A multi-tunnel excavation efficiency advancement evaluation and resource allocation optimization method

Abstract

This invention provides a method for evaluating the efficiency advancement and optimizing resource allocation in multi-tunnel excavation, belonging to the field of intelligent management technology for tunnel construction. The method includes: selecting tunnel excavation input and output indicators; using the evaluation period as a unit and grouping by surrounding rock grade, obtaining processed input and output indicators through data preprocessing and standardization; constructing a decision unit from the excavation data of each tunnel within the evaluation period for the same surrounding rock grade, and establishing a variable returns-to-scale model; performing a pre-set efficiency advancement evaluation based on the variable returns-to-scale model, obtaining the evaluation results, introducing slack variables, constructing and solving a data envelopment analysis model of excavation efficiency, obtaining the production front projection point, and adjusting the construction resource allocation optimization. This invention solves the problem of incomparable excavation efficiency between different tunnels due to objective differences in geological conditions and provides quantitative decision support for the optimal allocation of construction resources.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management technology for tunnel construction, and in particular relates to a method for evaluating the efficiency of multi-tunnel excavation and optimizing resource allocation. Background Technology

[0002] As a backbone node in infrastructure networks such as highways, railways, and water conservancy, tunnel engineering is experiencing a continuous increase in construction scale and complexity, leading to greater challenges in construction, management, and control. Tunnel construction efficiency, a key control indicator, directly impacts project economics and schedule controllability. Due to significant differences in geological conditions, construction methods, and resource allocation among different tunnel projects, conventional methods struggle to establish a unified and comparable benchmark framework, making it difficult to compare and evaluate excavation efficiency across projects. This hinders the coordinated management of multi-tunnel cluster construction by construction managers, making it difficult to effectively assess the advanced level of each tunnel's construction and the redundancy of construction resources. Existing methods for evaluating tunnel excavation efficiency typically rely on manual calculations, and the evaluation and horizontal comparison of excavation efficiency within a tunnel group depend heavily on managerial experience, resulting in a high degree of subjectivity.

[0003] Data envelopment analysis (DEA), as a multi-input, multi-output efficiency evaluation method, has been initially applied in the field of engineering efficiency evaluation, providing a possibility for solving the problem of horizontal comparison among multiple decision-making units. However, existing DEA research has not yet solved the problem of unified evaluation and resource optimization under heterogeneous conditions of multiple tunnels, mainly due to the following limitations: Existing technologies often rely on macro-level indicators in their DEA model index systems (such as funding and number of patents in enterprise innovation investment optimization problems; water quality and per capita GDP in water resource benefit accounting problems). These indicators are not suitable for dynamic investment scenarios involving multiple processes and resources in tunnel construction. Furthermore, they focus on vertical optimization of a single decision-making unit (such as investment adjustments for a single enterprise in different years; water resource benefit accounting for a region in different years) without involving horizontal comparisons of multiple objects, thus failing to address the efficiency comparison problem between multiple tunnels. In some existing technologies, the decision-making units are assumed to satisfy the homogeneity assumption. Under the engineering context of this invention, this cannot solve the incomparability problem caused by objective differences in the external environment (such as the surrounding rock grade in tunnel engineering), resulting in the evaluation results failing to truly reflect the management efficiency level.

[0004] Existing DEA-related research or indicator systems are not compatible with tunnel construction scenarios or fail to effectively address the heterogeneity between decision-making units, and therefore cannot be directly applied to the horizontal comparison of excavation efficiency and resource optimization of multiple tunnel clusters. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a method for evaluating the efficiency of multi-tunnel excavation and optimizing resource allocation. This method solves the problems of existing data envelopment analysis-related research or indicator systems not matching tunnel construction scenarios, failing to effectively handle heterogeneity between decision-making units, and not being directly applicable to horizontal comparisons and resource optimization of excavation efficiency in multi-tunnel clusters.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for evaluating the efficiency of multi-tunnel excavation and optimizing resource allocation, comprising the following steps: S1. By classifying the information characteristics of the tunnel's basic information and construction information, a tunnel excavation input index system and a tunnel excavation output index system are constructed. Indicators are collected from the tunnel group to obtain the tunnel excavation input index and tunnel excavation output index. S2. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, perform data preprocessing and standardization on the input and output indicators of each tunnel excavation to obtain the processed input and output indicators. S3. Construct a decision-making unit from the excavation data of each tunnel at the same surrounding rock grade during the evaluation period, and establish a variable return on scale model based on standardized input-output indicators. S4. Based on the variable returns to scale model, a pre-defined advanced evaluation of the excavation efficiency of multiple tunnels is conducted to obtain the advanced evaluation results. S5. Based on the results of the advanced evaluation, slack variables are introduced to construct and solve a data envelopment analysis model for excavation efficiency. The projection points of the production front are obtained, and adjustments are made to optimize the allocation of construction resources, thus completing the advanced evaluation and resource allocation optimization.

[0007] The beneficial effects of this invention are as follows: This invention constructs a tunnel excavation input index system and a tunnel excavation output index system adapted to tunnel engineering, constructs a surrounding rock grade definition and splits decision-making units, establishes a scale return variable model, and conducts advanced evaluation of the excavation efficiency of multiple tunnels and optimization analysis of tunnel construction resource allocation. This solves the problem of incomparable excavation efficiency caused by objective differences such as geological conditions among different tunnels, and provides quantitative decision support for the optimal allocation of construction resources.

