Bridge abutment back backfilling scheme optimization method and system

Abstract

The invention discloses a bridge abutment back backfilling scheme optimization method and system, and relates to the field of digital engineering scheme optimization. A bridge abutment back backfilling scheme optimization system comprises a data acquisition module, a modeling and dividing module, a frost heaving prediction module, a mechanical analysis module, a scheme generation module, a simulation analysis module, an optimization module, a scheduling generation module and a BIM management module. According to the method, the meteorological and geological coupling prediction model is constructed, and meteorological information and geological information are fused, so that quantitative prediction of the freezing depth and frost heaving risk grade identification are realized, and adaptability and foresight of backfill design of a climate-sensitive area are remarkably improved; and through simulation analysis of structural stress and deformation, in combination with load conditions of the backfill area and foundation response characteristics, a mechanical adaptation grade is generated, and it is ensured that the candidate backfill scheme meets the structural safety requirement.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of digital engineering scheme optimization, in particular to a bridge abutment backfill scheme optimization method and system. BACKGROUND

[0002] Bridge abutment backfill is an important transition area between bridge substructure and roadbed, and its construction quality is directly related to the long-term stability of the abutment, the structural stress transfer efficiency and the development trend of joint diseases (such as bumping and differential settlement). Especially in high-cold or cold-temperate regions where seasonal freeze-thaw cycles are frequent, the abutment backfill area is easily affected by frost heaving stress and water-heat migration effect, inducing structural deformation, filler loosening and local cavities, which can seriously affect the bearing capacity of the abutment and driving safety.

[0003] Traditional bridge abutment backfill scheme optimization relies on experience-based material selection and uniform construction path, and the abutment area lacks fine division, making it difficult to identify risk levels and differentiate materials according to local conditions. The lack of multi-source data coupling and prediction in frost heaving response assessment results in poor sensitivity of material configuration and construction scheme to climate change. Lack of simulation analysis of backfill scheme in digital environment makes it difficult to quantitatively compare and reconstruct the boundaries of multiple candidate schemes. There is a lack of backfill repair scheme optimization method for the backfill abnormalities of the abutment area of the built bridge.

[0004] Therefore, there is an urgent need for a digital optimization method for bridge abutment backfill scheme that integrates multi-source data driving, supports regional risk identification, scheme generation and simulation evaluation, and adapts to permafrost regions, to realize intelligent and quantitative support for the abutment backfill construction or repair work of new and existing bridges. SUMMARY

[0005] The present application proposes a bridge abutment backfill scheme optimization method and system, which integrates three-dimensional modeling, frost heaving prediction, structural analysis, material optimization and simulation optimization modules, breaks through the technical limitations of existing construction experience guidance and overall processing, and realizes the fine, data-driven and intelligent decision support of bridge abutment backfill scheme.

[0006] A bridge abutment backfill scheme optimization method, comprising: Collecting topographic information and geological information of the bridge abutment backfill area; Obtaining design information of the bridge abutment, and constructing a three-dimensional model of the abutment and abutment area in combination with the topographic information, automatically dividing the foundation backfill area, and generating a BIM model of the bridge abutment backfill; For each foundation backfill area in the BIM model, obtain meteorological information related to frost heaving, and input it into a meteorological-geological coupled prediction model, calculate the frost depth prediction value in combination with the geological information, and output the corresponding frost heaving risk level; Based on the frost heaving risk level of each foundation backfill area, combined with the load condition and foundation response characteristics in the design information, the structural stress and deformation are simulated and analyzed to generate the mechanical adaptation level of each foundation backfill area; Based on the frost heaving risk level and the mechanical adaptation level, the material performance database is called to generate multiple candidate backfill schemes for each foundation backfill area, each scheme including backfill depth, backfill material combination and proportion, and output corresponding material types, layer thickness and compaction degree parameters; Each candidate backfill scheme for each foundation backfill area is simulated and analyzed to evaluate the performance indicators, and based on the preset optimization objective function, the final backfill scheme for each foundation backfill area is selected, and the boundary of the foundation backfill area is adjusted and recombined based on the simulation results to generate an optimized construction area division scheme; Based on the construction area division scheme and the final backfill scheme, an optimized construction scheme including operation sequence, operation path and resource allocation is generated.

[0007] As a preferred technical solution of the present application, the automatic division of the foundation backfill area comprises: Based on the three-dimensional model of the abutment and the abutment back area, the structural boundary features, slope ratio, elevation distribution and relative position relationship information with the abutment are extracted, and the three-dimensional space is divided into multiple foundation backfill areas according to the preset partition rule, the partition rule including slope threshold, layered elevation, structure support area and non-support area division logic, and the automatic division result is written into the attribute field of the corresponding component in the BIM model.

[0008] As a preferred technical solution of the present application, the meteorological-geological coupling prediction model comprises: An input module for receiving meteorological information and geological information corresponding to each foundation backfill area; A feature construction module for input data normalization, time series expansion and feature encoding; A prediction module based on a random forest regression model, for establishing a nonlinear mapping relationship between input data and frozen depth prediction value, and calculating the frozen depth prediction value; A training module for supervised training of the prediction module using historical measured frozen depth data, and outputting optimal model weight parameters.

[0009] As a preferred technical solution of the present application, the calculation of the frozen depth prediction value comprises: For each foundation backfill area, a multi-dimensional input vector containing meteorological information and geological information is constructed; the meteorological information includes surface daily average temperature sequence, frozen duration, snow thickness and temperature fluctuation amplitude, and the geological information includes underground temperature profile, soil type, soil moisture content and regional permafrost distribution type; the multi-dimensional input vector is mapped into the trained random forest regression model through the prediction module, and the local frozen depth prediction value is output according to the division path of each decision tree, and the frozen depth prediction value of the area is generated through the weighted average of all trees.

