range of the carbon emission intensity for each candidate solution respectively; Screen the candidate solutions whose sensitivity of the safety factor to the disturbance of surrounding rock parameters and the sensitivity of the carbon emission intensity to the disturbance of construction parameters are both lower than the preset sensitivity threshold; Perform the Pareto front solution calculation for the screened candidate solutions, and retain the non-dominated solutions whose lower limit of the safety factor fluctuation range is not lower than the preset stability threshold and the upper limit of the carbon emission intensity fluctuation range is not higher than the preset carbon emission threshold; Based on the overlap degree and distribution density of the fluctuation ranges of the non-dominated solutions, calculate their fluctuation qualification rate and parameter deviation tolerance, and extract the solution set with a qualification rate not lower than the preset ratio and a deviation tolerance not lower than the preset tolerance threshold as the anti-interference Pareto solution set.

9. A low-carbon stability joint analysis method for underground engineering according to claim 7, characterized in that S602 includes: Based on the candidate solutions of the anti-interference Pareto solution set, combine the engineering constraints in the construction parameters to generate an initial balance solution for the support structure parameters, material selection, and construction process; Perform hierarchical correction on the unit parameters with a safety factor fluctuation qualification rate or a carbon emission intensity deviation tolerance lower than the preset threshold, and preferentially correct the unit with the lowest qualification rate; Input the corrected solution into the numerical model of mechanical stability and the carbon emission quantification model to verify that the safety factor is greater than or equal to the preset stability threshold and the carbon emission intensity is less than or equal to the preset carbon emission threshold; Check whether the material consumption, mechanical energy consumption, and transportation route of the corrected solution are compatible with the construction resources and equipment configuration; Output the solution that passes the verification and is resource-compatible as the final balance solution, otherwise iterate and correct until compliance is met.

10. A low-carbon stability joint analysis system for underground engineering, which is used to implement the low-carbon stability joint analysis method for underground engineering according to any one of claims 1-9, characterized in that, Includes: Data parameter acquisition module: Obtain the geotechnical mechanical parameters, construction parameters, and full-life cycle carbon emission factor data of the target area; Conflict section identification module: Divide the collaborative optimization area based on the geotechnical mechanical parameters and construction parameters, and identify the conflict sections between stability and carbon emissions; Model construction and analysis module: Establish a numerical model of mechanical stability and a carbon emission quantification model for the conflict sections, and output the mechanical response index and the carbon emission index respectively; Dynamic weight allocation module: Input the mechanical response index and the carbon emission index into the coupling analysis framework, and generate a two-objective optimization decision space through dynamic weight allocation; Candidate solution screening module: Traverse the two-objective optimization decision space and screen the candidate solutions that simultaneously meet the mechanical safety threshold and the carbon emission constraint; Anti-interference solution set extraction module: Perform a geological environment sensitivity simulation on the candidate solutions to evaluate the impact of parameter fluctuations on the two objectives, and extract the anti-interference Pareto solution set; Compliant solution generation module: Generate a low-carbon-stability balance solution based on the Pareto solution set, and confirm the joint compliance of the corrected solution through reverse coupling verification.

Images