[0008] Further, S1 includes the following steps: S101. Classify the tunnel foundation information and construction information by information features to obtain information feature categories that include engineering feature parameters, construction methods, external environment, human resources and mechanical equipment. S102. By selecting non-adjustable input indicators that include tunnel cross-sectional dimensions, number of blast holes, excavation method, surrounding rock grade, and gas grade in the information feature category, and adjustable input indicators that include the number of drilling workers, number of scaffolding workers, personnel work efficiency, number of transport vehicles, and number of loaders, a tunnel excavation input indicator system is obtained. S103. Based on the actual situation of tunnel excavation, a tunnel excavation output index system is obtained by selecting excavation advance, total excavation time, drilling time, charging and blasting time, muck removal time, frame erection time, and shotcreting time. S104. Based on the tunnel excavation input index system and the tunnel excavation output index system, and using a data collection method that combines automated means with manual data entry, indexes are collected for each tunnel and each excavation cycle in the tunnel group to obtain the tunnel excavation input index and the tunnel excavation output index.

[0009] The beneficial effects of the above-mentioned further solutions are as follows: By deeply integrating the background of tunnel engineering, this invention constructs an input-output index system adapted to tunnel engineering, and divides the input indicators into non-adjustable input indicators and adjustable input indicators, so that the evaluation results can clearly distinguish the contribution of objective conditions and subjective management level, and provide a basis for subsequent resource optimization.

[0010] Furthermore, S2 includes the following steps: S201. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, the excavation cycle data of each tunnel under the same surrounding rock grade are collected to obtain the dataset to be evaluated under the corresponding evaluation period and the corresponding surrounding rock grade. S202. Select input indicators for tunnel excavation to obtain quantitative input indicators including excavation method and gas level, and qualitative input indicators including cross-sectional size, number of blast holes, number of drilling workers, number of erection workers, personnel work efficiency, number of transport vehicles and number of loaders. S203. Perform scale-weighted preprocessing on the quantitative input indicators in the dataset to be evaluated to obtain the preprocessed quantitative input indicators. S204. Quantify the proportion of each qualitative component in the qualitative indicators of inputs in the evaluation dataset in terms of the application progress of each component in the evaluation period to the total progress in the evaluation period, and obtain the preprocessed qualitative indicators of inputs. S205. Perform advance-weighted preprocessing on the tunnel excavation output indicators in the dataset to be evaluated to obtain the preprocessed output indicators. S206. Minimum-maximum standardization is applied to the pre-processed quantitative input indicators, pre-processed qualitative input indicators, and pre-processed output indicators. Among them, time-consuming output indicators, including total excavation time, drilling time, charging and blasting time, slag removal time, frame erection time, and shotcreting time, are subjected to negative minimum-maximum standardization and integrated to obtain the processed input-output indicators.

[0011] Furthermore, the calculation expression for the preprocessed quantitative input index is as follows: ; in, This indicates the quantitative indicators of pretreated inputs. This represents the total number of cycles within the range to be evaluated. Indicates the first within the scope to be evaluated r The current quantitative indicator data in each cycle, Indicates the first item within the scope to be evaluated. r One cycle of tunnel advance, Indicates the first j Total tunnel advance within the evaluation range.

[0012] Furthermore, the calculation expression for the preprocessed qualitative input indicators is as follows: ; in, This indicates the qualitative indicators of inputs after preprocessing. Indicates the types of qualitative components. Indicates qualitative components Application length within the range to be evaluated. Indicates the first j Total tunnel advance within the evaluation range.

[0013] Furthermore, the expression for the preprocessed output index is as follows: ; in, This indicates the output indicators after preprocessing. This represents the total number of cycles within the range to be evaluated. Indicates the first within the scope to be evaluated h The tunnel excavation output index in the first r The output value in each cycle, Indicates the first item within the scope to be evaluated. r One cycle of tunnel advance, Indicates the first j Total tunnel advance within the evaluation range.

[0014] The beneficial effects of the above-mentioned further solutions are as follows: This invention preprocesses the tunnel excavation input indicators by dividing them into quantitative and qualitative input indicators, and performs advance-weighted preprocessing on the tunnel excavation output indicators based on the proportion of tunnel advance in each excavation cycle within the evaluation range to the total advance within the evaluation range, thus obtaining the preprocessed output indicators. Furthermore, through minimum-maximum standardization, standardized input-output indicators are obtained. By performing advance-weighted preprocessing on multiple excavation cycle data, the discrete cycle data of the construction process is transformed into comparable and modelable data objects, enabling different tunnels to be compared in excavation efficiency under a unified framework, and realizing the horizontal comparison of excavation efficiency of multiple tunnels under heterogeneous conditions.

[0015] Furthermore, step S3 includes the following steps: S301. Construct a decision unit from the excavation data of each tunnel at the same surrounding rock grade within the evaluation period, and form a corresponding set of decision units within the same surrounding rock grade. S302. Based on the set of decision-making units within the same surrounding rock grade, construct the input vector and output vector of each decision-making unit according to the standardized input-output indicators. S303. Based on the set of decision-making units within the same surrounding rock grade, establish a variable return on scale model according to the input and output vectors of each decision-making unit.

[0016] Furthermore, the expression for the variable returns to scale model is as follows: ; in, Indicates the first The pure technical efficiency of each decision-making unit to be evaluated. Indicates the sequence number of the decision-making unit to be evaluated, and , Indicates the number of decision-making units. This indicates the sequence number of the decision-making unit involved in constructing the production frontier. Indicating the evaluation of the first When the first decision-making unit to be evaluated is... The linear combination coefficients corresponding to each decision unit This indicates the quantity of input indicators for tunnel excavation. Indicates the first The decision-making unit corresponding to the first Input index values ​​for tunnel excavation, Indicates the first The first decision-making unit to be evaluated corresponds to the first Input index values ​​for tunnel excavation, This indicates the quantity of output indicators from tunnel excavation. Indicates the first The decision-making unit corresponding to the first Various tunnel excavation output index values, Indicates the first The first decision-making unit to be evaluated corresponds to the first Output index values ​​for tunnel excavation.