[0010] As a preferred technical solution of the present application, the simulation analysis of structural stress and deformation includes: A multi-layer backfill structure foundation coupling model is constructed by using the finite element method, and corresponding frost heaving boundary conditions and load boundary conditions are set based on the frost heaving risk level, load condition and foundation response characteristics of each foundation backfill area; the vertical deformation, equivalent stress distribution and contact pressure response of the foundation backfill area under the action of frost heaving displacement are simulated and calculated, the response values are compared with the preset safety threshold, the structural safety level interval is determined, and the corresponding mechanical adaptation level is generated.

[0011] As a preferred technical solution of the present application, the simulation simulation includes: The corresponding candidate backfill scheme of each foundation backfill area is written into the temporary attribute field of the corresponding component in the BIM model, and the spatial position, stratum condition, boundary constraint and material parameter of the component are extracted to construct a structured simulation input data set; the simulation input data set is imported into the finite element simulation platform, and based on the frost heaving loading condition and the structural load condition, the settlement response analysis, lateral stability analysis and frozen boundary conduction analysis are carried out on each candidate backfill scheme to obtain the response value of the candidate backfill scheme under the three types of performance indicators.

[0012] As a preferred technical solution of the present application, the generation of the optimized construction area division scheme includes: Based on the performance indicators, a multi-objective weighted optimization function is constructed with performance balance and material coordination as the weight basis, and the final backfill scheme is selected from the candidate backfill schemes; according to the final backfill scheme results of each foundation backfill area, the spatial continuity is analyzed, the boundary adjustment and merging reorganization of adjacent areas are carried out based on the set area reorganization rules, and the optimized construction area division scheme meeting the construction continuity, material uniformity and risk controllability is output, and the scheme is written into the attribute field of the corresponding component in the BIM model.

[0013] As a preferred technical solution of the present application, a bridge abutment backfill scheme optimization method further includes: For the bridge that has been built and the abutment anomaly, the structure measured information is obtained by three-dimensional laser scanning and geological radar mode, the design information is replaced as the input basis for constructing the BIM model, and based on the backfill structure integrity, cavity distribution and compaction state, the initial state information of the foundation backfill area is defined; the corresponding frost heaving risk level, mechanical adaptation level and candidate backfill scheme are obtained by considering the physical performance damage of the original backfill material and the structure boundary uncertainty, by simulating the replacement reconstruction or superposition enhancement two kinds of schemes for comparison simulation, and the corresponding repair type backfill scheme and construction area adjustment scheme are generated.

[0014] A bridge abutment backfill scheme optimization system, comprising: A data acquisition module is configured to acquire topographic information, geological information and meteorological information of a bridge abutment backfill area, and to obtain design information or measured structure information of the bridge abutment; A modeling and division module is configured to construct a three-dimensional model of a bridge abutment and abutment area, automatically divide a foundation backfill area, and generate a BIM model of the bridge abutment backfill; A frost heaving prediction module is configured to calculate a frost heaving depth prediction value for each foundation backfill area based on a meteorological-geological coupling prediction model, and output a frost heaving risk level; A mechanical analysis module is configured to perform simulation analysis of structure stress and deformation, and generate a mechanical adaptation level for each foundation backfill area; A scheme generation module is configured to generate a candidate backfill scheme by calling a material performance database according to the frost heaving risk level and the mechanical adaptation level; A simulation analysis module is configured to simulate the candidate backfill scheme and output performance indicators; An optimization module is configured to select a final backfill scheme based on a preset optimization objective function, recombine the boundaries of the foundation backfill area, and output a construction area division scheme; A scheduling generation module is configured to generate an optimized construction scheme based on the construction area division scheme and the final backfill scheme; A BIM management module is configured to manage the backfill scheme optimization process based on the BIM model.

[0015] The present application has the following advantages: The present application automatically divides the foundation backfill area of the bridge abutment and abutment area, and writes the structure boundary, elevation information and spatial characteristics into the BIM model, realizes the fine management and component-level data expression of the abutment backfill area, and provides a basis for subsequent differential analysis and optimization; by compatible abutment repair process of existing bridge, introducing three-dimensional laser scanning and geological radar data to construct BIM model, and generating repair type backfill scheme under the condition of material damage and structure uncertainty, the application range and engineering scene of the method are expanded.

[0016] The application realizes quantitative prediction of the freezing depth and identification of the frost heaving risk level by constructing a meteorological-geological coupling prediction model, fusing meteorological information and geological information, and significantly improves the adaptability and foresight of the backfill design in climate-sensitive areas; and the mechanical adaptation level is generated by simulating and analyzing the structure stress and deformation in combination with the load conditions and foundation response characteristics of the backfill area, so that the candidate backfill scheme meets the structural safety requirements.

[0017] The application realizes quantitative comparison and optimization in multiple dimensions such as settlement control, structural stability and freezing response by constructing a candidate backfill scheme database and combining performance evaluation with simulation, enhances the engineering adaptability and cost rationality of material configuration, and generates an optimized construction area division scheme that meets the construction continuity, material uniformity and construction feasibility by boundary recombination and regional merging of the foundation backfill area based on the optimization objective function, thereby improving the overall backfill quality and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only schematic diagrams of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without any creative effort; Figure 1 A structural schematic diagram of a bridge abutment backfill scheme optimization system used in the embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort are within the scope of protection of the present application.

[0020] Embodiment 1, a bridge abutment backfill scheme optimization method, comprising the following steps: Step S1: collecting topographic information and geological information of the bridge abutment backfill area; obtaining the design information of the bridge abutment, and constructing a three-dimensional model of the abutment and abutment area in combination with the topographic information, automatically dividing the foundation backfill area, and generating a BIM model of the bridge abutment backfill; Topographic information: obtain the bridge surrounding topographic data through unmanned aerial vehicle oblique photogrammetry, laser radar (LiDAR) scanning or three-dimensional laser point cloud, obtain a high-precision digital terrain model (DTM), and provide geometric attribute information including at least regional slope, slope direction and elevation distribution.

