A BIM-based optimization design method and system for foundation pit support

Abstract

The application provides an optimization design method and system for foundation pit support based on BIM, and belongs to the field of foundation pit support design. The method comprises the following steps: constructing a BIM foundation pit support model, obtaining a plurality of BIM free structure units and surface attribute parameters of each BIM free structure unit; collecting pollution monitoring data samples, analyzing support resistance indexes, and obtaining support resistance index calculation results; obtaining an identification optimization design strategy for the maximum support resistance index of each BIM free structure unit, generating a plurality of identification optimization design strategies, and optimizing the BIM foundation pit support model. The application solves the technical problem that the existing BIM-based foundation pit support design lacks targeted optimization adjustment after model construction, resulting in insufficient adaptability of the support structure, and achieves the technical effect of optimizing and adjusting the BIM foundation pit support model through support resistance index analysis, and improving the adaptability and reliability of the foundation pit support structure.

Description

Technical Field

[0001] This invention relates to the field of foundation pit support design, and in particular to an optimization design method and system for foundation pit support based on BIM. Background Technology

[0002] Foundation pit support engineering is a crucial part of building construction, and its design quality directly affects construction safety and project quality. With the development of Building Information Modeling (BIM) technology, BIM-based foundation pit support design methods are increasingly being applied in practical engineering projects. BIM technology enables three-dimensional visual modeling of foundation pit support structures, providing designers with an intuitive structural display and improving design efficiency.

[0003] However, existing BIM-based foundation pit support design methods primarily focus on model construction and visualization. After the BIM model is built, it is typically applied directly to construction guidance, lacking in-depth analysis and targeted optimization. This neglects the actual construction environment of the foundation pit support structure and fails to dynamically adjust and optimize the support scheme according to specific engineering conditions. Therefore, the lack of targeted optimization and adjustment after model construction in BIM-based foundation pit support design results in insufficient adaptability of the support structure to the actual construction environment, affecting the safety and reliability of foundation pit support projects. Summary of the Invention

[0004] This invention addresses the technical problem in existing BIM-based foundation pit support designs that lack targeted optimization and adjustments after model construction, resulting in insufficient adaptability of the support structure. It provides a BIM-based optimization design method and system for foundation pit support to solve this problem.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] In a first aspect, the present invention provides a BIM-based optimization design method for foundation pit support, comprising: constructing a BIM foundation pit support model; decomposing the BIM foundation pit support model into support structures to obtain multiple BIM free structural units and surface attribute parameters of each BIM free structural unit; constructing a candidate optimization design strategy library corresponding to each of the multiple BIM free structural units; collecting pollution monitoring data samples of the construction project during its construction cycle; performing support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples; obtaining support resistance index calculation results; obtaining, according to the support resistance index calculation results, identifying optimization design strategies that maximize the support resistance index of each BIM free structural unit from the candidate optimization design strategy library; generating multiple identifying optimization design strategies; and optimizing the BIM foundation pit support model according to the multiple identifying optimization design strategies.

[0007] Secondly, this invention provides a BIM-based optimization design system for foundation pit support, comprising: a model construction and decomposition module for constructing a BIM foundation pit support model, decomposing the BIM foundation pit support model into support structures, obtaining multiple BIM free structural units and surface attribute parameters of each BIM free structural unit; a strategy library construction module for constructing a candidate optimization design strategy library corresponding to each of the multiple BIM free structural units; a resistance index analysis module for collecting pollution monitoring data samples of the construction project during the construction period, performing support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples, and obtaining the support resistance index calculation results; and an optimization strategy execution module for obtaining, according to the support resistance index calculation results, identifying optimization design strategies that maximize the support resistance index of each BIM free structural unit from the candidate optimization design strategy library, generating multiple identified optimization design strategies, and optimizing the BIM foundation pit support model according to the multiple identified optimization design strategies.

[0008] The beneficial effects of this invention are:

[0009] First, a BIM foundation pit support model is constructed, and the support structure of the BIM foundation pit support model is decomposed to obtain multiple BIM free structural units and the surface attribute parameters of each BIM free structural unit. By decomposing the overall support structure into multiple independent structural units, the foundation is laid for subsequent differentiated analysis and optimization for each unit. At the same time, the obtained surface attribute parameters can characterize the physical properties of each structural unit. Then, a candidate optimization design strategy library is constructed for each BIM free structural unit among multiple BIM free structural units. Multiple optional optimization schemes are preset for each structural unit, allowing the selection of the most suitable optimization strategy from the strategy library based on actual analysis results. Next, pollution monitoring data samples are collected during the construction project's construction cycle. Based on these pollution monitoring data samples, the surface attribute parameters of each BIM free structural unit are analyzed for support resistance indices. The calculation results of the support resistance indices are obtained. By introducing actual pollution data monitored at the construction site and combining it with the surface attribute parameters of each structural unit for quantitative analysis, the resistance capacity of each structural unit in the actual construction environment can be accurately assessed, and weak structural units can be identified. Then, according to the support resistance index calculation results, the identification optimization design strategy that maximizes the support resistance index of each BIM free structural unit is obtained from the candidate optimization design strategy library, generating multiple identification optimization design strategies. The BIM foundation pit support model is optimized according to these strategies. Based on the quantified resistance index results, the optimal design strategy is matched for each structural unit, achieving differentiated optimization adjustments for different structural unit characteristics, thereby improving the adaptability and reliability of the overall foundation pit support structure in the construction environment.

[0010] The above technical solutions enable quantitative evaluation and targeted optimization of support structures based on real-world construction site environmental data. This addresses the issue of directly applying BIM models after their construction in existing technologies, allowing for dynamic adjustments to foundation pit support designs based on actual construction conditions. This effectively improves the adaptability of the support structure to the construction environment and the overall safety and reliability of the project. Attached Figure Description

[0011] Figure 1 A flowchart illustrating an optimized design method for foundation pit support based on BIM provided by this invention;

[0012] Figure 2 This is a structural schematic diagram of an optimized design system for foundation pit support based on BIM, provided by the present invention.

[0013] In the attached diagram, the components represented by each number are as follows:

[0014] The model building module 11, the strategy library building module 12, the resistance index analysis module 13, and the strategy execution optimization module 14 are all included. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0018] Example 1, as Figure 1 As shown, this embodiment of the invention provides an optimized design method for foundation pit support based on BIM, including:

[0019] S1. Construct a BIM foundation pit support model, decompose the support structure of the BIM foundation pit support model, and obtain multiple BIM free structural units and the surface attribute parameters of each BIM free structural unit.

[0020] Specifically, firstly, a BIM model of the foundation pit support is constructed based on the actual design scheme of the foundation pit support project. This BIM model includes the three-dimensional geometric information, material property information, and connection relationship information between components of the foundation pit support structure. Specifically, the BIM model covers complete information such as the spatial location, dimensional parameters, and material type of various support components, including support piles, anchors, lintels, support beams, and retaining walls.

[0021] The foundation pit support structure is continuously exposed to the construction environment throughout the construction period. Excavating machinery, concrete mixing equipment, welding equipment, and other equipment used during construction generate large amounts of exhaust gases and dust, among other pollutants. These pollutants include acidic gases such as nitrogen oxides and sulfides from diesel engine combustion, metal oxide particles from welding, and dust particles raised during earthwork excavation and material transportation. These pollutants deposit on the surface of the support components and affect their material properties through chemical reactions, physical adsorption, and pore penetration. For example, acidic gases corrode steel surfaces and concrete protective layers, while dust particles clog coating micropores and carry corrosive ions. Over time, this leads to a degradation of the load-bearing capacity of the support components and a shortened service life. Therefore, a detailed structural decomposition of the BIM foundation pit support model is necessary to assess the degree of pollution impact on components with different environmental exposure conditions and surface characteristics, thereby implementing differentiated optimization design strategies. When decomposing the support structure of the BIM foundation pit support model, the connection points are first decomposed to obtain an initial set of structural units. Connection points include physical connections between components such as welding points, bolted connections, anchor points, and gusset plate connections. By virtually cutting at these connection points, the overall BIM foundation pit support model is decomposed into independent initial structural units. For example, a complete support pile can be considered one initial structural unit, and a section of a joist beam can be considered another. Next, the static mechanical performance characteristic labels, dynamic mechanical performance characteristic labels, and surface material characteristic labels of each initial structural unit in the initial structural unit set are identified. The static mechanical performance characteristic labels reflect the static load-bearing role of the component in the overall structure. For example, "high axial compression component" identifies support piles that primarily bear axial pressure, "bending control component" identifies joists that primarily bear bending moments, and "tension control component" identifies anchors that primarily bear tensile forces. The dynamic mechanical performance characteristic labels reflect the dynamic mechanical role of movable components. For example, "earth pressure response component" identifies retaining plates that adjust displacement with changes in earth pressure, and "support adjustment component" identifies support beams that can be prestressed. Surface material feature labels identify the material type of the component surface, such as "concrete surface", "exposed steel surface", "epoxy coated surface", etc.

[0022] Then, the initial structural unit set is clustered and merged based on the identified static mechanical performance characteristic labels, dynamic mechanical performance characteristic labels, and surface material characteristic labels. The clustering principle is to merge initial structural units with the same combination of characteristic labels into a single BIM free structural unit. For example, all initial structural units labeled "high axial compression member + earth pressure response member + concrete surface" are merged into one BIM free structural unit; all initial structural units labeled "bending control member + bracing adjustment member + exposed steel surface" are merged into another BIM free structural unit; and all initial structural units labeled "tension control member + prestressed retaining member + hot-dip galvanized surface" are merged into yet another BIM free structural unit. Through this clustering and merging, multiple BIM free structural units with similar functional characteristics and surface material properties are obtained. After the structural decomposition is completed, the surface attribute parameters of each BIM free structural unit are automatically extracted. These surface attribute parameters mainly include surface material type, surface protective coating thickness, and surface porosity characteristics. Surface material type parameters identify the material composition of the surface layer, such as C30 concrete, Q345 steel, epoxy resin coating, etc. Different materials have significantly different resistance to pollutants. Surface protective coating thickness parameters are for components with anti-corrosion coatings, recording the actual coating thickness value. Coating thickness directly affects the barrier effect against pollutant erosion. Surface porosity parameters are specifically for porous materials such as concrete, characterizing the distribution density of open pores on the surface. Higher porosity allows pollutants such as exhaust gases and dust generated during construction to more easily penetrate into the material, accelerating the material degradation process. These surface attribute parameters are automatically extracted from the BIM model's material library and component attribute data, providing a foundation for subsequent support resistance index analysis.