[0017] The beneficial effects of the above-mentioned further solutions are as follows: By dividing the decision-making units according to the surrounding rock grade, the present invention divides the excavation data under different geological conditions into independent evaluation objects, eliminating the influence of heterogeneous factors on the decision-making units. Furthermore, by constructing a variable return-to-scale model for data envelopment analysis, it can adapt to the nonlinear and non-fixed ratio changes between the input resources and efficiency output during tunnel excavation. This enables a quantitative evaluation of the pure technical efficiency of each decision-making unit, improves the objectivity of judging the advancement of multi-tunnel excavation efficiency, and provides a quantitative basis for subsequent identification of inefficient decision-making units and optimization of construction resource allocation.

[0018] Furthermore, step S4 includes the following steps: S401. Based on the variable returns to scale model, evaluate the excavation efficiency of multiple tunnels based on pure technical efficiency, obtain the pure technical efficiency value, and determine whether the current pure technical efficiency value is one. If it is, determine that the pure technical efficiency is strongly effective and obtain the pure technical efficiency evaluation result. If not, then the pure technical efficiency is determined to be ineffective, and the pure technical efficiency evaluation result is obtained; S402. Based on the variable returns to scale model, the comprehensive efficiency value is calculated by removing the linear combination coefficient constraint, and it is determined whether the current comprehensive efficiency value is one. If it is, the data envelopment analysis is determined to be strongly effective, and the comprehensive efficiency evaluation result is obtained. If not, the data envelopment analysis is deemed invalid, and the overall efficiency evaluation result is obtained. S403. Based on the current pure technical efficiency value and the current comprehensive efficiency value, the scale efficiency evaluation result is obtained by calculating the scale efficiency. S404. The results of the pure technical efficiency evaluation, the comprehensive efficiency evaluation, and the scale efficiency evaluation are integrated to obtain the advanced evaluation results.

[0019] The beneficial effects of the above-mentioned further solutions are as follows: This invention comprehensively evaluates excavation efficiency by adopting three dimensions: overall efficiency, pure technical efficiency, and scale efficiency, which assists engineering managers in scientifically judging the current overall input-output effect, construction resource utilization capacity and construction organization level, and whether the construction scale is optimal.

[0020] Furthermore, step S5 includes the following steps: S501. Based on the results of the advanced evaluation, select decision units that are not effective in data envelopment analysis as the excavation process to be optimized. S502. Based on the input index values, output index values, and linear combination coefficients of each decision-making unit, an excavation efficiency data envelopment analysis model with slack variables is constructed by introducing input slack variables and output slack variables into the data envelopment analysis constraints; wherein, the input slack variables are used to characterize input redundancy, and the output slack variables are used to characterize output insufficiency. S503. The decision unit corresponding to the excavation process to be optimized is taken as the decision unit to be evaluated. By solving the data envelopment analysis model of excavation efficiency with slack variables, the optimal efficiency value, optimal input slack variable and optimal output slack variable corresponding to the excavation process to be optimized are obtained. S504. In response to the fact that the optimal input slack variable or the optimal output slack variable corresponding to the excavation process to be optimized is not all zero, the projection point of the production front surface of the excavation process to be optimized is calculated based on the optimal efficiency value, the optimal input slack variable and the optimal output slack variable corresponding to the excavation process to be optimized. S505. Based on the projection points of the production front surface, optimize and adjust the allocation of construction resources for the excavation process to be optimized, and complete the advanced evaluation and resource allocation optimization.

[0021] The beneficial effects of the above-mentioned further solutions are as follows: By introducing slack variables and projection analysis, this invention identifies redundant adjustable input indicators, provides specific optimization values, and offers quantitative guidance for resource allocation optimization schemes. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] Before describing this embodiment, the following terms will be explained: DEA: Data Envelopment Analysis; BCC model: Variable returns to scale model; DMU: Decision Unit; VRS: Variable returns to scale.

[0025] Example like Figure 1 As shown, this invention provides a method for evaluating the efficiency of multi-tunnel excavation and optimizing resource allocation, the implementation of which is as follows: S1. By classifying the information characteristics of tunnel foundation information and construction information, a tunnel excavation input index system and a tunnel excavation output index system are selected. Then, using a combination of automated methods and manual data entry, the indexes for the tunnel group are collected to obtain the tunnel excavation input index and tunnel excavation output index. The specific steps are as follows: S101. Classify the tunnel foundation information and construction information by information features to obtain information feature categories that include engineering feature parameters, construction methods, external environment, human resources and mechanical equipment. S102. By selecting non-adjustable input indicators that include tunnel cross-sectional dimensions, number of blast holes, excavation method, surrounding rock grade, and gas grade in the information feature category, and adjustable input indicators that include the number of drilling workers, number of scaffolding workers, personnel work efficiency, number of transport vehicles, and number of loaders, a tunnel excavation input indicator system is obtained. S103. Based on the actual situation of tunnel excavation, a tunnel excavation output index system is obtained by selecting excavation advance, total excavation time, drilling time, charging and blasting time, muck removal time, frame erection time, and shotcreting time.