[0021] Geological information: Extracted from drilling, in-situ testing, geological radar and other means combined with existing geological survey reports, at least including soil type, stratified structure, groundwater depth, water content, permafrost distribution type and underground temperature profile in the abutment area. This information will be an important input variable for subsequent frost heaving risk prediction and backfill material adaptation.

[0022] For example: For a highway bridge located in a cold region, the soil layer in the abutment area is from top to bottom: fill, silty clay and strongly weathered rock. Through underground temperature monitoring data, the average freezing depth in winter is 1.3m, and the groundwater depth is 2.1m all year round.

[0023] Design information: Extracted from design documents, at least including bridge abutment structure type (gravity type, ribbed slab type or expanded foundation type), abutment structure size, design elevation, support location, elevation difference and load distribution characteristics, used to guide parameterized modeling of BIM components and setting of regional division rules.

[0024] Based on the modeling platform (such as Revit), a three-dimensional geometric model of the abutment and abutment area is constructed, and the geological information is attached to the corresponding stratum component in the form of attributes, generating a BIM basic component model with attribute information.

[0025] The foundation backfill area refers to multiple independent analysis units between the abutment structure and the surrounding soil, which are divided according to geometric position, structural function and geological characteristics, etc. It is used to realize regionalized risk assessment and differentiated scheme configuration.

[0026] The automatic division of the foundation backfill area includes: Based on the three-dimensional model of the abutment and abutment area, the structural boundary characteristics (slope direction), slope ratio (source: DTM model calculation), elevation distribution (from laser scanning or photogrammetry) and relative position relationship information with the abutment (such as the intersection of the abutment back and the slope) are extracted, and the three-dimensional space is divided into multiple foundation backfill areas according to the preset division rules, which include slope threshold (regions with slope greater than 1:1.5 need to be separately divided into high-risk areas), stratified elevation (according to every 0.5m or 1m to divide backfill layer), structure support area and non-support area division logic (to divide direct bearing area and free backfill area with abutment bottom plate, wing wall or support pile as boundary), and the automatic division result is written into the attribute field of the corresponding component in the BIM model.

[0027] The automatic division result is written into the attribute field of the corresponding component in the BIM model through script tool (such as Dynamo), at least including area number, stratified layer, adjacent structure component identification, whether in the structure key influence area.

[0028] Example: In the abutment area of a continuous rigid frame bridge, the model is divided into three types of regions according to the zoning rules: structural support area A1, structural free backfill area B1, and high-risk slope area C1. Each region has a unique number and attribute identification.

[0029] The data formed in this step will flow into the following steps through the following paths: the basic backfill area extracted from the three-dimensional model and its attribute field will be input into the meteorological-geological coupling prediction model for freeze depth prediction; the spatial position and layering information in the same model will be input into the finite element simulation platform for structural response analysis; all division results will be uniformly visualized through the BIM platform, supporting attribute-based query, filtering, and dynamic updating.

[0030] Step S2: For each foundation backfill area in the BIM model, obtain the meteorological information related to frost heaving and input it into the meteorological-geological coupling prediction model. Combine the geological information to calculate the freeze depth prediction value and output the corresponding frost heaving risk level. Meteorological information: obtained through historical meteorological station data or remote sensing climate models, at least including surface daily temperature sequence, used to construct the thermal conduction boundary condition during freezing; freeze duration, the number of consecutive days with air temperature below 0℃; snow thickness, snow layer as a heat insulation layer affects the freezing depth; air temperature fluctuation amplitude, measures the disturbance degree of temperature repeated freezing and thawing to the soil.

[0031] For example: In the abutment area of a bridge in the northeast region, the average freeze duration in winter 2021 is 84 days, the maximum single-day snow thickness is 26 cm, and the air temperature fluctuation amplitude reaches ±12℃.

[0032] Geological information used by the model includes: underground temperature profile, reflecting the initial thermal field distribution at different depths; soil type, such as clay, silt, and sand, with obvious differences in thermal conductivity and frost heaving coefficient; soil moisture content, the more water, the greater the freezing potential; regional permafrost distribution type, such as seasonal frozen soil, perennial frozen soil, or non-frozen soil area.

[0033] The meteorological-geological coupling prediction model includes the following modules: Input module, used to receive the combined data of meteorological information and geological information corresponding to each foundation backfill area, and uniformly encode to form a structured input vector; Feature construction module, used to process input data as follows: Normalization processing: used to eliminate dimension differences (such as different units of temperature and thickness); Time series expansion: convert daily temperature sequence into statistical features (maximum value, mean value, trend item); Feature encoding: convert categorical variables (such as soil type) into one-hot encoding or embedding vectors.

[0034] A prediction module that establishes a nonlinear mapping relationship between the frost heave-related variables and the predicted value of the frost depth based on a random forest regression model. The random forest model is an ensemble learning method composed of multiple regression trees, which has good anti-overfitting ability and feature selection ability. The model output is the predicted value of the frost depth for each area (unit: meters); A training module that uses historical measured data of the frost depth in existing areas for supervised learning. During the training process: the measured frost depth is used as the supervisory variable; an optimization problem is constructed with mean square error (MSE) as the objective function; feature importance ranking and splitting strategy optimization are performed on all tree nodes during the training process; the model performance control target is to have an average relative error of less than 10%.

[0035] The calculation of the predicted value of the frost depth includes: For each basic backfill area, a multidimensional input vector containing meteorological information and geological information is constructed, with the following dimension structure: Meteorological information: {daily average temperature sequence mean: -6.4°C, freezing duration: 72 days, maximum snow thickness: 24 cm, maximum daily temperature fluctuation: ±10°C}; Geological information: {soil type (one-hot encoding): clay, underground temperature gradient: -0.4°C / m, water content: 18%, frozen soil type: short-season frozen soil (annual freezing time is more than 2 months and less than 4 months)}; The input vector is fed into the trained random forest regression model, and each regression tree outputs a sub-predicted value of the frost depth according to its division path. The final predicted value of the frost depth is the weighted average of all tree outputs.