[0023] S2. Construct a candidate optimization design strategy library for each BIM free structural unit among the multiple BIM free structural units.

[0024] Specifically, for each of the multiple obtained BIM free structural elements, a corresponding candidate optimization design strategy library is constructed. In other words, each BIM free structural element has its own dedicated candidate optimization design strategy library, which is a collection of surface protection optimization schemes for that specific BIM free structural element, used to provide optional design adjustment schemes during subsequent optimization processes.

[0025] Each candidate optimization design strategy library contains multiple candidate strategies, and each candidate strategy corresponds to a set of surface treatment parameter configuration schemes. Surface treatment parameters include, but are not limited to, surface protective coating type, coating thickness, surface sealing treatment method, and type and ratio of anti-corrosion additives. For example, for a BIM free structural unit labeled as "high axial compression member + earth pressure response member + concrete surface", its corresponding candidate optimization design strategy library may include different surface protection schemes such as "epoxy resin coating + 150µm thickness", "polyurethane coating + 2000µm thickness", and "silane impregnation treatment + penetration depth 5mm". For another BIM free structural unit labeled as "bending control member + support adjustment member + exposed steel surface", its corresponding candidate optimization design strategy library may include schemes such as "hot-dip galvanizing treatment + zinc layer thickness 850µm", "zinc-rich primer + epoxy intermediate paint + polyurethane topcoat three-layer system", and "sprayed aluminum composite coating".

[0026] When constructing the candidate optimization design strategy library for each BIM free structural unit, the appropriate surface treatment technology category is selected based on the surface material feature tags of that BIM free structural unit. Simultaneously, the static and dynamic mechanical performance feature tags of the BIM free structural unit are considered to exclude treatment schemes that may affect the structural mechanical performance. For example, for high axial compression members, thick coating schemes that add excessive weight are avoided; for dynamic members subject to frequent stress changes, coating materials with good flexibility and resistance to cracking are prioritized. Each candidate strategy is stored as a record in its respective candidate optimization design strategy library, containing specific values ​​of surface treatment parameters, expected protective performance indicators, construction process requirements, and cost estimates. By constructing a dedicated candidate optimization design strategy library for each BIM free structural unit, a differentiated selection space is provided for subsequent targeted optimization based on the support resistance index analysis results.

[0027] S3. Collect pollution monitoring data samples of the construction project during the construction period, and perform support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples to obtain the support resistance index calculation results.

[0028] Specifically, pollution monitoring data samples are collected by reading historical pollution monitoring data from a group of analogous construction projects related to the target construction project. The analogous construction project group refers to a set of historical construction projects that are highly similar to the target project in terms of construction type, equipment, and environment, with similarity exceeding a preset threshold. For example, if the target construction project is a subway station foundation pit project, then historical projects that are also subway station foundation pits, use similar excavation equipment and support techniques, and are located in similar urban environments are selected as analogous projects. Pollution monitoring data samples include pollutant concentration data monitored at different time points and spatial locations during the construction period, such as atmospheric nitrogen oxide concentration, sulfide concentration, PM2.5 and PM10 concentrations, and time-series variation data of environmental pollution parameters such as soil pH and heavy metal ion concentration.

[0029] After acquiring pollution monitoring data samples, support resistance index analysis was performed on the surface attribute parameters of each BIM free structural unit. Specifically, firstly, multiple environmental exposure attributes corresponding to multiple BIM free structural units were identified. Environmental exposure attributes refer to the environmental area type in which the BIM free structural unit is located within the foundation pit support structure, including the above-ground atmosphere zone, soil contact zone, and water level fluctuation zone. For example, the lintel beams and support beams located above ground belong to the above-ground atmosphere zone, the support piles deeply buried in the soil belong to the soil contact zone, and the components within the range of groundwater level fluctuation belong to the water level fluctuation zone. By analyzing the spatial coordinates of each BIM free structural unit in the BIM model and the geological and hydrological conditions of the foundation pit, its corresponding environmental exposure attributes were determined.

[0030] Secondly, the pollution monitoring data samples were segmented to obtain multiple subsets of dynamic pollution prediction time-series data corresponding to various environmental exposure attributes. Specifically, based on the spatial location information of the pollution monitoring data, the overall pollution monitoring data samples were grouped according to the type of environmental exposure attribute. For example, pollution monitoring data from the atmospheric region was grouped into the dynamic pollution prediction time-series data subset for the surface atmospheric region, pollution monitoring data from soil sampling points was grouped into the dynamic pollution prediction time-series data subset for the soil contact zone, and groundwater monitoring data was grouped into the dynamic pollution prediction time-series data subset for the water level fluctuation zone. Each subset of dynamic pollution prediction time-series data contains the dynamic change curve of pollutant concentration in that environmental area over time, reflecting the pollutant accumulation pattern under that environmental exposure condition.

[0031] Then, support defect simulations are performed based on multiple subsets of dynamic pollution prediction time-series data and the surface attribute parameters of each BIM free structural unit, resulting in multiple subsets of support defect simulation data. During the support defect simulation, the BIM foundation pit support model includes multiple simulation sub-models, including a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a load-bearing capacity simulation sub-model. Using these simulation sub-models, support defect simulations are performed based on multiple subsets of dynamic pollution prediction time-series data and the surface attribute parameters of each BIM free structural unit. The obtained surface pollution quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical load-bearing capacity degradation simulation data are output as a subset of support defect simulation data. Specifically, the pollution deposition simulation sub-model calculates the deposition rate and accumulation of pollutants on the component surface based on the pollutant concentration time-series curves in the dynamic pollution prediction time-series data subset, combined with parameters such as surface porosity and surface roughness in the surface attribute parameters of the BIM free structural unit, and outputs surface pollution quantification simulation data. The material degradation simulation sub-model, based on quantitative simulation data of surface contamination and combined with parameters such as surface material type and surface protective coating thickness, simulates the chemical corrosion process of contaminants on surface materials, calculates degradation indices such as coating thickness loss rate, concrete carbonation depth, and steel corrosion depth, and outputs simulation data of surface coating corrosion defects. The load-bearing capacity simulation sub-model, based on the surface coating corrosion defect simulation data and combined with the mechanical performance characteristics of BIM free structural elements, calculates the degree of impact of material degradation on the load-bearing capacity of components, and outputs simulation data of structural mechanical load-bearing capacity degradation, such as the percentage decrease in load-bearing capacity and the reduction in safety factor.

[0032] Subsequently, by analyzing subsets of multiple sets of simulated support defect data, the support resistance index of each BIM free structural unit was obtained. This analysis process will be described in detail in subsequent steps. Finally, the calculated support resistance index results for each BIM free structural unit are obtained. The calculated support resistance index results characterize the BIM free structural unit's ability to resist degradation and maintain function under the predicted polluted environment.

[0033] By utilizing historical pollution monitoring data from analogous construction project clusters, the time cost of waiting for data collection after the actual construction of the target project is avoided, enabling predictive assessment of pollution impacts during the design phase. By grouping pollution monitoring data according to environmental exposure attributes and combining it with surface attribute parameters for multi-level simulation, the differentiated degradation behavior of structural units with different locations and surface characteristics under polluted environments is accurately captured, providing a basis for subsequent targeted optimization design.

[0034] S4. Based on the calculation results of the support resistance index, obtain the identification optimization design strategy for maximizing the support resistance index of each BIM free structural unit from the candidate optimization design strategy library, generate multiple identification optimization design strategies, and optimize the BIM foundation pit support model according to the multiple identification optimization design strategies.

[0035] Specifically, firstly, multiple BIM free structural elements are traversed, and those with a resistance index below a preset threshold are extracted and designated as BIM free structural elements. The preset resistance index threshold is a minimum resistance level pre-set based on the design service life and safety factor requirements of the foundation pit support structure. For example, if a BIM free structural element has a resistance index of 0.65 calculated in step S3, while the preset resistance index threshold is 0.80, then this BIM free structural element is identified as a BIM free structural element requiring optimization. Through this screening, structural elements with insufficient support resistance are identified and used as optimization targets.

[0036] Secondly, with the preset support resistance index threshold as the target, the surface treatment parameters of the identified BIM free structural units are optimized from the candidate optimization design strategy library to obtain the identification optimization design strategy corresponding to each identified BIM free structural unit. Specifically, for each identified BIM free structural unit, each candidate strategy is traversed from its corresponding candidate optimization design strategy library. The surface treatment parameters of each candidate strategy are substituted into the support defect simulation process in step S3, and the support resistance index under that strategy is recalculated. The strategy that enables the support resistance index to reach or exceed the preset support resistance index threshold is selected as the identification optimization design strategy for that identified BIM free structural unit, thus achieving the optimization of surface treatment parameters. If multiple strategies can meet the requirements of the preset support resistance index threshold, the constraints such as construction cost and construction period are further considered, and the strategy with the best overall cost-effectiveness is selected as the identification optimization design strategy. In the optimization process, the key BIM free structural units in the identified BIM free structural units are first obtained according to the load-bearing location. The key BIM free structural units refer to structural units in the support structure system that bear the main load-bearing function and whose failure would have serious consequences, such as main support piles subjected to high axial compression and girders at critical stress nodes. Priority is given to determining identification optimization design strategies for key BIM free structural units to ensure the safety of the structural system. By completing the above optimization process for all identified BIM free structural units, multiple identification optimization design strategies are generated. Each identification optimization design strategy includes the optimized surface treatment parameter configuration for the corresponding BIM free structural unit, such as using epoxy resin coating, a coating thickness of 200μm, and surface pretreatment using sandblasting for rust removal, among other specific technical solutions.

[0037] Subsequently, the BIM foundation pit support model was optimized according to multiple identification optimization design strategies. Specifically, the surface attribute parameters of the corresponding BIM free structural units in the BIM foundation pit support model were updated, and the original surface protection scheme was replaced with the scheme specified in the identification optimization design strategy. For example, the "uncoated protection" of a certain support pile in the original design was updated to "epoxy resin coating + 200μm thickness", and the "ordinary anti-rust paint" of a certain lintel beam was updated to a "three-layer system of zinc-rich primer + epoxy intermediate paint + polyurethane topcoat". In the optimized BIM foundation pit support model, the support resistance index of all BIM free structural units reached or exceeded the corresponding preset support resistance index threshold, forming a foundation pit support design scheme with stronger durability and reliability in construction pollution environments.