[0026] In this embodiment, the steps for selecting and constructing the input and output indicator system for tunnel excavation are carried out. Based on the actual situation of tunnel excavation, the input indicator system and the output indicator system for tunnel excavation are constructed. Specifically: The tunnel excavation input index system categorizes tunnel basic information and construction information into categories such as engineering characteristic parameters, construction methods, external environment, human resources, and machinery and equipment based on information characteristics. Engineering characteristic parameters have a significant impact on the subsequent input of construction resources, often determining the amount of excavation work and resource requirements. These mainly include structural dimensions and construction methods, such as tunnel cross-sectional dimensions. and the number of gun holes wait; Different construction methods have varying requirements for construction resources, playing a significant role in the scale and flow of subsequent resource input. The choice of construction method is primarily based on the actual construction characteristics and construction capabilities, such as excavation methods. ; External environmental factors are closely related to the allocation of construction resources and affect the overall construction plan. These factors mainly include the external objective environment that is difficult to change during the excavation process, such as the surrounding rock grade. and gas level ; The input of human resources in tunnel construction exhibits significant dynamic changes at each stage. These fluctuations and adjustments are closely linked to the changes in construction procedures, significantly impacting tunnel excavation efficiency and forming a crucial part of construction strategy adjustments. This includes the number of drilling workers. Number of scaffolding erection workers and staff work efficiency ; The rational allocation of machinery and equipment can avoid overcapacity, reduce waste of construction resources, and improve tunnel excavation efficiency. This mainly includes the number of transport vehicles. and the number of loaders wait; Therefore, the selected tunnel excavation input indicators They are divided into two categories: non-adjustable input indicators and adjustable input indicators; Non-adjustable input indicators mainly include tunnel basic information and construction information such as engineering characteristic parameters, construction methods, and external environment, specifically including: tunnel cross-sectional dimensions. Number of blast holes Excavation method Surrounding rock grade and gas level ; Adjustable input indicators mainly include tunnel construction information such as the input of human resources and machinery and equipment, specifically including: the number of drilling workers. Number of scaffolding erection workers Staff work efficiency Number of transport vehicles and the number of loaders ; Among them, the tunnel cross-sectional dimensions Number of blast holes Number of drilling workers Number of scaffolding erection workers Staff work efficiency Number of transport vehicles and the number of loaders Quantitative indicators; excavation method Surrounding rock grade and gas level For qualitative indicators, excavation method Including full-section, two-step, and three-step rock formations, and surrounding rock grades. Gas classification includes Level I, Level II, Level III, Level IV, and Level V. Including both gas-free and gas-containing; Integrating tunnel excavation input indicators A system of indicators for tunnel excavation input was obtained; The tunnel excavation output index system, based on the tunnel excavation process, selects the excavation progress. Total excavation time Drilling time Time consumed by explosive blasting Slag removal time Erection time and shotcrete time Output indicators for tunnel excavation And integrated them to obtain a tunnel excavation output index system.

[0027] S104. Based on the tunnel excavation input index system and the tunnel excavation output index system, and using a data collection method that combines automated means with manual data entry, indexes are collected for each tunnel and each excavation cycle in the tunnel group to obtain the tunnel excavation input index and the tunnel excavation output index.

[0028] In this embodiment, the process of collecting tunnel basic information and construction information for each cycle is carried out using a combination of automated methods and manual data entry. Based on the tunnel excavation input index system and the tunnel excavation output index system, the system collects tunnel excavation input and output indices for each tunnel and each excavation cycle in the tunnel group, including engineering characteristic parameters, construction methods, external environment, human resources, and machinery and equipment. Specifically, this includes the tunnel cross-sectional dimensions corresponding to each tunnel and each excavation cycle. Number of blast holes Excavation method Surrounding rock grade Gas level Number of drilling workers Number of scaffolding erection workers Staff work efficiency Number of transport vehicles Number of loaders Excavation progress Total excavation time Drilling time Time consumed by explosive blasting Slag removal time Erection time and shotcrete time .

[0029] S2. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, perform data preprocessing and standardization on the input and output indicators of each tunnel excavation to obtain the processed input and output indicators. The specific steps are as follows: S201. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, the excavation cycle data of each tunnel under the same surrounding rock grade are collected to obtain the dataset to be evaluated under the corresponding evaluation period and the corresponding surrounding rock grade. S202. Select input indicators for tunnel excavation to obtain quantitative input indicators including excavation method and gas level, and qualitative input indicators including cross-sectional size, number of blast holes, number of drilling workers, number of erection workers, personnel work efficiency, number of transport vehicles and number of loaders. S203. Perform scale-weighted preprocessing on the quantitative input indicators in the dataset to be evaluated to obtain the preprocessed quantitative input indicators. S204. Based on the advance of each qualitative component in the input qualitative indicators in the dataset to be evaluated during the evaluation period, the applied advance of the input qualitative indicators is obtained. By quantifying the proportion of the applied advance of each input qualitative indicator in the total tunnel advance, the preprocessed input qualitative indicators are obtained. S205. Perform advance-weighted preprocessing on the tunnel excavation output indicators in the dataset to be evaluated to obtain the preprocessed output indicators. S206. Minimum-maximum standardization is applied to the pre-processed quantitative input indicators, pre-processed qualitative input indicators, and pre-processed output indicators. Among them, time-consuming output indicators, including total excavation time, drilling time, charging and blasting time, slag removal time, frame erection time, and shotcreting time, are subjected to negative minimum-maximum standardization and integrated to obtain the processed input-output indicators.