[0036] The frost heave risk level refers to the possibility and impact of the bridge abutment area under existing geological and meteorological conditions to undergo frost heave deformation, which is divided into three levels: high risk, medium risk, and low risk.

[0037] The determination method: the predicted value of the frost depth is jointly analyzed with factors such as the design backfill layer thickness, groundwater level, and soil type; empirical weights or expert systems are used to define grading rules, for example: if the frost depth is greater than 75% of the backfill thickness and the water content is greater than 15%, it is defined as high risk; if the frost depth is between 50% and 75% of the backfill thickness, it is medium risk; and if it is less than 50%, it is low risk.

[0038] The calculation results of the frost heave risk level are written into the attribute field of the corresponding basic backfill area in the BIM model for subsequent structure simulation and material matching.

[0039] The data flow and processing path in this step are as follows: geological and meteorological information is imported into the coupled prediction model through the interface; after feature construction, a standardized input vector is formed; the input vector is calculated through the random forest model, and the predicted value of the frost depth is output; compared with the design backfill layer thickness, etc., the risk level is calculated; the risk level value is written into the attribute field of the corresponding area in the BIM model (such as Risk_Frost_Level=High); the output result will enter the structure response simulation in the next stage, which is used to set the boundary conditions and structure adaptation parameters.

[0040] Step S3: Based on the frost heaving risk level of each foundation backfill area, combined with the load conditions and foundation response characteristics in the design information, the simulation analysis of structure stress and deformation is carried out, and the mechanical adaptation level of each foundation backfill area is generated. Frost heaving risk level (input parameter): the calculation result from step S2, used to set the frost heaving boundary condition in simulation. The risk level affects the boundary parameters such as the frost depth, frost heaving displacement load and frost heaving duration in simulation.

[0041] Load condition, structure load input parameter extracted from design information, including: dead load, self weight, weight of backfill; live load, vehicle load, passing load; special load (such as snow load, impact load); working condition combination, the most unfavorable combination defined in design specification is input into simulation.

[0042] Foundation response characteristics, constitutive relationship and parameters of foundation soil, including: modulus (compression modulus, deformation modulus); Poisson's ratio; shear strength parameters (internal friction angle, cohesion); frost heaving deformation modulus (used for material response simulation under frozen conditions); foundation bearing capacity characteristic value.

[0043] Mechanical adaptation level refers to the adaptation degree of the foundation backfill area to the frost heaving influence under structure simulation, which is divided into three levels: good adaptation, medium adaptation and poor adaptation. The level evaluation is based on the comparison results of structure response index and safety standard.

[0044] The simulation analysis of structure stress and deformation includes: A multi-layer backfill structure foundation coupled model is constructed by using finite element method. Each foundation backfill area is modeled as an independent sub-domain; the model includes: upper structure rigid body or semi-rigid structure (abutment); multi-layer backfill body (divided according to material type and compaction degree); foundation soil (layered soil, saturation distribution); equivalent parameters are used to convert the frost heaving influence into initial strain or displacement load.

[0045] Set boundary conditions, including frost heaving boundary conditions and load boundary conditions; Frost heave boundary condition: frost depth from frost heave risk level determination; vertical frost heave displacement load (calculated according to frost heave coefficient x frost depth); horizontal constraint boundary or sliding boundary is set according to the site conditions.

[0046] Example: When a certain area is high risk level, the frost depth is 1.2m, and the frost heave coefficient is 2%, then 24mm upward initial frost heave displacement is applied to the corresponding node in the simulation.

[0047] Load boundary condition: dead load is applied to the top surface of the abutment; live load is loaded according to the node distribution of vehicle load standard (such as highway-I load); support condition is set as fixed or elastic support according to foundation type (rigid foundation, pile foundation).

[0048] Through simulation calculation, the response of each foundation backfill area under the action of frost heave displacement is as follows: vertical deformation, vertical displacement of simulation node under the action of frost loading; equivalent stress distribution, equivalent stress field in soil, reflecting the adaptability of structure; contact pressure response, the reaction force distribution between the abutment bottom or backfill layers; safety margin factor, compare the calculated value with the design allowable value, output whether the structure is in a safe state.

[0049] All indicators are compared with the safety threshold value defined by design specification or project; For example: vertical differential settlement <15mm; contact stress does not exceed 60% of the compressive strength of soil; maximum displacement difference between backfill layers <10mm.

[0050] Mechanical adaptation level definition: measure the response ability of structure and foundation in a certain area under the action of frost heave deformation, and indicate whether it is suitable to maintain the existing structure function and stability under the current design parameter condition.

[0051] The determination method is that when all the structure response indicators are less than the threshold value and have a margin of >20%, it is determined to be well adapted; if some indicators are close to the limit value (margin <10%), it is determined to be moderately adapted; if it exceeds the threshold value or there is obvious differential settlement, it is determined to be poorly adapted.

[0052] The adaptation level is written into the attribute field of the foundation backfill area component in the BIM model as a label, which is used to guide the matching of backfill material and scheme in the next stage.

[0053] The data input and output paths of this step are as follows: input data, frost heaving risk level from step S2; geometric and load parameters from step S1 and design file; foundation response parameters from geological survey; processing, building a finite element model and setting boundary conditions; performing simulation for each region, outputting multiple structural response indicators; comparing with preset standards, outputting safety judgment results; output data, mechanical adaptation level (for S4 candidate scheme matching); all structural response values can be optionally written into BIM attribute fields or exported to simulation reports; model results are visualized to present structural weak areas and optimization potential areas.

[0054] Step S4: Based on the frost heaving risk level and the mechanical adaptation level, call the material performance database to generate multiple candidate backfill schemes for each foundation backfill area, each scheme including backfill depth, backfill material combination and proportion, output corresponding material types, layer thickness and compaction degree parameters; The candidate backfill scheme refers to a set of implementable backfill designs selected from the material performance database according to the frost heaving risk level and structural adaptation ability of a certain foundation backfill area. Each scheme contains the following parameters: backfill layer number and total depth; material type of each layer; material proportion (such as particle size ratio, cement content); layer thickness; compaction degree target value.