[0038] Through the above technical solution, a BIM foundation pit support model is constructed and its structure is decomposed. Combined with construction pollution monitoring data, the support resistance index of each structural unit is quantitatively analyzed. The optimization scheme that maximizes the support resistance index is accurately selected from the candidate optimization design strategy library. This solves the technical problem in the existing technology that the BIM-based foundation pit support design lacks targeted optimization and adjustment after model construction, resulting in insufficient adaptability of the support structure in the construction pollution environment. It achieves the technical effect of optimizing and adjusting the BIM foundation pit support model through support resistance index analysis, thereby improving the durability, load-bearing reliability and overall adaptability of the foundation pit support structure in the construction cycle pollution environment.

[0039] Furthermore, based on the pollution monitoring data samples, support resistance index analysis is performed on the surface property parameters of each BIM free structural unit. The method includes:

[0040] S31. Identify multiple environmental exposure attributes corresponding to the multiple BIM free structural units;

[0041] S32. Divide the pollution monitoring data samples to obtain multiple sets of pollution dynamic prediction time series data subsets corresponding to the multiple environmental exposure attributes;

[0042] S33. Perform support defect simulation according to the multiple sets of pollution dynamic prediction time series data subsets and the surface attribute parameters of each BIM free structural unit to obtain multiple sets of support defect simulation data subsets.

[0043] S34. By analyzing the subsets of the multiple sets of support defect simulation data, the support resistance index of each BIM free structural unit is obtained.

[0044] In one feasible implementation, when analyzing the support resistance index of the surface attribute parameters of each BIM free structural unit based on pollution monitoring data samples, the first step is to identify multiple environmental exposure attributes corresponding to multiple BIM free structural units. Environmental exposure attributes refer to the type of environmental area in which the BIM free structural unit is located within the foundation pit support system. Different environmental areas have different types of pollutants, concentration levels, and erosion mechanisms. Specifically, environmental exposure attributes include types such as above-ground atmospheric zones, soil contact zones, and water level fluctuation zones. Above-ground atmospheric zones refer to areas exposed to the air environment above ground. Structural units in these zones are mainly affected by pollutants such as exhaust gases and dust in the air, such as lintels and above-ground support beams. Soil contact zones refer to areas in direct contact with the soil. Structural units in these zones are subject to erosion by acidic and alkaline substances and salt ions in the soil, such as the sidewalls of support piles deeply buried in the soil. Water level fluctuation zones refer to areas within the fluctuation range of the groundwater level. Structural units in these zones experience alternating wet and dry cycles, and pollutants migrate with the moisture and accumulate on the surface of the components during evaporation, such as support pile sections located near the groundwater level. When identifying the environmental exposure attributes of each BIM free structural unit, the spatial coordinates of the unit within the BIM foundation pit support model are analyzed, combined with geological survey data and hydrological data of the foundation pit. For example, the vertical elevation coordinates of the BIM free structural unit are extracted and compared with the ground elevation and groundwater level. If the vertical elevation is higher than the ground elevation, it is identified as a surface atmospheric zone; if the vertical elevation is lower than the groundwater level but higher than the lower limit of seasonal groundwater level fluctuations, it is identified as a water level fluctuation zone; and if the vertical elevation is lower than the lower limit of groundwater level fluctuations, it is identified as a soil contact zone. In this way, the corresponding environmental exposure attributes of each BIM free structural unit are labeled.

[0045] Subsequently, pollution monitoring data samples were segmented to obtain multiple subsets of dynamic pollution prediction time-series data corresponding to various environmental exposure attributes. Specifically, based on the spatial location information of each monitoring data point in the pollution monitoring data sample, it was categorized into the corresponding environmental exposure attribute category. For example, time-series data on nitrogen oxide concentrations, sulfide concentrations, and PM2.5 concentrations collected from above-ground air quality monitoring stations were categorized into the above-ground atmospheric pollution dynamic prediction time-series data subset. Time-series data on pH values, chloride ion concentrations, and sulfate ion concentrations measured at different depths from soil sampling points were categorized into the soil contact zone pollution dynamic prediction time-series data subset. Time-series data on dissolved oxygen content, ion concentrations, and corrosive gas content measured from groundwater monitoring wells were categorized into the water level fluctuation zone pollution dynamic prediction time-series data subset. Each subset of dynamic pollution prediction time-series data contains the dynamic change curve of pollutant concentrations in that environmental area over time. For example, the time-series data subset for dynamic prediction of surface atmospheric pollution records the daily variation trends of various pollutant concentrations in the atmosphere from day 1 to day N of construction, reflecting the correlation between construction activity intensity and pollutant emissions. This time-series data includes not only the average concentration levels of pollutants but also characteristic parameters such as concentration peaks, durations, and cumulative exposure, providing environmental load input for subsequent support defect simulation.

[0046] Next, support defects are simulated using multiple subsets of dynamic pollution prediction time-series data and surface attribute parameters of each BIM free structural unit, resulting in multiple subsets of simulated support defect data. This simulation process is based on multiple simulation sub-models included in the BIM foundation pit support model, such as a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a load-bearing capacity simulation sub-model. Specifically, for each BIM free structural unit, firstly, based on its labeled environmental exposure attributes, the corresponding pollution time-series data is extracted from multiple subsets of dynamic pollution prediction time-series data as the environmental load input for that structural unit. Then, the pollution time-series data and the surface attribute parameters of the BIM free structural unit are input into the pollution deposition simulation sub-model. The pollution deposition simulation sub-model calculates the deposition rate and accumulation of pollutants on the component surface using an adsorption kinetics model or a diffusion model, based on the pollutant concentration time-series curve and surface attribute parameters such as surface porosity and surface roughness. For example, for concrete surfaces, the embedding depth of dust particles is calculated based on surface porosity, and the adsorption amount of acidic gases is calculated based on surface chemical composition. The output quantitative simulation data of surface contamination includes quantitative parameters such as the mass of contaminant deposits per unit area, the depth of contaminant penetration, and the density of surface contaminant distribution. This data is then input into the material degradation simulation sub-model. Based on the type and accumulation of surface contaminants, combined with surface property parameters such as surface material type and protective coating thickness, the sub-model simulates the chemical corrosion and physical damage processes of contaminants on surface materials. For example, for steel surfaces, the electrochemical corrosion model calculates the rust depth and rate based on the concentration of deposited sulfides and chloride ions. For concrete surfaces, the carbonation model calculates the carbonation depth and rate based on the amount of adsorbed acidic gases. For coated surfaces, degradation parameters such as the coating thickness loss rate, the coating blistering area ratio, and the coating peeling probability are calculated based on the chemical erosion effect of contaminants. The output simulation data of surface coating corrosion defects includes curves showing the decay of coating thickness over time, the increase in concrete carbonation depth over time, and the development of steel corrosion depth over time. This data is then input into the load-bearing capacity simulation sub-model. The load-bearing capacity simulation sub-model calculates the impact of material degradation on the load-bearing capacity of components based on the degree of material degradation and the static and dynamic mechanical performance feature tags of BIM free structural elements. For example, for high axial compression members, the structural mechanics model is used to recalculate the axial compression capacity of the member based on the weakening of the protective layer caused by concrete carbonation and the reduction of the effective cross-section caused by steel corrosion, yielding the percentage decrease in load-bearing capacity. For bending-controlled members, the attenuation of bending load-bearing capacity is calculated based on steel corrosion caused by coating failure. The output structural mechanics load-bearing capacity degradation simulation data includes parameters such as the load-bearing capacity decay curve over time, the safety factor reduction curve over time, and the predicted time to reach the load-bearing capacity limit.

[0047] Through sequential calculations using the pollution deposition simulation sub-model, material degradation simulation sub-model, and load-bearing capacity simulation sub-model, the surface pollution quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical load-bearing capacity degradation simulation data are integrated into a subset of support defect simulation data for output. Since each BIM free structural unit undergoes the above simulation process, multiple subsets of support defect simulation data are ultimately obtained, each corresponding to the complete degradation evolution process of a BIM free structural unit under its environmental exposure conditions.

[0048] Subsequently, by analyzing multiple subsets of support defect simulation data, the support resistance index for each BIM free structural unit was obtained. This analysis process comprehensively considered the surface contamination quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data from the support defect simulation data subsets. Specifically, the support resistance index was calculated using a comprehensive scoring method, which comprehensively reflects the performance status of the structural unit at the end of the predicted construction period, including the remaining bearing capacity, the integrity of the surface protective layer, and the depth of contaminant erosion. For example, the support resistance index was defined as a weighted composite value of bearing capacity retention rate, coating integrity rate, and erosion depth control rate. The bearing capacity retention rate is equal to the ratio of the remaining bearing capacity at the end of the construction period to the initial design bearing capacity, reflecting the safety reserve of the structure. The coating integrity rate is equal to the ratio of the area of ​​coating that has not blistered or peeled to the total coating area, reflecting the effectiveness of surface protection. The erosion depth control rate is equal to the ratio of the allowable erosion depth limit to the actual predicted erosion depth, reflecting the degree of control over contaminant penetration. By setting weighting coefficients, the three factors were combined into a support resistance index between 0 and 1. Specifically, the weighting coefficients are configured differently based on the static mechanical performance characteristic tags of the BIM free structural units. For structural units bearing the main load-bearing functions, such as high axial compression members and bending control members, the weighting coefficient for load-bearing capacity retention rate is set higher, for example, to 0.6; the weighting coefficient for coating integrity rate is set to 0.25; and the weighting coefficient for erosion depth control rate is set to 0.15, to highlight the importance of load-bearing safety. For non-primary load-bearing members or auxiliary members, the weighting coefficients for coating integrity rate and erosion depth control rate can be appropriately increased, for example, to 0.4 and 0.3 respectively, while the weighting coefficient for load-bearing capacity retention rate is reduced to 0.3, focusing more on the long-term effectiveness of surface protection. Through differentiated weighting configuration, the closer the calculated support resistance index is to 1, the stronger the resistance to pollution degradation of the BIM free structural unit; the closer the value is to 0, the weaker the resistance. In this way, a quantitative support resistance index is calculated for each BIM free structural unit, providing a basis for evaluation in subsequent optimization design.