[0030] In this embodiment, a preprocessing step is performed on the input-output indicators of the excavation cycle within the tunnel evaluation area. The tunnel excavation input and output indicators for each tunnel excavation cycle are preprocessed sequentially using the evaluation period as the unit, followed by minimum-maximum standardization. For example, if the overall data includes ten months of data, the evaluation area is ten months. If the excavation efficiency is evaluated for each month, the evaluation period is one month. Specifically: Data preprocessing is performed on a time-period basis, and quantitative indicators for tunnel excavation inputs include: tunnel cross-sectional dimensions. Number of blast holes Number of drilling workers Number of scaffolding erection workers Staff work efficiency Number of transport vehicles and the number of loaders The average value of the characteristic parameter of the project within the evaluation range is used as the characterization, with the tunnel cross-sectional dimensions as the basis. For example, the expression for calculating the mean of engineering characteristic parameters within the evaluation range is as follows: ; in, Indicates the cross-sectional dimensions of the tunnel. This represents the total number of cycles within the range to be evaluated. Indicates the first item within the scope to be evaluated. r Cross-sectional dimensions in each cycle, Indicates the first item within the scope to be evaluated. r One cycle of tunnel advance, Indicates the first jTotal tunnel advance within the evaluation scope; Qualitative indicators for tunnel excavation input: Excavation method and gas level The qualitative components of each indicator are quantified by their respective proportions within the total tunnel advance within the evaluation range, using the excavation method... For example, its qualitative components include the entire cross-section. Two steps and three steps The calculation expressions for the qualitative components are as follows: ; in, Indicates the type of excavation method. These represent the full cross-section, two-step section, and three-step section, respectively. Indicates qualitative components , Indicates qualitative components Application length within the range to be evaluated. Indicates the first j Total tunnel advance within the evaluation scope; Due to the surrounding rock grade The impact on the excavation efficiency of different tunnels is particularly critical. Therefore, the excavation efficiency of different tunnels under different surrounding rock grades will be analyzed and evaluated in the subsequent steps. All tunnel excavation output indicators are quantitative indicators. For example, the tunnel excavation output indicator includes: excavation progress. Total excavation time Drilling time Time consumed by explosive blasting Slag removal time Erection time and shotcrete time The average level within the evaluation range is represented by a cumulative averaging method, and the calculation expression is as follows: ; in, This indicates the output indicators of tunnel excavation. This represents the total number of cycles within the range to be evaluated. Indicates the first item within the scope to be evaluated. h The tunnel excavation output index in the first r The output value in each cycle, Indicates the first item within the scope to be evaluated. r One cycle of tunnel advance, Indicates the first j Total tunnel advance within the evaluation scope; After monthly data preprocessing of tunnel excavation input and output indicators, a minimum-maximum standardization method is adopted to eliminate the influence of dimensions on each indicator, taking into account the general requirement that DEA indicators should be non-negative. The calculation expression is shown below: ; in, This represents a standardized index value. Indicators of tunnel input and output or tunnel excavation output indicators , Indicators The minimum value, Indicators The maximum value; The time-consuming output indicators are then subjected to negative minimax standardization, and the calculation expression is as follows: ; in, This refers to time-consuming output indicators, which include total excavation time, drilling time, charging and blasting time, muck removal time, frame erection time, and shotcreting time.

[0031] S3. Construct a decision-making unit from the excavation data of each tunnel within the same surrounding rock grade during the evaluation period, and establish a variable return on scale model based on standardized input-output indicators. The specific steps are as follows: S301. Construct a decision unit from the excavation data of each tunnel at the same surrounding rock grade within the evaluation period, and form a corresponding set of decision units within the same surrounding rock grade. S302. Based on the set of decision-making units within the same surrounding rock grade, construct the input vector and output vector of each decision-making unit according to the standardized input-output indicators. S303. Based on the set of decision-making units within the same surrounding rock grade, establish a variable return on scale model according to the input and output vectors of each decision-making unit.

[0032] In this embodiment, decision units and BCC models are established for the excavation cycles of each tunnel's evaluation area. Specifically, within the same surrounding rock grade within the unit evaluation area... The excavation data below is used as DMU, ​​and the excavation data corresponding to different geological conditions (different grades of surrounding rock) are split into independent DMU to eliminate heterogeneity interference; To address the nonlinear variations in construction efficiency output and the dynamic nature of construction resource allocation in tunnel engineering, and to reflect the relationship between various construction resource inputs and construction efficiency output during excavation from multiple perspectives, a BCC model under VRS conditions was selected for DEA evaluation analysis. The data envelopment analysis model for evaluating the efficiency advancement of multiple tunnel excavations possesses… n indivual each For the excavation process corresponding to a specific time period, each have p Input indicators for tunnel excavation and q The various tunnel excavation output indicators represent the... The efficiency of input resources and output, for the first k Each decision-making unit ( The linear programming form of the BCC model for efficiency evaluation is shown below: ; in, Indicates the first The pure technical efficiency of each decision-making unit to be evaluated. Indicates the sequence number of the decision-making unit to be evaluated, and , Indicates the number of decision-making units. This indicates the sequence number of the decision-making unit involved in constructing the production frontier. Indicating the evaluation of the first When the first decision-making unit to be evaluated is... The linear combination coefficients corresponding to each decision unit This indicates the quantity of input indicators for tunnel excavation. Indicates the first The decision-making unit corresponding to the first Input index values ​​for tunnel excavation, Indicates the first The first decision-making unit to be evaluated corresponds to the first Input index values ​​for tunnel excavation, This indicates the quantity of output indicators from tunnel excavation. Indicates the first The decision-making unit corresponding to the first Various tunnel excavation output index values, Indicates the first The first decision-making unit to be evaluated corresponds to the first Output index values ​​for tunnel excavation.