[0055] Material performance database (data structure), the database stores the engineering physical properties and construction parameters of various backfill materials in a structured form, the main fields include (as shown in the table below):

[0056] The data sources include laboratory material testing, engineering experience accumulation, standardized material manual and previous engineering measured data.

[0057] The candidate scheme generation logic and process includes: Obtain input variables, including frost heaving risk level from S2; mechanical adaptation level from S3; spatial location, layered structure and design load level from BIM model.

[0058] Set the screening and matching rules: for high frost heaving risk + poor adaptation area, preferentially select lightweight materials with strong anti-frost heaving ability (low frost heaving coefficient) (such as foam mixed soil, clay-gravel composite layer); for medium risk + moderate adaptation area, you can select combined schemes, such as coarse-grained bottom layer + fine-grained upper layer; for low risk + good adaptation area, you can use conventional fillers, such as compacted loess or natural graded sand.

[0059] Generate several optional schemes through a rule-driven matching engine or rule base (such as based on expert system); generate at least 2-3 technically feasible, economically differentiated combined schemes for each region; the generated scheme data is bound and written into the temporary attribute fields of BIM components.

[0060] Example: The following candidate schemes are generated for a certain frost heaving risk area: Scheme A: Upper layer of lime soil (15 cm) + middle layer of lime stabilized sand (25 cm) + lower layer of gravel (40 cm); compaction degree 92%; Scheme B: Foam light soil overall backfill (1.0 m); compaction degree 85%, higher unit cost but small deformation; Scheme C: Sand + geotextile layered combination, compaction degree 90%, good material synergy.

[0061] Parameter output content description: Each candidate scheme outputs the following technical parameters and is written into the BIM model as a data field, material type, component attribute field represents each layer of material; layer thickness (Layer_Thickness), millimeter or centimeter unit; compaction degree (Compaction_Ratio), target compaction ratio, for example 95%; backfill depth (Backfill_Depth), total depth of the area; material combination number and scheme ID, used for simulation call and version control; material performance score (optional), freeze sensitivity score, construction convenience score, etc.

[0062] The generation of candidate backfill schemes forms the following data flow: input, geometric properties such as area location, layered thickness in BIM model; frost heaving grade and adaptation grade output by S2 / S3; processing, conditional matching in material performance database; automatically combined to generate schemes based on rule base; output, 2-3 groups of candidate schemes generated for each area; all schemes written into BIM model attributes; data synchronization output to simulation platform as input for next stage performance evaluation.

[0063] Step S5: Simulate each candidate backfill scheme for each foundation backfill area, evaluate performance indicators, select the final backfill scheme for each foundation backfill area based on the preset optimization objective function, and combine the simulation results to adjust and recombine the boundaries of the foundation backfill area, generating an optimized construction area division scheme; Candidate backfill scheme simulation: For each scheme of each foundation backfill area, construct its finite element model under actual frost heaving and load, evaluate its performance indicators such as settlement, stability and heat conduction, and quantify its engineering response.

[0064] Optimization objective function: used to evaluate multiple-dimensional performance indicators of multiple schemes, sort and select the results by weighting. The objective function includes: settlement performance indicators (maximum settlement); stability indicators (slope safety factor); degree of influence of frozen boundary (heat conduction path); material coordination and balance; economic parameters (optional).

[0065] Boundary adjustment and regional reorganization: Based on the spatial continuity and material consistency of the performance of each region in the simulation results, the backfill construction boundary is redefined to achieve construction continuity, process simplification, and material centralized configuration.

[0066] The simulation includes: Write the candidate backfill scheme corresponding to each basic backfill region into the temporary attribute field of the corresponding component in the BIM model, and extract the spatial position (region boundary, coordinate position, layer thickness, from the BIM model), stratum condition (soil layer distribution, foundation parameters, groundwater level, from geological survey data), boundary constraint (live load, dead load combination, frost heaving loading boundary, from design information, S2 results) and material parameters (material type, compaction degree, physical parameters of each layer, from material performance database) of the component to construct a structured simulation input data set. For example: Candidate scheme B includes a combination of foam light soil upper layer + gravel base layer, and its material parameters (such as modulus 30 MPa, thermal conductivity 0.42 W / m·K) are provided by the material database. The backfill thickness is 80 cm, and the frost heaving displacement is calculated by the freezing depth multiplied by the frost heaving coefficient, and is loaded on the top surface of the simulation model.

[0067] Using finite element simulation tools (such as ABAQUS), the following three types of performance evaluation simulation are performed: Settlement response analysis: Simulate the vertical deformation behavior of the foundation backfill area after frost heaving loading, output the maximum settlement and uneven settlement difference; Lateral stability analysis: For slope or free-air area, calculate the safety factor and sliding trend; Freezing boundary conduction analysis: Evaluate the influence of different material combinations on temperature field propagation and identify whether it is easy to form a frost heaving concentration area. Each backfill scheme outputs corresponding simulation results, which are written in a structured format (such as JSON) to the model or exported.

[0068] The generation of the optimized construction area division scheme includes: Based on the performance indicators, a multi-objective weighted optimization function is constructed with performance balance and material coordination as the weight basis, and the final backfill scheme is selected from the candidate backfill schemes. Where performance balance refers to the specific requirements of the three types of performance evaluation results (such as settlement priority project, then set the settlement performance indicator to be larger).

[0069] Material coordination refers to the consistency degree of material types, compaction standards, and process flow between different backfill regions. The higher the coordination, the simpler the construction organization, and the higher the material scheduling efficiency. For example: If A and B regions both use a double-layer structure of clay + sand cushion, with a compaction degree of 92%, it is considered as high coordination; if the material types differ greatly, the coordination is low.