[0049] Through the above steps, a precise quantitative assessment of the support resistance of each BIM free structural unit under its specific environmental exposure conditions was achieved. By identifying environmental exposure attributes and dividing the pollution data subsets, it was ensured that structural units in different environmental areas were analyzed using pollution loads that matched their actual exposure conditions, avoiding misjudgments caused by general assessments. Through a three-layer progressive simulation sub-model of pollution deposition, material degradation, and load-bearing capacity degradation, a transmission chain from environmental pollution to structural performance degradation was established. This allows the support resistance index to not only reflect surface phenomena but also delve into the essence of structural safety, providing a foundation for differentiated optimization design.

[0050] Furthermore, the BIM foundation pit support model includes multiple simulation sub-models, including a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a bearing capacity simulation sub-model.

[0051] S331. Using the multiple simulation sub-models, support defects are simulated according to the multiple sets of pollution dynamic prediction time series data subsets and the surface attribute parameters of each BIM free structural unit. The obtained surface pollution quantitative simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are output as a set of support defect simulation data subsets.

[0052] In a preferred embodiment, the BIM foundation pit support model, in addition to containing the geometric and material property information of the support structure, also integrates multiple simulation sub-models for simulation analysis. These simulation sub-models are not structural units formed by physically disassembling the support structure, but rather simulation analysis models embedded in the BIM platform to perform specific calculation tasks. These multiple simulation sub-models include a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a load-bearing capacity simulation sub-model. These three models form a progressive relationship in terms of function, together constituting a simulation chain from pollutant erosion to structural performance degradation.

[0053] In simulating support defects, multiple simulation sub-models are used to simulate support defects based on multiple subsets of dynamic pollution prediction time-series data and the surface attribute parameters of each BIM free structural unit. The acquired surface pollution quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are output as a subset of support defect simulation data. Specifically, the pollution deposition simulation sub-model, material degradation simulation sub-model, and bearing capacity simulation sub-model are three independent simulation models integrated in the BIM foundation pit support model, which can be called to analyze any BIM free structural unit. When it is necessary to simulate support defects for a specific BIM free structural unit, the corresponding pollution time-series data is first extracted from multiple subsets of dynamic pollution prediction time-series data based on the environmental exposure attributes of the BIM free structural unit, and then the three simulation sub-models are called sequentially for calculation.

[0054] First, at the first level, the contamination deposition simulation sub-model is invoked. Taking the extracted contamination time-series data and the surface attribute parameters of the BIM free structural unit as input, the contamination deposition simulation sub-model calculates the deposition rate and accumulation of contaminants on the component surface using an adsorption kinetics model or a diffusion model, based on the contaminant concentration time-series curve and surface attribute parameters such as surface porosity and surface roughness. The output is quantitative simulation data of surface contamination, including parameters such as contaminant deposition mass per unit area, contaminant penetration depth, and surface contaminant distribution density.

[0055] Next, in the second level, the material degradation simulation sub-model is invoked. The surface contamination quantification simulation data output from the first level is used as input, along with surface attribute parameters such as the surface material type and protective coating thickness of the BIM free structural unit. Based on the type and accumulation of surface contaminants, the material degradation simulation sub-model simulates the chemical corrosion and physical damage processes of the surface materials. For example, for steel surfaces, an electrochemical corrosion model is used to calculate the rust depth and rate; for concrete surfaces, a carbonation model is used to calculate the carbonation depth; and for coated surfaces, the coating thickness loss rate and blistering area ratio are calculated. The output is simulation data of surface coating corrosion defects, including the coating thickness decay curve over time, the concrete carbonation depth growth curve over time, and the steel rust depth development curve over time.

[0056] Subsequently, at the third level, the load-bearing capacity simulation sub-model is invoked. Using the surface coating corrosion defect simulation data output from the second level as input, and combining it with the static and dynamic mechanical performance characteristic tags of the BIM free structural element, the load-bearing capacity simulation sub-model calculates the degree of impact on the component's load-bearing capacity based on the degree of material degradation. For example, for high axial compression members, based on the weakening of the protective layer caused by concrete carbonation and the reduction of the effective cross-section caused by steel corrosion, the axial compression bearing capacity of the member is recalculated using a structural mechanics model, yielding the percentage decrease in bearing capacity. The output is structural mechanics load-bearing capacity degradation simulation data, including parameters such as the bearing capacity decay curve over time and the safety factor reduction curve over time.

[0057] Through the aforementioned three-tiered progressive calculations, the surface contamination quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are integrated into a subset of support defect simulation data for output. Although the three simulation sub-models are independent simulation modules integrated in parallel within the BIM foundation pit support model, when analyzing specific BIM free structural units, they are sequentially invoked in the order of "contamination deposition → material deterioration → bearing capacity degradation," forming a hierarchical progressive chain. The output data of each level serves as the input data for the next level, resulting in a complete simulation result of the support defect evolution process.

[0058] Through a multi-level simulation sub-model joint invocation mechanism, cross-scale simulation analysis was achieved, ranging from environmental pollution loads to microscopic material damage and structural performance degradation. The pollution deposition simulation sub-model focuses on the physicochemical interaction between pollutants and component surfaces, the material degradation simulation sub-model focuses on the deterioration evolution of material microstructures, and the load-bearing capacity simulation sub-model focuses on the degradation law of macroscopic mechanical properties of the structure. This ensures that the subset of support defect simulation data not only includes surface phenomenon data but also in-depth structural safety performance data, providing support for accurately assessing support resistance indicators.

[0059] Furthermore, the BIM foundation pit support model is decomposed into support structure to obtain multiple BIM free structural elements. The method also includes:

[0060] S11. Decompose the connection points of the BIM foundation pit support model to obtain an initial set of structural units.

[0061] S12. Identify the static mechanical performance characteristic label, dynamic mechanical performance characteristic label, and surface material characteristic label of each initial structural unit in the initial structural unit set;

[0062] S13. Based on the static mechanical performance feature labels, dynamic mechanical performance feature labels, and surface material feature labels, the initial structural unit set is merged and clustered to obtain multiple BIM free structural units.

[0063] In a preferred embodiment, when acquiring multiple BIM free structural units, the BIM foundation pit support model is first decomposed by connection points to obtain an initial set of structural units. Connection points refer to the physical connection locations between various support components in the BIM foundation pit support model, including welding points, bolt connections, anchor points, and node plate connections. By virtually cutting at these connection points, the overall BIM foundation pit support model is decomposed into mutually independent initial structural units. For example, a virtual cut is made at the bolt connection between the support pile and the lintel beam, making a complete support pile an initial structural unit; a virtual cut is made at the node plate connection between the lintel beam and the support beam, making a section of the lintel beam another initial structural unit. By traversing all connection points in the BIM foundation pit support model and decomposing them sequentially, an initial set of structural units containing multiple initial structural units is obtained. At this point, the initial structural units are the smallest independent component units obtained by decomposing according to physical connection relationships, and have not yet been merged and integrated according to functional and environmental characteristics.

[0064] Then, the static mechanical performance characteristic labels, dynamic mechanical performance characteristic labels, and surface material characteristic labels of each initial structural unit in the initial structural unit set are identified. For the identification of static mechanical performance characteristic labels, the main types and amplitudes of internal forces borne by the initial structural unit in the support system are extracted by reading the structural static calculation results from the BIM model. Classification is based on controlling internal force characteristics; for example, support piles dominated by axial pressure and with high axial compression ratios are labeled as "high axial compression members," waist beams with bending moment as the main controlling internal force are labeled as "bending-controlled members," and anchor rods mainly bearing axial tension are labeled as "tension-controlled members." This static mechanical performance characteristic label reflects the load-bearing function positioning of the member in the load transfer path of the support system. For the identification of dynamic mechanical performance characteristic labels, the initial structural unit is classified by analyzing its deformation response and internal force adjustment patterns under dynamic conditions such as staged excavation of the foundation pit and changes in earth pressure over time. For example, retaining walls that adjust their displacement according to changes in earth pressure are labeled "earth pressure response components," support beams whose deformation can be controlled by adjusting prestress are labeled "support adjustment components," and anchor rods that require constant tension are labeled "prestress retention components." This dynamic mechanical performance characteristic label reflects the component's response characteristics to time-varying loads. For the identification of surface material characteristic labels, the material composition type of the exposed surface of the component is extracted by reading the material property data in the BIM model. For example, plain concrete components without protective coatings are labeled "concrete surfaces," steel components without anti-corrosion treatment are labeled "exposed steel surfaces," steel components that have undergone hot-dip galvanizing are labeled "hot-dip galvanized surfaces," and components coated with epoxy resin are labeled "epoxy-coated surfaces." This surface material characteristic label determines the initial barrier performance of the component surface against contaminants. Through the label identification process, a label combination of "static mechanical performance characteristic label + dynamic mechanical performance characteristic label + surface material characteristic label" is established for each initial structural unit. For example, a certain support pile is marked as "high axial compression member + earth pressure response member + concrete surface", and a certain section of waist beam is marked as "bending control member + support adjustment member + exposed steel surface".

[0065] Next, based on the identified static mechanical performance characteristic labels, dynamic mechanical performance characteristic labels, and surface material characteristic labels, the initial structural unit set is merged and clustered to obtain multiple BIM free structural units. The clustering principle is to merge initial structural units with the same combination of the three characteristic labels into the same BIM free structural unit, so that a unified evaluation method and optimization strategy can be adopted for component groups with consistent functional characteristics, mechanical behavior, and surface materials in subsequent analysis. Specifically, the initial structural unit set is traversed, the label combination of each initial structural unit is extracted, and initial structural units with completely identical label combinations are grouped together. For example, all initial structural units labeled "high axial compression member + earth pressure response member + concrete surface" are merged into one BIM free structural unit. These support piles play the same static bearing role in the support system, have the same dynamic response characteristics, and have the same surface material type. Therefore, their mechanisms and degrees of contamination are similar, and they can be analyzed and optimized as a whole. All initial structural units labeled "bending control member + support adjustment member + exposed steel surface" are merged into another BIM free structural unit. All initial structural units labeled "tension control component + prestress retention component + hot-dip galvanized surface" are merged into another BIM free structural unit. Through clustering and merging, multiple BIM free structural units are obtained. Each BIM free structural unit contains one or more initial structural units. Although these initial structural units may be spatially dispersed, they exhibit high consistency in functional characteristics, mechanical behavior, and surface materials. Compared to the initial structural unit set, the number of BIM free structural units is usually significantly reduced, but each BIM free structural unit represents a group of components with similar characteristics, facilitating classification management and targeted analysis. For example, the original BIM foundation pit support model may contain hundreds of support piles, dozens of joists, dozens of anchor bolts, and other initial structural units. After clustering and merging, it may form more than ten BIM free structural units, each corresponding to a specific functional type and surface characteristic combination.