[0033] S4. Based on the variable returns to scale model, a pre-defined advanced evaluation of the excavation efficiency of multiple tunnels is conducted to obtain the advanced evaluation results. The specific steps are as follows: S401. Based on the variable returns to scale model, evaluate the excavation efficiency of multiple tunnels based on pure technical efficiency, obtain the pure technical efficiency value, and determine whether the current pure technical efficiency value is one. If it is, determine that the pure technical efficiency is strongly effective and obtain the pure technical efficiency evaluation result. If not, then the pure technical efficiency is determined to be ineffective, and the pure technical efficiency evaluation result is obtained.

[0034] In this embodiment, the efficiency advancement of multiple tunnel excavation is evaluated based on the tunnel excavation input index system, tunnel excavation output index system, decision-making unit, and BCC model. The evaluation includes evaluation based on pure technical efficiency, evaluation based on comprehensive efficiency, and evaluation based on scale efficiency. An evaluation based on pure technical efficiency is conducted, as follows: Pure technical efficiency is mainly used to measure the utilization of construction resources during this tunnel excavation process. It is solved using the BCC model to obtain the pure technical efficiency of the current tunnel excavation process. And make a judgment; when If the technical efficiency is deemed strong and effective, it indicates that the construction resources in the current excavation process can be fully utilized and the construction organization is reasonable. when If the pure technical efficiency is deemed ineffective, it indicates that there are technical inefficiencies in the tunnel excavation process for a given scale of investment. For example, there may be problems with the arrangement of the construction process and the level of construction automation, resulting in insufficient utilization of construction resources. This leads to a pure technical efficiency evaluation result based on pure technical efficiency.

[0035] S402. Based on the variable returns to scale model, the comprehensive efficiency value is calculated by removing the linear combination coefficient constraint, and it is determined whether the current comprehensive efficiency value is one. If it is, the data envelopment analysis is determined to be strongly effective, and the comprehensive efficiency evaluation result is obtained. If not, the data envelopment analysis is deemed invalid, and the overall efficiency evaluation result is obtained.

[0036] In this embodiment, an evaluation based on comprehensive efficiency is performed, as follows: For the first k Decision-making unit Overall efficiency value To remove the constraint that the sum of the coefficients of all linear combinations must be one The efficiency is calculated afterward, and the formula for calculating the overall efficiency value is as follows: ; in, Indicates the first k The overall efficiency value of the decision-making unit is calculated to obtain the th decision-making unit's efficiency value. kThe overall efficiency value of each decision-making unit And make the following judgments; when If the data envelopment analysis is strong, it indicates that the current excavation process has achieved good excavation efficiency with relatively small investment, and the excavation efficiency is at an advanced level among tunnel groups. when If the data envelopment analysis is ineffective, it indicates that there is room for optimization in adjusting the input; when comparing different excavation processes, The larger the value and the closer it is to 1, the closer the excavation process is to the production front face, and the more advanced it is compared with the excavation efficiency of other projects in the tunnel group, thus obtaining a comprehensive efficiency evaluation result based on comprehensive efficiency.

[0037] S403. Based on the current pure technical efficiency value and the current comprehensive efficiency value, the scale efficiency evaluation result is obtained by calculating the scale efficiency. S404. The results of the pure technical efficiency evaluation, the comprehensive efficiency evaluation, and the scale efficiency evaluation are integrated to obtain the advanced evaluation results.

[0038] In this embodiment, an evaluation based on scale efficiency is performed. Scale efficiency reflects the situation where output changes proportionally to input, and measures the efficiency difference caused by output scale factors. It considers whether the production scale of the decision-making unit is in an optimal state. The calculation expression is as follows: ; in, Indicates scale efficiency. This represents the overall efficiency value. Indicates pure technical efficiency, and ; In specific evaluations, scale efficiency is used to describe the relationship between changes in scale and changes in output during the tunnel excavation process. That is, when the input of construction resources increases by a certain proportion, if the increase in excavation efficiency is greater than the increase in input, then the construction process is in the stage of increasing returns to scale; if the increase in excavation efficiency is less than the increase in input, then the construction process is in the stage of decreasing returns to scale, and the scale efficiency evaluation result is obtained. By integrating the results of pure technical efficiency evaluation, comprehensive efficiency evaluation, and scale efficiency evaluation, the result of advancedness evaluation is obtained.

[0039] S5. Based on the results of the advanced evaluation, slack variables are introduced to construct and solve a data envelopment analysis model for excavation efficiency. The projection points of the production front are obtained, and adjustments are made to optimize the allocation of construction resources. The advanced evaluation and resource allocation optimization are completed. The specific steps are as follows: S501. Based on the results of the advanced evaluation, select decision units that are not effective in data envelopment analysis as the excavation process to be optimized. S502. Based on the input index values, output index values, and linear combination coefficients of each decision-making unit, an excavation efficiency data envelopment analysis model with slack variables is constructed by introducing input slack variables and output slack variables into the data envelopment analysis constraints; wherein, the input slack variables are used to characterize input redundancy, and the output slack variables are used to characterize output insufficiency. S503. The decision unit corresponding to the excavation process to be optimized is taken as the decision unit to be evaluated. By solving the data envelopment analysis model of excavation efficiency with slack variables, the optimal efficiency value, optimal input slack variable and optimal output slack variable corresponding to the excavation process to be optimized are obtained. S504. In response to the fact that the optimal input slack variable or the optimal output slack variable corresponding to the excavation process to be optimized is not all zero, the projection point of the production front surface of the excavation process to be optimized is calculated based on the optimal efficiency value, the optimal input slack variable and the optimal output slack variable corresponding to the excavation process to be optimized. S505. Based on the projection points of the production front surface, optimize and adjust the allocation of construction resources for the excavation process to be optimized, and complete the advanced evaluation and resource allocation optimization.