[0070] The optimization target function value is calculated for each regional candidate scheme, and the optimal scheme is selected as the final backfilling scheme, written into the BIM model main attribute field, and called by the next construction scheme.

[0071] The generated optimized construction region division scheme includes: The regional boundary adjustment is performed based on the following factors, including spatial continuity, no structural division between adjacent regions, and little difference in geology; material uniformity, similar material types and compaction parameters in the final scheme; construction convenience, preferentially combining regions without affecting the operation order and process. The regional merging method includes automatic merging, based on BIM attribute analysis of the similarity of adjacent regions; semi-automatic adjustment, handed over to engineers to confirm whether to merge; manual splitting, if local anomalies are found in the simulation results, reverse division can be performed on large regions. The final output is an optimized construction region division scheme, written into the BIM model component attribute field, and used for subsequent construction simulation, scheduling, and resource allocation.

[0072] The data flow path of step S5 is as follows: input, candidate backfilling scheme data from S4; BIM geometry and attribute model; geological, structural, and load information extracted in S1-S3; processing, batch generation of simulation models; extraction of result indicators; construction of and calculation of optimization target function; output, final backfilling scheme for each region; simulation score report; optimized regional division boundary (spatial data + attribute field); visual map or IFC partition result.

[0073] Step S6: Based on the construction region division scheme and the final backfilling scheme, an optimized construction scheme is generated, including operation order, operation path, and resource allocation.

[0074] Optimized construction scheme: refers to a construction organization scheme that has implementability and operability, based on regional division results and final backfilling design, and considering the coordination of construction order, equipment path, material transportation, compaction process, and resource organization. This scheme is not equivalent to traditional construction drawings or processes, but is highly dependent on model-driven data-based construction scheduling planning results, has structured expression ability, and supports construction simulation, progress simulation, and dynamic updating.

[0075] The data relied on by the optimized construction scheme mainly comes from the following sources: regional division boundary, from the simulation region reorganization result (BIM) in step S5, used for operation unit determination; final backfilling scheme parameters, from the final backfilling scheme output in step S5, used for material and compaction control; operation surface geometry, from the spatial position and hierarchical relationship of BIM components, used for path generation and equipment layout; material properties and loading requirements, from the material performance database, used for transportation and storage organization; site resource list, from the engineering resource scheduling platform (external system interface), used for matching available equipment, manpower, and materials.

[0076] The operation sequence refers to the sequential arrangement of backfill construction in each area or layer during the construction process, which must take into account structural safety, construction technology, material transportation and equipment path continuity. Operation sequence planning principles: give priority to construction of high-risk and critical structure adjacent areas; advance layer by layer from bottom to top based on elevation difference; if there is a compaction dependency sequence (such as the compaction of the upper layer must be based on the stable surface of the lower layer), the process relationship must be set compulsorily. For example: Area A (the back of the abutment is adjacent to the bridge pier) must be operated earlier than Area B (the free backfill area on the outer edge) to prevent construction vehicles from disturbing the unstable area. Output form: structured task scheduling table (Gantt format or node dependency diagram); each area is assigned a construction priority field and written into the BIM model component attributes.

[0077] The work path is the result of dynamic path planning for construction equipment (such as rollers, transport vehicles, and vibratory compactors) at the construction site, emphasizing path accessibility, continuity, and the principle of minimizing construction interference. Path generation logic: A feasible path network is constructed based on the spatial coordinate information and obstacle locations of the BIM model; turning radius and width restrictions for construction equipment are introduced; the optimal work path is planned using A* search, Dijkstra, or genetic algorithms (automatic or semi-automatic methods can be selected depending on the site conditions); and the round-trip paths of the work equipment, the routes of material transport vehicles, and the location of temporary roads are considered. Output format: Path data is written to the associated path field of the GIS layer or BIM component; the output is a path coordinate sequence, a two-dimensional graphic, or an animated simulation trajectory.

[0078] Resource allocation refers to the organizational method of rationally allocating various types of construction resources (people, machines, materials) to different work areas and time periods to ensure work efficiency and quality. Resource types: human resources, construction teams, technical personnel; mechanical resources, rollers, forklifts, sprinklers, compaction equipment; material resources, various types of backfill materials, additives, auxiliary materials; auxiliary resources, water supply, power supply, lighting, traffic facilities, etc. Scheduling principles: match the order of operations to avoid resource conflicts or idleness; when the same materials are used in different areas, priority is given to merging supply batches; high-risk areas are given priority to highly qualified and experienced personnel; path and resource allocation are linked: the longer the path, the higher the transportation capacity needs to be. Resource allocation output form: operation unit-resource allocation table; daily construction plan (daily input of manpower, machines and materials); marking the construction stage status and resource consumption attributes in the BIM model.

[0079] This step establishes the following data processing chain: in the input stage, the construction area boundary and final plan data are imported from step S5; the resource library and construction site layout are obtained from the project management system; in the generation stage, the operation sequence and path are generated through the scheduling algorithm and rule engine; the resource matching module is called to generate the daily resource allocation plan; in the output stage, the construction plan results are written into the BIM model in the form of attributes; and the task list and resource plan are synchronously exported to the construction management system.

[0080] Step S7: For the bridge with built and abnormal abutment, the measured information of the structure is obtained by three-dimensional laser scanning and geological radar, which replaces the design information as the input basis for building the BIM model, and based on the backfill structure integrity, cavity distribution and compaction state, the initial state information of the foundation backfill area is defined; The corresponding frost heaving risk level, mechanical adaptation level and candidate backfill scheme considering the physical performance damage of the original backfill material and the uncertainty of the structure boundary, through simulation and comparison of the two schemes of alternative reconstruction or superposition enhancement, and the corresponding repair type backfill scheme and construction area adjustment scheme are generated.

[0081] Abnormal abutment bridge: refers to the bridge in the operation period, the abutment area appears one of the following problems: the settlement difference is greater than the allowable value of the specification; backfill cavity or collapse appears behind the back wall; frost heaving or bridge head bumping phenomenon; local structure loosening or compaction deficiency is detected. This kind of structure lacks effective original construction records or drawing information, so it is necessary to build subsequent model mainly based on field measured data.