[0066] Through the above steps, a structured decomposition was achieved, from the overall BIM foundation pit support model to multiple functionally grouped BIM free structural units. This decomposition method is not a simple geometric cut, but rather a classification based on mechanical function, dynamic characteristics, and surface materials. This allows subsequent pollution impact analysis and optimization design to adopt differentiated strategies for different types of component groups, improving analysis efficiency while ensuring the relevance of optimization solutions.

[0067] Furthermore, by analyzing subsets of the multiple sets of simulated support defect data, the support resistance index of each BIM free structural unit is obtained. The method includes:

[0068] S341. By analyzing the subsets of the multiple sets of support defect simulation data, the initial node resistance index of each BIM free structural unit is obtained.

[0069] S342. Construct a failure propagation network based on the multiple BIM free structural units as nodes;

[0070] S343. Analyze the failure propagation network based on the subset of multiple sets of support defect simulation data, and establish the weighted edges of the failure propagation network based on the influence relationship of failure propagation to obtain the failure propagation weighted network.

[0071] S344. Update the initial node resistance index of each BIM free structural unit according to the failure propagation weight network to obtain the support resistance index of each BIM free structural unit.

[0072] In a preferred embodiment, firstly, the initial node resistance index of each BIM free structural unit is obtained by analyzing multiple subsets of support defect simulation data. Specifically, for each BIM free structural unit, surface contamination quantification simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are extracted from its corresponding subset of support defect simulation data. Based on these simulation data, the bearing capacity retention rate, coating integrity rate, and erosion depth control rate of the BIM free structural unit are calculated. The bearing capacity retention rate is equal to the ratio of the remaining bearing capacity at the end of the construction period to the initial design bearing capacity, reflecting the structural safety reserve; the coating integrity rate is equal to the ratio of the area of ​​coating that has not blistered or peeled to the total coating area, reflecting the effectiveness of surface protection; and the erosion depth control rate is equal to the ratio of the allowable erosion depth limit to the actual predicted erosion depth, reflecting the degree of control over pollutant penetration. Then, weighting coefficients are set according to the static mechanical performance characteristic labels of the BIM free structural unit, and the three indicators are weighted and comprehensively calculated. For structural units bearing primary load-bearing functions, such as high axial compression members and bending control members, the weighting coefficient for load-bearing capacity retention rate is set relatively high, for example, 0.6; the weighting coefficient for coating integrity rate is set to 0.25; and the weighting coefficient for erosion depth control rate is set to 0.15. For non-primary load-bearing members, the weighting coefficients for coating integrity rate and erosion depth control rate can be appropriately increased, for example, set to 0.4 and 0.3 respectively, while the weighting coefficient for load-bearing capacity retention rate is reduced to 0.3. The initial node resistance index for this BIM free structural unit is obtained through weighted calculation. This index value ranges from 0 to 1, with values ​​closer to 1 indicating stronger resistance to pollution degradation. However, the initial node resistance index only considers the performance degradation of the BIM free structural unit itself in a polluted environment and does not consider the interaction between this structural unit and other structural units. In actual support structure systems, the failure of one structural unit may trigger load redistribution and deterioration of the bearing condition of adjacent or connected structural units, i.e., there is a failure propagation effect. Therefore, the initial node resistance index, as a preliminary assessment result, needs further correction through failure propagation network analysis.

[0073] Next, a failure propagation network based on multiple BIM free structural units as nodes is constructed. Since a BIM free structural unit is an abstract category formed by clustering and merging multiple initial structural units, the failure propagation relationships between its nodes need to be extracted from the actual connection relationships of the initial structural units in the BIM foundation pit support model. Therefore, the construction of the failure propagation network is first based on the connection analysis of the initial structural units, and then the analysis results are mapped to the BIM free structural unit level. Specifically, the connection relationships and load transfer paths between all initial structural units are first identified in the BIM foundation pit support model. By reading the connection point information in the BIM foundation pit support model, direct physical connections between initial structural units are identified. For example, a support pile is connected to a section of joist beam via bolts, and a section of joist beam is connected to a support beam via a node plate; these connections are clearly recorded in the BIM foundation pit support model. In addition, indirect mechanical relationships also need to be identified. For example, although an anchor rod is not directly connected to the support pile, it transfers tension to the support pile through the joist beam; therefore, there is an indirect failure propagation path between the anchor rod and the support pile. Then, the connection relationships at the initial structural unit level are mapped to the BIM free structural unit level. Since each initial structural unit belongs to a specific BIM free structural unit, if there is a connection relationship between initial structural unit A and initial structural unit B, and A belongs to BIM free structural unit α and B belongs to BIM free structural unit β, then an edge connection is established or strengthened between node α and node β. By traversing the connection relationships between all initial structural units in the BIM foundation pit support model, the number of connections and connection patterns between different BIM free structural units are counted, and a failure propagation network with BIM free structural units as nodes is constructed. In this failure propagation network, each node represents a BIM free structural unit, and the edges between nodes indicate that there is a failure propagation relationship between the initial structural units contained in two BIM free structural units.

[0074] Subsequently, the failure propagation network was analyzed based on subsets of multiple sets of simulated support defects. Weighted edges of the failure propagation network were established based on the influence relationships of failure propagation, resulting in a weighted failure propagation network. In the constructed failure propagation network, edges only represent failure propagation relationships between BIM free structural units, but the strength of these relationships is not quantified. The purpose of establishing weighted edges in the failure propagation network is to assign weight values ​​to each edge, quantifying the degree of influence of failure propagation. Specifically, for each edge connecting node α and node β in the failure propagation network, it is necessary to analyze the impact of the failure of the initial structural unit in the BIM free structural unit corresponding to node α on the initial structural unit in the BIM free structural unit corresponding to node β. This analysis is achieved through structural failure simulation in the BIM foundation pit support model. For example, simulating a 20% decrease in the bearing capacity of a support pile in the BIM free structural unit corresponding to node α, the structural static calculation is re-performed, and the changes in internal forces of each segment of the lintel beam in the BIM free structural unit corresponding to node β are observed. The percentage increase in internal forces of all initial structural units in node β is statistically analyzed, and the average increase in internal forces is calculated. If the average internal force increase is large, for example, reaching 15%, it indicates a strong impact of node α on the failure propagation of node β, and the weight value of this edge is set higher, for example, to 0.75. If the average internal force increase is small, for example, only 3%, it indicates a weaker impact, and the weight value is set lower, for example, to 0.15. To comprehensively evaluate the impact of failure propagation, failure simulations are performed on multiple representative initial structural units in node α to calculate the degree of impact on node β, and then the average value is taken as the weight of the edge "node α—node β". By performing the above analysis on each edge in the failure propagation network and assigning corresponding weight values ​​to all edges, a failure propagation weight network is obtained. This failure propagation weight network not only includes the topological connection relationship between BIM free structural units, but also quantifies the strength of failure propagation.

[0075] Subsequently, the initial node resistance index of each BIM free structural unit is updated according to the failure propagation weight network to obtain the support resistance index of each BIM free structural unit. The principle of the update is to consider the influence of the resistance status of adjacent nodes on the current node. If the resistance of an adjacent node is low, the node will need to bear additional loads after the failure of the adjacent node, and its actual resistance level should be lower than the initial node resistance index. Specifically, for each node in the failure propagation weight network, all edges connected to it are traversed, and the weight value of the edge and the initial node resistance index of the node at the other end of the edge are extracted. The resistance correction amount of the node affected by the adjacent nodes is calculated. If the resistance index of the adjacent node is low and the edge weight is large, it means that the node is easily affected by the failure of the adjacent node, and its resistance correction amount is large, requiring a significant downward correction of the initial node resistance index. Conversely, if the resistance of the adjacent node is high or the edge weight is small, the correction amount is small.

[0076] Specifically, the updated support resistance index is calculated using a formula. The calculation employs a weighted combination of the element's own resistance and the influence of adjacent nodes. The formula is: Support Resistance Index = λ × Initial Node Resistance Index + (1-λ) × Weighted Average of Adjacent Node Resistance, where λ is the self-resistance influence coefficient, determined based on the structural redundancy of the BIM free structural unit within the support system. For support systems with high structural redundancy, where loads can be redistributed through multiple paths after adjacent node failure, a higher λ value is set, typically 0.8, indicating that the node primarily relies on its own resistance and is less affected by adjacent node failure. For support systems with low structural redundancy, where load redistribution paths are singular after adjacent node failure and the node must bear most of the additional load, a lower λ value is set, typically 0.7, indicating a greater need to consider the impact of adjacent node failure propagation. In this way, the calculation of the support resistance index reflects both the inherent pollution resistance of the BIM free structural unit and the potential risks it faces in the support system due to adjacent node failure. The weighted average resistance of adjacent nodes is calculated as follows: multiply the initial resistance index of each adjacent node by the weight value of the corresponding connecting edge, sum the results for all adjacent nodes, and then divide by the sum of the weight values ​​of all connecting edges to obtain the normalized weighted average. For example, if a node is connected to three adjacent nodes with weight values ​​of 0.6, 0.4, and 0.3 respectively, and the initial resistance indices of the corresponding adjacent nodes are 0.65, 0.72, and 0.58 respectively, then the weighted average resistance of adjacent nodes = (0.65*0.6 + 0.72*0.4 + 0.58*0.3) / (0.6 + 0.4 + 0.3) = 0.656. If the initial resistance index of the node is 0.70, and the structural redundancy is high (λ = 0.8), then the updated support resistance index = 0.8 × 0.70 + (1 - 0.8) × 0.656 = 0.691.

[0077] Through the above steps, a shift from single-component resistance assessment to overall system resistance assessment is achieved. Initial node resistance indices are calculated based on the performance degradation of each initial structural unit, while the final support resistance index is derived through failure propagation network analysis, comprehensively considering the position of the BIM free structural unit within the support system, its mechanical relationship with other BIM free structural units, and the impact of failure propagation paths. This resistance index calculation method based on failure propagation networks is built upon the real connection relationships of the initial structural units in the BIM foundation pit support model. By mapping to the BIM free structural unit level for system-level analysis, it more realistically reflects the system reliability of the support structure in polluted environments, avoiding the underestimation of risk caused by isolated assessments, and providing a more scientific and reasonable basis for subsequent optimization design.