[0040] In this embodiment, the tunnel construction resource allocation optimization analysis step involves introducing slack variables after evaluating the advancement using a data envelopment analysis model. slack variables This reflects the gap between the excavation process and the production frontier at the current input-output level, where negative slack variables are input. , representing the amount of redundant input, i.e., the amount of construction input that can be reduced without decreasing excavation efficiency; output positive slack variables. This represents the insufficient output, that is, the excavation efficiency that could be improved without increasing construction input. Therefore, for the first... k Decision-making unit A data envelopment analysis model for excavation efficiency is constructed, expressed as follows: ; in, Denotes non-Archimedean infinitesimals. Indicating the evaluation of the first When the first decision-making unit to be evaluated is... Negative relaxation variables corresponding to various tunnel excavation input indicators. Indicating the evaluation of the first When the first decision-making unit to be evaluated is... Positive relaxation variables corresponding to various tunnel excavation output indicators; By solving the DEA analysis model of excavation efficiency, the pure technical efficiency value of each excavation process and the slack variables of each influencing factor are obtained; When the slack variables of all influencing factors are 0, it indicates that the excavation process is on the production front and no further optimization is needed. When the slack variable of any factor is not zero, it indicates that the adjustable input is at a certain distance from the production front. The position of its projection point on the production front can be determined based on the value of the slack variable. The technical expression is as follows: ; in, Indicates the first The first decision-making unit to be evaluated on the production frontier surface Projected values ​​of various input indicators; Indicates the first The first decision-making unit to be evaluated on the production frontier surface Projected values ​​of output indicators; , , These represent the optimal pure technical efficiency value, the optimal negative slack variable, and the optimal positive slack variable obtained from the above DEA analysis model, respectively. Using projection points Provide specific directions and goals for improvement in the excavation process, and managers can optimize the input of adjustable construction resources accordingly, so that the excavation process moves closer to the production frontier until DEA is effective.

Claims

1. A method for evaluating the efficiency advancement and optimizing resource allocation in multi-tunnel excavation, characterized in that, Includes the following steps: S1. By classifying the information characteristics of the tunnel's basic information and construction information, a tunnel excavation input index system and a tunnel excavation output index system are constructed. Indicators are collected from the tunnel group to obtain the tunnel excavation input index and tunnel excavation output index. S2. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, perform data preprocessing and standardization on the input and output indicators of each tunnel excavation to obtain the processed input and output indicators. S3. Construct a decision-making unit from the excavation data of each tunnel at the same surrounding rock grade during the evaluation period, and establish a variable return on scale model based on standardized input-output indicators. S4. Based on the variable returns to scale model, a pre-defined advanced evaluation of the excavation efficiency of multiple tunnels is conducted to obtain the advanced evaluation results. S5. Based on the results of the advanced evaluation, slack variables are introduced to construct and solve a data envelopment analysis model for excavation efficiency. The projection points of the production front are obtained, and adjustments are made to optimize the allocation of construction resources, thus completing the advanced evaluation and resource allocation optimization.

2. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 1, characterized in that, S1 includes the following steps: S101. Classify the tunnel foundation information and construction information by information features to obtain information feature categories that include engineering feature parameters, construction methods, external environment, human resources and mechanical equipment. S102. By selecting non-adjustable input indicators that include tunnel cross-sectional dimensions, number of blast holes, excavation method, surrounding rock grade, and gas grade in the information feature category, and adjustable input indicators that include the number of drilling workers, number of scaffolding workers, personnel work efficiency, number of transport vehicles, and number of loaders, a tunnel excavation input indicator system is obtained. S103. Based on the actual situation of tunnel excavation, a tunnel excavation output index system is obtained by selecting excavation advance, total excavation time, drilling time, charging and blasting time, muck removal time, frame erection time, and shotcreting time. S104. Based on the tunnel excavation input index system and the tunnel excavation output index system, and using a data collection method that combines automated means with manual data entry, indexes are collected for each tunnel and each excavation cycle in the tunnel group to obtain the tunnel excavation input index and the tunnel excavation output index.

3. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 1, characterized in that, S2 includes the following steps: S201. Using the evaluation period as the unit and the surrounding rock grade as the grouping condition, the excavation cycle data of each tunnel under the same surrounding rock grade are collected to obtain the dataset to be evaluated under the corresponding evaluation period and the corresponding surrounding rock grade. S202. Select input indicators for tunnel excavation to obtain quantitative input indicators including excavation method and gas level, and qualitative input indicators including cross-sectional size, number of blast holes, number of drilling workers, number of erection workers, personnel work efficiency, number of transport vehicles and number of loaders. S203. Perform scale-weighted preprocessing on the quantitative input indicators in the dataset to be evaluated to obtain the preprocessed quantitative input indicators. S204. Based on the advance of each qualitative component in the input qualitative indicators in the dataset to be evaluated during the evaluation period, the applied advance of the input qualitative indicators is obtained. By quantifying the proportion of the applied advance of each input qualitative indicator in the total tunnel advance, the preprocessed input qualitative indicators are obtained. S205. Perform advance-weighted preprocessing on the tunnel excavation output indicators in the dataset to be evaluated to obtain the preprocessed output indicators. S206. Minimum-maximum standardization is applied to the pre-processed quantitative input indicators, pre-processed qualitative input indicators, and pre-processed output indicators. Among them, time-consuming output indicators, including total excavation time, drilling time, charging and blasting time, slag removal time, frame erection time, and shotcreting time, are subjected to negative minimum-maximum standardization and integrated to obtain the processed input-output indicators.

4. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 3, characterized in that, The calculation formula for the pretreated quantitative input index is as follows: in, This indicates the quantitative indicators of pretreated inputs. This represents the total number of cycles within the range to be evaluated. Indicates the first within the scope to be evaluated r The current quantitative indicator data in each cycle, Indicates the first within the scope to be evaluated r One cycle of tunnel advance, Indicates the first j Total tunnel advance within the evaluation range.

5. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 4, characterized in that, The calculation formula for the preprocessed qualitative indicators of the inputs is as follows: in, This indicates the qualitative indicators of inputs after preprocessing. Indicates the types of qualitative components. Indicates qualitative components Application length within the range to be evaluated. Indicates the first j Total tunnel advance within the evaluation range.

6. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 5, characterized in that, The expression for the preprocessed output index is as follows: in, This indicates the output indicators after preprocessing. This represents the total number of cycles within the range to be evaluated. Indicates the first within the scope to be evaluated h The tunnel excavation output index in the first r The output value in each cycle, Indicates the first within the scope to be evaluated r One cycle of tunnel advance, Indicates the first j Total tunnel advance within the evaluation range.

7. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 1, characterized in that, S3 includes the following steps: S301. Construct a decision unit from the excavation data of each tunnel at the same surrounding rock grade within the evaluation period, and form a corresponding set of decision units within the same surrounding rock grade. S302. Based on the set of decision-making units within the same surrounding rock grade, construct the input vector and output vector of each decision-making unit according to the standardized input-output indicators. S303. Based on the set of decision-making units within the same surrounding rock grade, establish a variable return on scale model according to the input and output vectors of each decision-making unit.

8. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 7, characterized in that, The expression for the variable returns to scale model is as follows: in, Indicates the first The pure technical efficiency of each decision-making unit to be evaluated. Indicates the sequence number of the decision-making unit to be evaluated, and , Indicates the number of decision-making units. This indicates the sequence number of the decision-making unit involved in constructing the production frontier. Indicating the evaluation of the first When the first decision-making unit to be evaluated is... The linear combination coefficients corresponding to each decision unit This indicates the quantity of input indicators for tunnel excavation. Indicates the first The decision-making unit corresponding to the first Input index values ​​for tunnel excavation, Indicates the first The first decision-making unit to be evaluated corresponds to the first Input index values ​​for tunnel excavation This indicates the quantity of output indicators from tunnel excavation. Indicates the first The decision-making unit corresponding to the first Various tunnel excavation output index values, Indicates the first The first decision-making unit to be evaluated corresponds to the first Output index values ​​for tunnel excavation.

9. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 1, characterized in that, S4 includes the following steps: S401. Based on the variable returns to scale model, evaluate the excavation efficiency of multiple tunnels based on pure technical efficiency, obtain the pure technical efficiency value, and determine whether the current pure technical efficiency value is one. If it is, determine that the pure technical efficiency is strongly effective and obtain the pure technical efficiency evaluation result. If not, then the pure technical efficiency is determined to be ineffective, and the pure technical efficiency evaluation result is obtained; S402. Based on the variable returns to scale model, the comprehensive efficiency value is calculated by removing the linear combination coefficient constraint, and it is determined whether the current comprehensive efficiency value is one. If it is, the data envelopment analysis is determined to be strongly effective, and the comprehensive efficiency evaluation result is obtained. If not, the data envelopment analysis is deemed invalid, and the overall efficiency evaluation result is obtained. S403. Based on the current pure technical efficiency value and the current comprehensive efficiency value, the scale efficiency evaluation result is obtained by calculating the scale efficiency. S404. The results of the pure technical efficiency evaluation, the comprehensive efficiency evaluation, and the scale efficiency evaluation are integrated to obtain the advanced evaluation results.

10. The method for evaluating the efficiency advancement and optimizing resource allocation of multi-tunnel excavation according to claim 1, characterized in that, S5 includes the following steps: S501. Based on the results of the advanced evaluation, select decision units that are not effective in data envelopment analysis as the excavation process to be optimized. S502. Based on the input index values, output index values, and linear combination coefficients of each decision-making unit, an excavation efficiency data envelopment analysis model with slack variables is constructed by introducing input slack variables and output slack variables into the data envelopment analysis constraints; wherein, the input slack variables are used to characterize input redundancy, and the output slack variables are used to characterize output insufficiency. S503. The decision unit corresponding to the excavation process to be optimized is taken as the decision unit to be evaluated. By solving the data envelopment analysis model of excavation efficiency with slack variables, the optimal efficiency value, optimal input slack variable and optimal output slack variable corresponding to the excavation process to be optimized are obtained. S504. In response to the fact that the optimal input slack variable or the optimal output slack variable corresponding to the excavation process to be optimized is not all zero, the projection point of the production front surface of the excavation process to be optimized is calculated based on the optimal efficiency value, the optimal input slack variable and the optimal output slack variable corresponding to the excavation process to be optimized. S505. Based on the projection points of the production front surface, optimize and adjust the allocation of construction resources for the excavation process to be optimized, and complete the advanced evaluation and resource allocation optimization.

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