[0082] Structural measured information is obtained by field non-destructive testing and high-precision scanning methods, including: three-dimensional laser scanning (TLS), obtaining the abutment, backfill surface morphology and settlement deformation; geological radar (GPR), used for detecting internal cavity, layer thickness distribution and compaction unevenness of backfill layer; dynamic load response test (optional), to evaluate the response characteristics of the structure under load (such as dynamic deformation modulus); sampling detection, to obtain the residual backfill material for mechanical / thermal performance test.

[0083] BIM model construction logic: three-dimensional laser point cloud is used as the basis for geometric reconstruction, to automatically generate the structure contour; the backfill layer interface is analyzed by geological radar scanning atlas and AI image recognition model; the detection results are attached to the BIM component in the form of attributes, to generate a digital abutment model with historical deformation + current state. Since the original design drawings may be missing or unreliable, the measured data is used to replace the following fields: abutment area size and boundary position; thickness and material type of each backfill layer (partly estimated); compaction condition and integrity label of backfill structure (such as existence of cavity, compaction deficiency); actual deformation field distribution (settlement, uplift, crack, etc.).

[0084] Example: a certain operating bridge is detected by radar to have a 60cm deep, 1.2m long cavity at 2m behind the abutment, with a surrounding compaction degree less than 85%. This area is marked as a local instability area and is preferentially included in the subsequent repair simulation range.

[0085] In the built BIM model, each foundation backfill area will have the following initial state information attribute fields: Layer_Integrity_Status: Structural integrity status (intact / void / delamination); Compaction_Estimate: compaction estimate (derived from radar signal analysis or compaction detection); Material_Damage_Index: Material damage index (laboratory test or empirical model estimation); Freeze_History_Tag: whether it has experienced significant frost heave impact; Uncertainty_Level: Uncertainty level of the structure boundary (high / medium / low).

[0086] This information will serve as input correction factors for the S2 and S3 models and participate in the generation of repair strategies.

[0087] In such existing abnormal bridges, since the original material state is uncontrollable, the models in S2 and S3 need to be adjusted as follows: Correction of damaged material parameters: frost heave coefficient correction, adjusted according to the moisture content increase and microcrack evolution model; modulus reduction factor, reducing the material elastic modulus according to the empirical damage curve (e.g. 30-60%); thermal conductivity change, considering the difference between the void area and the dense area (heat conduction is blocked in the void).

[0088] Interval parameter modeling or Monte Carlo simulation methods are used to introduce boundary condition uncertainty zones: fuzzification of the stratum interface (±10cm tolerance); instability of the load diffusion path; and transformation of the contact stress boundary into a flexible contact model.

[0089] Two types of repair strategies are designed for each unstable area and compared by simulation: Simulate an alternative reconstruction scheme (Scheme Type I), remove all existing materials in the abnormal area, and refill with new high-freeze-resistant materials (such as lightweight foam soil and lime-soil stabilized gravel). The compaction degree is implemented according to the new construction standard. The simulation is set to the ideal reconstruction working condition, and the overall deformation and stress field of the structure are re-simulated.

[0090] The superimposed reinforcement scheme (scheme type II) does not completely remove the original materials; an antifreeze and heat-insulating layer (such as extruded polystyrene board + coarse-grained cushion layer) is added above the abnormal area; local grouting or spraying is used to increase the contact density; the simulation process sets the original material retention + new layer material covering the double layer combination.

[0091] Simulation outputs and comparison metrics include: frost heave control capability; project cost estimates (which can be estimated using a unilateral cost model); and construction period and operational feasibility scores. The outputs will be incorporated into the restoration BIM model for subsequent construction decision-making and on-site scheduling.

[0092] Embodiment 2, a bridge abutment backfill scheme optimization system, see Figure 1 As shown, comprising the following modules: A data acquisition module for acquiring topographic information, geological information and weather information of the bridge abutment backfill area, and obtaining design information or measured structure information of the bridge abutment; A modeling and division module for constructing a three-dimensional model of the abutment and abutment area, automatically dividing the foundation backfill area, and generating a BIM model of the bridge abutment backfill; A frost heaving prediction module for calculating the frost heaving risk level based on the weather-geology coupling prediction model and the calculated frost depth prediction value of each foundation backfill area; A mechanical analysis module for simulating and analyzing the structure stress and deformation to generate the mechanical adaptation level of each foundation backfill area; A scheme generation module for generating candidate backfill schemes by calling the material performance database according to the frost heaving risk level and the mechanical adaptation level; A simulation analysis module for simulating the candidate backfill schemes and outputting performance indicators; An optimization module for selecting the final backfill scheme based on the preset optimization objective function, and performing boundary reorganization on the foundation backfill area to output the construction area division scheme; A scheduling generation module for generating an optimized construction scheme based on the construction area division scheme and the final backfill scheme; A BIM management module for managing the backfill scheme optimization process based on the BIM model.

[0093] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A bridge abutment backfill scheme optimization method, characterized in that: include: Collect topographic and geological information of the backfill area behind the bridge abutment; Obtain the design information of the bridge abutment and build a 3D model of the abutment and abutment area based on the terrain information. Automatically divide the foundation backfill area and generate a BIM model of the bridge abutment backfill. For each foundation backfill area in the BIM model, meteorological information related to frost heave is obtained and input into the meteorological-geological coupling prediction model. The predicted freezing depth is calculated in combination with the geological information, and the corresponding frost heave risk level is output. Based on the frost heave risk level of each foundation backfill area, combined with the load conditions and foundation response characteristics in the design information, a simulation analysis of the structural stress and deformation is performed to generate a mechanical adaptability level for each foundation backfill area; Based on the frost heave risk level and mechanical adaptability level, the material properties database is called to generate multiple candidate backfill schemes for each foundation backfill area. Each scheme includes backfill depth, backfill material combination and ratio, and outputs the corresponding material type, layer thickness and compaction parameters. Simulate each candidate backfill scheme for each foundation backfill area, evaluate performance indicators, select the final backfill scheme for each foundation backfill area based on the preset optimization objective function, and adjust and merge the foundation backfill area boundaries based on the simulation results to generate an optimized construction area division plan; Based on the construction area division plan and the final backfill plan, an optimized construction plan including work sequence, work path and resource allocation is generated.