[0078] Furthermore, from the candidate optimization design strategy library, the identification optimization design strategy that maximizes the support resistance index for each BIM free structural unit is obtained, generating multiple identification optimization design strategies. The method includes:

[0079] S41. Each strategy in the candidate optimization design strategy library corresponds to a surface treatment parameter of a free structural unit;

[0080] S42. Traverse the multiple BIM free structural units and extract the identified BIM free structural units that are less than the preset support resistance index threshold.

[0081] S43. Taking the preset support resistance index threshold as the target, optimize the surface treatment parameters of the identified BIM free structural unit from the candidate optimization design strategy library to obtain the identification optimization design strategy corresponding to each identified BIM free structural unit.

[0082] In a preferred embodiment, firstly, the structural composition of the candidate optimization design strategy library is defined, with each strategy in the library corresponding to a set of surface treatment parameters. Specifically, each BIM free structural unit has its corresponding candidate optimization design strategy library, which contains multiple candidate strategies. Each candidate strategy defines a specific set of surface treatment parameters, including the specific values ​​of parameters such as surface protective coating type, coating thickness, surface sealing treatment method, and type and ratio of anti-corrosion additives. For example, for a BIM free structural unit labeled as "high axial compression member + earth pressure response member + concrete surface", its candidate optimization design strategy library may contain multiple different combinations of surface treatment parameters, such as Strategy 1 "epoxy resin coating + 150μm thickness + silane primer treatment", Strategy 2 "polyurethane coating + 200μm thickness + surface sandblasting treatment", and Strategy 3 "silane impregnation treatment + penetration depth 5mm + no coating". Each strategy, through the differences in its surface treatment parameters, corresponds to different pollution protection performance and cost levels.

[0083] Then, multiple BIM free structural units are traversed, and BIM free structural units with support resistance indices below the preset threshold are identified. The preset support resistance indices threshold is a minimum resistance level pre-set according to the design requirements of the foundation pit support structure. For example, if the design requires the support structure to maintain no less than 80% of its initial performance after the entire construction period, the preset support resistance indices threshold can be set to 0.80. In practice, the support resistance index of each BIM free structural unit is read one by one and compared with the preset support resistance indices threshold. If the support resistance index of a certain BIM free structural unit is less than the preset support resistance indices threshold, then the BIM free structural unit is identified as an identified BIM free structural unit that needs optimization. For example, if the support resistance index of a certain BIM free structural unit is 0.72, and the preset support resistance indices threshold is 0.80, then the BIM free structural unit is identified as an identified BIM free structural unit, and its support resistance needs to be improved by optimizing its surface treatment parameters. If the support resistance index of a BIM free structural element is 0.85, which has reached or exceeded the preset support resistance index threshold, then the BIM free structural element does not need to be optimized and the original design scheme can be maintained. Through this screening mechanism, structural elements with insufficient support resistance are identified as targets for subsequent optimization, avoiding unnecessary over-design of structural elements whose performance already meets the requirements.

[0084] Next, targeting a preset support resistance index threshold, the surface treatment parameters of the identified BIM free structural units are optimized from the candidate optimization design strategy library to obtain the identification optimization design strategy corresponding to each identified BIM free structural unit. The goal of this optimization process is to find a candidate strategy for each identified BIM free structural unit, such that after adopting the surface treatment parameters of the strategy, the support resistance index of the BIM free structural unit can be greater than or equal to the preset support resistance index threshold. Specifically, for each identified BIM free structural unit, candidate strategies are extracted one by one from its corresponding candidate optimization design strategy library. For each candidate strategy, the surface treatment parameters defined by it are updated to the surface attribute parameters of the identified BIM free structural unit, and then the support defect simulation process in step S33 and the support resistance index calculation process in step S34 are re-executed to obtain a new support resistance index after adopting the candidate strategy. The new support resistance index is compared with the preset support resistance index threshold to determine whether the candidate strategy can make the support resistance index meet the standard. The algorithm iterates through all candidate optimization design strategies in the candidate optimization design strategy library for each BIM free structural unit, selecting a set of feasible strategies that ensure the support resistance index is greater than or equal to a preset support resistance index threshold. If multiple feasible strategies exist, engineering constraints such as construction cost, construction period, and material availability are further considered, and the strategy with the best overall cost-effectiveness is selected as the optimal design strategy for the BIM free structural unit. For example, if both strategy A and strategy B can achieve a support resistance index of 0.82, meeting the threshold requirement of 0.80, but strategy A has a material cost of 200 yuan per square meter and a construction period of 3 days, while strategy B has a material cost of 150 yuan per square meter and a construction period of 2 days, then, given similar performance, strategy B, with its lower cost and shorter construction period, is prioritized as the optimal design strategy. By sequentially performing the above optimization process on all BIM free structural units, a corresponding optimal design strategy is determined for each BIM free structural unit, generating multiple optimal design strategies. Each identifier optimization design strategy includes the optimized surface treatment parameter configuration for the BIM free structural unit, such as specific technical solutions like using epoxy resin coating, coating thickness of 200μm, and surface pretreatment using sandblasting to remove rust, providing clear design instructions for subsequent optimization and adjustment of the BIM foundation pit support model.

[0085] Through the above steps, precise optimization strategy selection based on the quantitative assessment results of support resistance indicators was achieved. By optimizing only the identified BIM free structural units with insufficient support resistance, the resource waste and cost increase caused by global optimization were avoided. The multi-scheme selection space provided by the candidate optimization design strategy library, combined with the quantitative assessment of support defect simulation, ensured the effectiveness and feasibility of the optimization strategy, enabling the optimized design to meet support resistance requirements while also considering engineering economy and construction feasibility.

[0086] Furthermore, the method further includes optimizing the surface treatment parameters of the identified BIM free structural unit from the candidate optimization design strategy library, and also includes:

[0087] S431. Obtain the key BIM free structural units in the identified BIM free structural units according to the bearing position;

[0088] S432. After obtaining the identification optimization design strategy corresponding to the key BIM free structural unit, the candidate optimization design strategies of the remaining identification BIM free structural units are constrained by constructing consistency constraints.

[0089] In a preferred embodiment, firstly, key BIM free structural units are identified from the identified BIM free structural units according to their load-bearing locations. A key BIM free structural unit refers to a structural unit within the identified BIM free structural units that bears the main load-bearing function, is located on a critical load transfer path, or has serious consequences if it fails. Specifically, key BIM free structural units are identified by analyzing the load-bearing location and functional role of each identified BIM free structural unit in the support system. The assessment of the load-bearing location includes the following aspects: First, based on the structural static calculation results in the BIM foundation pit support model, the internal force level borne by each identified BIM free structural unit is extracted; structural units with larger internal force amplitudes are more critical in their load-bearing locations. Second, the load transfer path is analyzed; structural units located on the main load transfer path, such as the main support piles that directly bear earth pressure and transfer the load to the support system, are critical in their load-bearing locations. Third, structural redundancy is assessed; if a identified BIM free structural unit fails and lacks an alternative load transfer path, leading to a high risk of overall instability of the support system, then that structural unit is considered a key BIM free structural unit. For example, in identifying BIM free structural elements, if there is a main support pile category labeled "high axial compression member + earth pressure response member + concrete surface," this category bears high axial compression and directly resists earth pressure, then it is identified as a critical BIM free structural element. If there is a lintel beam category labeled "bending control member + support adjustment member + exposed steel surface," but this category only plays an auxiliary role in load transfer and has a low internal force level, then it is not identified as a critical BIM free structural element. Through this evaluation mechanism, critical BIM free structural elements are selected from the identified BIM free structural elements, and optimized design strategies are prioritized for them to ensure the safety of key components of the support system.

[0090] After obtaining the identification optimization design strategy corresponding to the key BIM free structural unit, consistency constraints are constructed to constrain the candidate optimization design strategies of the remaining identification BIM free structural units. The purpose of consistency constraints is to ensure that adjacent or functionally related identification BIM free structural units adopt coordinated surface treatment schemes, avoiding problems such as appearance inconsistency, complex construction procedures, and scattered material procurement caused by excessive differences in treatment methods. Specifically, the consistency constraints require that the surface treatment parameters of the remaining identification BIM free structural units should maintain a certain degree of consistency with the identification optimization design strategy of the determined key BIM free structural unit when selecting candidate optimization design strategies. The consistency constraints are reflected in aspects such as the type of surface protective coating, the coating thickness range, and the surface pretreatment process. For example, if the identification optimization design strategy of the key BIM free structural unit adopts an epoxy resin coating system with a coating thickness of 200μm, then consistency constraints are constructed for the remaining identification BIM free structural units: requiring that their candidate optimization design strategies also preferentially choose an epoxy resin coating system, and the deviation of the coating thickness from 200μm does not exceed the specified constraint error threshold.

[0091] When applying consistency constraints, candidate strategies that meet the consistency constraints are selected from the candidate optimization design strategy library of the remaining BIM free structural units, forming a subset of feasible strategies after constraints. Then, within this subset of feasible strategies, the strategy that enables the support resistance index to reach the preset threshold and has the best overall cost-effectiveness is selected as the identification optimization design strategy according to the optimization method in step S43. Through this constraint mechanism, the support resistance of the remaining BIM free structural units is improved, and the overall optimization scheme is coordinated and unified. For example, if the key BIM free structural unit adopts the identification optimization design strategy of "epoxy resin coating + 200μm thickness + sandblasting pretreatment", then consistency constraints are constructed for the remaining BIM free structural units: the coating type must be epoxy resin, the coating thickness must be in the range of 180μm to 220μm, and the pretreatment process must be sandblasting or grinding. Under this constraint, strategies that meet the constraint are selected from the candidate optimization design strategy library of the remaining BIM free structural units, such as "epoxy resin coating + 190μm thickness + sandblasting pretreatment" and "epoxy resin coating + 210μm thickness + grinding pretreatment". Then, the strategy that meets the support resistance index and has a better cost is selected from these strategies.

[0092] Through the above steps, the hierarchical and coordinated optimization design was achieved. By prioritizing the optimization schemes of key BIM free structural units, the safety of the most important parts of the support system was ensured. By applying consistency constraints to the remaining identified BIM free structural units, the overall coordination of the optimization scheme was achieved while ensuring performance requirements, reducing construction complexity, improving material utilization efficiency, and making the optimized design more feasible for engineering implementation.

[0093] Furthermore, before applying consistency constraints to the candidate optimization design strategies of the remaining BIM free structural units by constructing consistency constraints, the method also includes:

[0094] S433. Determine whether the environmental exposure attributes of the remaining BIM free structural units are consistent with the key BIM free structural units.

[0095] S434. If consistent, apply consistency constraints to the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units using the first constraint error threshold.

[0096] S435. If there is no consistency, the surface treatment parameters of the candidate optimization design strategies of the remaining BIM free structural units are constrained for consistency using the second constraint error threshold, wherein the first constraint error threshold is less than the second constraint error threshold.

[0097] In a preferred embodiment, before applying consistency constraints to the candidate optimization design strategies of the remaining BIM free structural units by constructing consistency constraints, differentiated constraint strengths are set based on the differences in environmental exposure attributes. First, it is determined whether the environmental exposure attributes of the remaining BIM free structural units are consistent with those of the key BIM free structural units. Environmental exposure attributes, identified in step S31, include types such as aboveground atmospheric zone, soil contact zone, and water level fluctuation zone. Different environmental exposure attributes correspond to different pollutant types, concentration levels, and erosion mechanisms. Specifically, the environmental exposure attributes corresponding to the key BIM free structural units are extracted, and then the environmental exposure attributes of each remaining BIM free structural unit are read one by one for consistency comparison. If the environmental exposure attribute of a remaining BIM free structural unit is the same as that of the key BIM free structural unit, for example, both are exposed to the aboveground atmospheric zone, then the environmental exposure attributes are determined to be consistent. If the environmental exposure attribute of a remaining BIM free structural unit is different from that of the key BIM free structural unit, for example, the key BIM free structural unit is exposed to the aboveground atmospheric zone, while the remaining BIM free structural unit is exposed to the soil contact zone, then the environmental exposure attributes are determined to be inconsistent.

[0098] If the environmental exposure attributes are consistent, the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units are constrained for consistency using a first constraint error threshold. The first constraint error threshold defines the allowable deviation range between the surface treatment parameters and the corresponding parameters in the key BIM free structural unit's identification optimization design strategy. Specifically, since consistent environmental exposure attributes mean that the remaining BIM free structural units and the key BIM free structural units face the same pollution exposure conditions, similar surface treatment parameters can achieve similar protective effects; therefore, a stricter consistency constraint can be set. A smaller first constraint error threshold requires that the surface treatment parameters of the remaining BIM free structural units be highly consistent with those of the key BIM free structural units. For example, if the key BIM free structural unit's identification optimization design strategy uses an epoxy resin coating with a thickness of 200 μm, then for the remaining BIM free structural units with consistent environmental exposure attributes, the first constraint error threshold can be set to ±10%, meaning the coating type must be epoxy resin, the coating thickness must be in the range of 180 μm to 220 μm, and the surface pretreatment process should also be consistent or use an equivalent process. This strict constraint ensures that structural units in the same environmental area adopt uniform protection standards, which facilitates construction organization and quality control.

[0099] If environmental exposure attributes are inconsistent, a second constraint error threshold is used to constrain the surface treatment parameters of candidate optimization design strategies for the remaining BIM free structural units to ensure consistency. The first constraint error threshold is smaller than the second constraint error threshold. The second constraint error threshold also defines the allowable deviation range for surface treatment parameters, but allows for greater parameter adjustment space due to differences in environmental exposure conditions. Specifically, inconsistent environmental exposure attributes mean that the remaining BIM free structural units and the critical BIM free structural units face different pollution exposure conditions, requiring differentiated protective measures based on their respective environmental characteristics. The larger second constraint error threshold allows for appropriate differential adjustments to the surface treatment parameters of the remaining BIM free structural units, while maintaining overall consistency, to adapt to different environmental requirements. For example, if a critical BIM free structural unit is located in the above-ground atmospheric zone and uses an epoxy resin coating with a thickness of 200 μm, while a remaining BIM free structural unit is located in the soil contact zone, the second constraint error threshold can be set to ±30%. This means that different coating types are allowed, but they must belong to the same protection level. The coating thickness can be adjusted within the range of 140 μm to 260 μm. Different protection systems, such as polyurethane coating or composite coating, can even be used to better cope with chemical erosion in the soil.

[0100] The principle of setting the first constraint error threshold to be less than the second constraint error threshold reflects an optimization strategy that emphasizes consistency under the same environmental conditions but allows for differences under different environmental conditions. For example, the first constraint error threshold can be set at ±10% of the coating thickness, and the coating type must be the same, while the second constraint error threshold can be set at ±30% of the coating thickness, and the coating types can be different but the protection level should be equivalent. This differentiated constraint mechanism ensures a certain degree of coordination in the overall optimization scheme, facilitating construction management and material procurement, while also allowing for targeted protection designs for different environmental exposure conditions, thus improving the scientific rigor and applicability of the optimization scheme.

[0101] After completing the environmental exposure attribute judgment and constraint error threshold setting, candidate strategies that meet the constraint conditions are selected from the candidate optimization design strategy library of the remaining BIM free structural units according to the corresponding constraint error threshold, forming a subset of feasible strategies after constraints. Then, the strategy that makes the support resistance index meet the standard and has the best overall cost performance is selected as the identification optimization design strategy.

[0102] Through the above steps, a differentiated consistency constraint mechanism based on differences in environmental exposure attributes is achieved. For structural units located in the same environmental area, strict consistency constraints are adopted to ensure the uniformity of the protection scheme and the convenience of construction. For structural units located in different environmental areas, consistency constraints are relaxed, allowing for differentiated design based on environmental characteristics. This maintains the overall scheme's coordination while achieving targeted protection, improving the rationality and economy of the optimized design.

[0103] Furthermore, the pollution monitoring data sample is obtained by reading historical pollution monitoring data from a group of analogous construction projects to the target construction project;

[0104] Wherein, the similarity between the construction type, construction equipment, and construction environment of the analogous construction project group and the target construction project is greater than a preset threshold.

[0105] In a preferred embodiment, pollution monitoring data samples are obtained by reading historical pollution monitoring data from a group of analogous construction projects related to the target construction project. The target construction project refers to a project currently requiring optimized design of its foundation pit support. Since this project is still in the design phase or early construction stage, complete pollution monitoring data is lacking; therefore, historical data from analogous construction projects is needed for predictive analysis. The analogous construction project group refers to a set of historical construction projects that are highly similar to the target construction project in terms of construction type, equipment, and environment, with a similarity exceeding a preset threshold.

[0106] Specifically, similarity assessment can include three aspects: construction type, construction equipment, and construction environment. Regarding construction type, the analogous construction project and the target construction project should belong to the same or similar engineering category. For example, if the target construction project is a deep foundation pit project for an urban subway station, then the analogous construction project should also be a subway station foundation pit, an underground commercial facility foundation pit, or a similar urban deep foundation pit project, rather than projects with significant differences in construction type, such as shallow foundation pits or mountainous foundation pits. Similarity in construction type ensures the comparability of key factors such as excavation depth, support structure form, and construction procedures. Regarding construction equipment, the main construction machinery and equipment used in the analogous construction project should be consistent with or similar to that in the target construction project. For example, if the target construction project uses rotary drilling rigs for support pile construction, tracked excavators for earthwork excavation, and diesel-powered concrete mixing and transportation equipment, then the analogous construction project should also use the same types of construction equipment. The similarity of construction equipment directly determines the comparability of pollutant emission characteristics, because differences in fuel type, emission standards, and operating efficiency of different equipment will lead to significant differences in pollution characteristics such as exhaust gas composition and dust generation. Regarding the construction environment, the geographical location, climate conditions, and surrounding environment of the analogous construction project should be similar to those of the target construction project. For example, if the target construction project is located in a city center with dense surrounding buildings, a high groundwater level, and cohesive soil, then the analogous construction project should also be a project with an urban environment and similar hydrogeological conditions. The similarity of the construction environment affects the diffusion, deposition, and accumulation patterns of pollutants. For example, the shielding effect of buildings in an urban environment affects the diffusion of air pollutants, and the level of groundwater affects the pollution exposure characteristics of areas with fluctuating water levels. Weight coefficients are assigned to the three dimensions of construction type, construction equipment, and construction environment, and an overall similarity score is calculated for each historical construction project and the target construction project. For example, the weight for the construction type dimension is set to 0.4, the weight for the construction equipment dimension is set to 0.3, and the weight for the construction environment dimension is set to 0.3. An evaluation group composed of experts in foundation pit engineering, construction technology, environmental engineering, and other fields is organized to score the similarity between each historical construction project and the target construction project in the three dimensions. The score range for each dimension is 0 to 1, where 1 represents complete similarity and 0 represents complete dissimilarity. The overall similarity score is obtained by weighting and summing the expert scores from the three dimensions. A preset threshold of 0.75 can be set to include only historical construction projects with an overall similarity score of 0.75 or higher into the analog construction project group.

[0107] After selecting and forming a group of analogous construction projects, historical pollution monitoring data is retrieved from this group. This historical pollution monitoring data includes pollutant concentration data monitored at different time points and spatial locations during the construction period of the analogous construction projects. Examples include time-series records of atmospheric nitrogen oxide, sulfide, and particulate matter PM2.5 and PM10 concentrations; spatial distribution data of soil pH, chloride ion, and sulfate ion concentrations; and monitoring records of dissolved oxygen and corrosive ion content in groundwater. The historical pollution monitoring data from multiple projects within the analogous construction project group are then integrated to form a pollution monitoring data sample.

[0108] Because the analogous construction project group and the target construction project share high similarities in key characteristics, their historical pollution monitoring data can accurately predict the potential pollution exposure of the target construction project during construction. This method, based on historical data from analogous projects, avoids the time cost of waiting for data collection after the target project's actual construction, enabling predictive assessments and optimized designs for pollution impacts during the design phase, thus improving the design's foresight. Furthermore, by setting strict similarity thresholds, the representativeness and reliability of the analogous data are ensured, providing effective data support for subsequent support defect simulation and resistance index analysis.

[0109] Example 2, as Figure 2 As shown, based on the same inventive concept as the BIM-based optimization design method for foundation pit support provided in Embodiment 1, this embodiment of the invention also provides a BIM-based optimization design system for foundation pit support, comprising:

[0110] The model construction and splitting module 11 is used to construct a BIM foundation pit support model, split the support structure of the BIM foundation pit support model, and obtain multiple BIM free structural units and surface attribute parameters of each BIM free structural unit.