2. The bridge abutment backfill scheme optimization method according to claim 1 is characterized in that: The automatic division of the foundation backfill area includes: Based on the three-dimensional model of the abutment and back area, the structural boundary characteristics, slope ratio, elevation distribution and relative position relationship information with the abutment are extracted, and the three-dimensional space is divided into multiple foundation backfill areas according to preset zoning rules. The zoning rules include slope threshold, layered elevation, and the logic of dividing the structural support area and non-support area. The results of the automatic division are written into the attribute fields of the corresponding components in the BIM model.

3. The bridge abutment backfill scheme optimization method according to claim 1 is characterized in that: The meteorological-geological coupling prediction model includes: An input module for receiving meteorological and geological information corresponding to each foundation backfill area; Feature construction module, which is used to normalize input data, expand time series and encode features; The prediction module is built based on the random forest regression model and is used to establish a nonlinear mapping relationship between input data and the predicted freezing depth value, and calculate the predicted freezing depth value; The training module is used to perform supervised training on the prediction module using historical measured freezing depth data and output the optimal model weight parameters.

4. The bridge abutment backfill optimization method according to claim 3 is characterized in that: Calculating the predicted freezing depth value includes: For each basic backfill area, a multidimensional input vector containing meteorological and geological information is constructed; the meteorological information includes the average daily surface temperature series, the number of freezing days, the snowfall thickness and the temperature fluctuation amplitude; the geological information includes the underground temperature profile, soil type, soil moisture content and the regional frozen soil distribution type. The multidimensional input vector is mapped to the trained random forest regression model through the prediction module, and the local freezing depth prediction value is output according to the division path of each decision tree. The freezing depth prediction value of the area is generated by the weighted average of all trees.

5. The bridge abutment backfill scheme optimization method according to claim 1 is characterized in that: The simulation analysis of structural stress and deformation includes: The finite element method is used to construct a multi-layer backfill structure foundation coupling model. Based on the frost heave risk level, load conditions and foundation response characteristics of each foundation backfill area, the corresponding frost heave boundary conditions and load boundary conditions are set. The vertical deformation, equivalent stress distribution and contact pressure response of the foundation backfill area under the action of frost heave displacement are calculated through simulation. The response values ​​are compared with the preset safety threshold to determine the structural safety level range and generate the corresponding mechanical adaptation level.

6. The bridge abutment backfill optimization method according to claim 1 is characterized in that: The simulation includes: The candidate backfill schemes corresponding to each foundation backfill area are written into the temporary attribute fields of the corresponding components in the BIM model, and the spatial position, ground conditions, boundary constraints and material parameters of the components are extracted to construct a structured simulation input data set. The simulation input data set is imported into the finite element simulation platform. Based on the frost heave loading conditions and structural load conditions, settlement response analysis, lateral stability analysis and frozen boundary conduction analysis are performed on each candidate backfill scheme to obtain the response values ​​of the candidate backfill schemes under the three types of performance indicators.

7. The bridge abutment backfill optimization method according to claim 1 is characterized in that: The generated optimized construction area division scheme includes: Based on performance indicators, a multi-objective weighted optimization function with performance balance and material coordination as weights is constructed to select the final backfill scheme from the candidate backfill schemes; according to the final backfill scheme results of each basic backfill area, its spatial continuity is analyzed, and the boundaries of adjacent areas are adjusted and merged and reorganized based on the set regional reorganization rules. The optimized construction area division scheme that meets construction continuity, material uniformity and risk controllability is output, and the scheme is written into the attribute field of the corresponding component in the BIM model.

8. The bridge abutment backfill optimization method according to claim 1 is characterized in that: Also includes: For completed bridges with abnormal abutment backs, 3D laser scanning and geological radar are used to obtain measured structural information, which replaces design information as the input for building the BIM model. The initial state information of the foundation backfill area is defined based on the backfill structural integrity, void distribution, and compaction status. The process of obtaining the corresponding frost heave risk level, mechanical adaptation level and candidate backfill schemes takes into account the physical property damage of the original backfill material and the uncertainty of the structural boundary. By simulating alternative reconstruction or superposition enhancement, a comparative simulation is performed to generate corresponding repair backfill schemes and construction area adjustment schemes.

9. A bridge abutment backfill scheme optimization system, characterized in that: The system applies any one of the bridge abutment backfill scheme optimization methods described in claims 1 to 8, including: The data acquisition module is used to collect topographic information, geological information, and meteorological information of the backfill area of ​​the bridge abutment, as well as to obtain the design information or measured structural information of the bridge abutment; The modeling and division module is used to construct a 3D model of the abutment and backfill area, automatically divide the foundation backfill area, and generate a BIM model of the bridge abutment backfill; The frost heave prediction module calculates the predicted freezing depth for each foundation backfill area based on a meteorological-geological coupling prediction model and outputs the frost heave risk level; Mechanical analysis module, used to simulate and analyze structural stress and deformation, and generate the mechanical adaptability level of each foundation backfill area; The scheme generation module is used to call the material performance database and generate candidate backfill schemes based on the frost heave risk level and mechanical adaptability level; Simulation analysis module, used to simulate candidate backfill schemes and output performance indicators; The optimization module is used to select the final backfill plan based on the preset optimization objective function, reorganize the boundaries of the foundation backfill area, and output the construction area division plan; The scheduling generation module is used to generate an optimized construction plan based on the construction area division plan and the final backfill plan; The BIM management module is used to manage the backfill scheme optimization process based on the BIM model.

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