[0111] The strategy library construction module 12 is used to construct a candidate optimization design strategy library for each BIM free structural unit among the plurality of BIM free structural units;

[0112] The resistance index analysis module 13 is used to collect pollution monitoring data samples of the construction project during the construction period, and to perform support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples, and to obtain the support resistance index calculation results.

[0113] The optimization strategy execution module 14 is used to obtain the identification optimization design strategy that maximizes the support resistance index of each BIM free structural unit from the candidate optimization design strategy library according to the support resistance index calculation results, generate multiple identification optimization design strategies, and optimize the BIM foundation pit support model according to the multiple identification optimization design strategies.

[0114] Furthermore, the resistance index analysis module 13 is also used for:

[0115] Identify multiple environmental exposure attributes corresponding to the multiple BIM free structural units;

[0116] Divide the pollution monitoring data samples to obtain multiple sets of time-series pollution dynamic prediction data subsets corresponding to the multiple environmental exposure attributes;

[0117] Based on the aforementioned subsets of dynamic pollution prediction time series data and the surface attribute parameters of each BIM free structural unit, support defect simulation is performed to obtain multiple subsets of support defect simulation data.

[0118] By analyzing the subsets of the multiple sets of support defect simulation data, the support resistance index of each BIM free structural unit is obtained.

[0119] Furthermore, the BIM foundation pit support model includes multiple simulation sub-models, including a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a bearing capacity simulation sub-model; the resistance index analysis module 13 is also used for:

[0120] Using the multiple simulation sub-models, support defects are simulated according to the multiple sets of pollution dynamic prediction time series data subsets and the surface attribute parameters of each BIM free structural unit. The obtained surface pollution quantitative simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are output as a set of support defect simulation data subsets.

[0121] Furthermore, the model building splitting module 11 is also used for:

[0122] The connection points of the BIM foundation pit support model are decomposed to obtain an initial set of structural units;

[0123] Identify the static mechanical property characteristic tags, dynamic mechanical property characteristic tags, and surface material characteristic tags of each initial structural unit in the initial structural unit set;

[0124] Based on the static mechanical performance feature labels, dynamic mechanical performance feature labels, and surface material feature labels, the initial set of structural units is merged and clustered to obtain multiple BIM free structural units.

[0125] Furthermore, the resistance index analysis module 13 is also used for:

[0126] By analyzing the subsets of the multiple sets of support defect simulation data, the initial node resistance index of each BIM free structural unit is obtained;

[0127] Construct a failure propagation network based on the multiple BIM free structural units as nodes;

[0128] The failure propagation network is analyzed based on the subset of multiple sets of support defect simulation data. The weighted edges of the failure propagation network are established based on the influence relationship of failure propagation to obtain the failure propagation weighted network.

[0129] The initial node resistance index of each BIM free structural unit is updated according to the failure propagation weight network to obtain the support resistance index of each BIM free structural unit.

[0130] Furthermore, the optimization strategy execution module 14 is also used for:

[0131] Each strategy in the candidate optimization design strategy library corresponds to a surface treatment parameter of a free structural unit.

[0132] Traverse the multiple BIM free structural units and extract the identified BIM free structural units that are less than the preset support resistance index threshold.

[0133] With the preset support resistance index threshold as the target, the surface treatment parameters of the identified BIM free structural unit are optimized from the candidate optimization design strategy library to obtain the identification optimization design strategy corresponding to each identified BIM free structural unit.

[0134] Furthermore, the optimization strategy execution module 14 is also used for:

[0135] According to the bearing location, obtain the key BIM free structural units in the identified BIM free structural units;

[0136] After obtaining the identification optimization design strategy corresponding to the key BIM free structural unit, consistency constraints are constructed to apply consistency constraints to the candidate optimization design strategies of the remaining identification BIM free structural units.

[0137] Furthermore, the optimization strategy execution module 14 is also used for:

[0138] Determine whether the environmental exposure attributes of the remaining BIM free structural units are consistent with those of the key BIM free structural units;

[0139] If consistent, the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units are constrained for consistency using the first constraint error threshold.

[0140] If there is a discrepancy, the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units are constrained for consistency using a second constraint error threshold, wherein the first constraint error threshold is less than the second constraint error threshold.

[0141] Furthermore, the pollution monitoring data sample is obtained by reading historical pollution monitoring data from a group of analogous construction projects to the target construction project;

[0142] Wherein, the similarity between the construction type, construction equipment, and construction environment of the analogous construction project group and the target construction project is greater than a preset threshold.

[0143] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0144] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0148] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0149] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A BIM-based optimization design method for foundation pit support, characterized in that, The method includes: Construct a BIM foundation pit support model, decompose the support structure of the BIM foundation pit support model, and obtain multiple BIM free structural elements and the surface attribute parameters of each BIM free structural element. Construct a candidate optimization design strategy library for each of the multiple BIM free structural elements; Collect pollution monitoring data samples of the construction project during the construction period, and perform support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples to obtain the support resistance index calculation results. Based on the calculation results of the support resistance index, the identification optimization design strategy for maximizing the support resistance index of each BIM free structural unit is obtained from the candidate optimization design strategy library, multiple identification optimization design strategies are generated, and the BIM foundation pit support model is optimized according to the multiple identification optimization design strategies.

2. The method as described in claim 1, characterized in that, Based on the pollution monitoring data samples, the surface property parameters of each BIM free structural unit are analyzed to determine the support resistance index. The method includes: Identify multiple environmental exposure attributes corresponding to the multiple BIM free structural units; Divide the pollution monitoring data samples to obtain multiple sets of time-series pollution dynamic prediction data subsets corresponding to the multiple environmental exposure attributes; Based on the aforementioned subsets of dynamic pollution prediction time series data and the surface attribute parameters of each BIM free structural unit, support defect simulation is performed to obtain multiple subsets of support defect simulation data. By analyzing the subsets of the multiple sets of support defect simulation data, the support resistance index of each BIM free structural unit is obtained.

3. The method as described in claim 2, characterized in that, The BIM foundation pit support model includes multiple simulation sub-models, including a pollution deposition simulation sub-model, a material degradation simulation sub-model, and a bearing capacity simulation sub-model. Using the multiple simulation sub-models, support defects are simulated according to the multiple sets of pollution dynamic prediction time series data subsets and the surface attribute parameters of each BIM free structural unit. The obtained surface pollution quantitative simulation data, surface coating corrosion defect simulation data, and structural mechanical bearing capacity degradation simulation data are output as a set of support defect simulation data subsets.

4. The method as described in claim 1, characterized in that, The method further includes decomposing the BIM foundation pit support model into support structure to obtain multiple BIM free structural elements. The connection points of the BIM foundation pit support model are decomposed to obtain an initial set of structural units; Identify the static mechanical property characteristic tags, dynamic mechanical property characteristic tags, and surface material characteristic tags of each initial structural unit in the initial structural unit set; Based on the static mechanical performance feature labels, dynamic mechanical performance feature labels, and surface material feature labels, the initial set of structural units is merged and clustered to obtain multiple BIM free structural units.

5. The method as described in claim 3, characterized in that, By analyzing subsets of the multiple sets of simulated support defects, the support resistance index of each BIM free structural unit is obtained. The method includes: By analyzing the subsets of the multiple sets of support defect simulation data, the initial node resistance index of each BIM free structural unit is obtained; Construct a failure propagation network based on the multiple BIM free structural units as nodes; The failure propagation network is analyzed based on the subset of multiple sets of support defect simulation data. The weighted edges of the failure propagation network are established based on the influence relationship of failure propagation to obtain the failure propagation weighted network. The initial node resistance index of each BIM free structural unit is updated according to the failure propagation weight network to obtain the support resistance index of each BIM free structural unit.

6. The method as described in claim 5, characterized in that, The method involves obtaining the identifier optimization design strategy that maximizes the support resistance index for each BIM free structural unit from the candidate optimization design strategy library, and generating multiple identifier optimization design strategies. Each strategy in the candidate optimization design strategy library corresponds to a surface treatment parameter of a free structural unit. Traverse the multiple BIM free structural units and extract the identified BIM free structural units that are less than the preset support resistance index threshold. With the preset support resistance index threshold as the target, the surface treatment parameters of the identified BIM free structural unit are optimized from the candidate optimization design strategy library to obtain the identification optimization design strategy corresponding to each identified BIM free structural unit.

7. The method as described in claim 6, characterized in that, The method further includes optimizing the surface treatment parameters of the identified BIM free structural unit from the candidate optimization design strategy library: According to the bearing location, obtain the key BIM free structural units in the identified BIM free structural units; After obtaining the identification optimization design strategy corresponding to the key BIM free structural unit, consistency constraints are constructed to apply consistency constraints to the candidate optimization design strategies of the remaining identification BIM free structural units.

8. The method as described in claim 7, characterized in that, Before applying consistency constraints to the candidate optimization design strategies for the remaining BIM free structural elements by constructing consistency constraints, the method also includes: Determine whether the environmental exposure attributes of the remaining BIM free structural units are consistent with those of the key BIM free structural units; If consistent, the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units are constrained for consistency using the first constraint error threshold. If there is a discrepancy, the surface treatment parameters of the candidate optimization design strategies for the remaining BIM free structural units are constrained for consistency using a second constraint error threshold, wherein the first constraint error threshold is less than the second constraint error threshold.

9. The method as described in claim 1, characterized in that, The pollution monitoring data sample was obtained by reading historical pollution monitoring data from a group of analogous construction projects to the target construction project. Wherein, the similarity between the construction type, construction equipment, and construction environment of the analogous construction project group and the target construction project is greater than a preset threshold.

10. A BIM-based optimized design system for foundation pit support, characterized in that, The system for implementing the method as described in any one of claims 1 to 9, the system comprising: The model construction and splitting module is used to construct a BIM foundation pit support model, split the support structure of the BIM foundation pit support model, and obtain multiple BIM free structural elements and the surface attribute parameters of each BIM free structural element. The strategy library construction module is used to construct a candidate optimization design strategy library for each BIM free structural unit among the multiple BIM free structural units; The resistance index analysis module is used to collect pollution monitoring data samples of the construction project during the construction period, and to perform support resistance index analysis on the surface attribute parameters of each BIM free structural unit based on the pollution monitoring data samples, and obtain the support resistance index calculation results. The optimization strategy execution module is used to obtain the identification optimization design strategy that maximizes the support resistance index for each BIM free structural unit from the candidate optimization design strategy library according to the support resistance index calculation results, generate multiple identification optimization design strategies, and optimize the BIM foundation pit support model according to the multiple identification optimization design strategies.

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