A method and system for intelligent site selection of bridge pile foundations based on geological BIM models

Abstract

This disclosure provides a method and system for intelligent site selection of bridge pile foundations based on a geological BIM model, comprising: loading a three-dimensional geological BIM model including topographic surfaces and strata; acquiring data of at least one bridge pile foundation site selection evaluation index, and generalizing geological parameters to generate a pile foundation suitability score; generating a spatial evaluation result of the engineering area based on the score; and visualizing a three-dimensional distribution map of pile foundation suitability in the BIM model. The method of this disclosure integrates the entire chain of geological parameter analysis, index quantification evaluation, and three-dimensional visualization into a single BIM platform, ensuring the integrity of key geological data, significantly improving the accuracy of pile foundation bearing capacity calculation and the reliability of site selection decisions, avoiding data conversion losses, and systematically optimizing the design efficiency and economy of bridge engineering.

Description

Technical Field

[0001] This disclosure relates to the field of bridge engineering construction, and in particular to a method and system for intelligent site selection of bridge pile foundations based on geological BIM models. Background Technology

[0002] In bridge engineering construction, pile foundations, as key load-bearing components supporting the bridge structure, directly affect the safety and economy of the project due to their site selection rationality. Currently, bridge pile foundation site selection mainly relies on manual geological survey data and empirical judgment, which presents significant technical bottlenecks: on the one hand, geological exploration data, limited by borehole density, cannot fully cover the project area, leading to large errors in the spatial distribution prediction of key parameters such as stratum lithology and bedrock depth; on the other hand, existing site selection evaluation systems rely on subjective expert scoring, lacking a unified quantitative standard for the weighting of indicators such as terrain slope and rock stratum dip angle, resulting in significant fluctuations in evaluation results among different engineering teams. Especially in complex geological areas such as mountains and valleys, traditional two-dimensional drawings cannot intuitively represent the three-dimensional stratigraphic structure, and the integration of BIM and GIS solutions frequently suffers from attribute loss and accuracy degradation during model conversion due to differences in software data formats, ultimately leading to errors in pile foundation bearing capacity calculation. Current mainstream technologies have not yet achieved deep coupling between geological data and three-dimensional models, nor have they established an intelligent site selection system based on quantitative geological parameters.

[0003] To overcome the above-mentioned shortcomings, it is urgent to develop an intelligent site selection method for bridge pile foundations based on geological BIM models. By integrating multi-source geological data and using a quantitative evaluation mechanism, this method can overcome the limitations of human experience and the bottleneck of data conversion, thereby systematically improving the scientific nature and engineering reliability of bridge pile foundation site selection. Summary of the Invention

[0004] This disclosure provides a method and system for intelligent site selection of bridge pile foundations based on geological BIM models, aiming to solve the problems existing in the above-mentioned prior art.

[0005] In a first aspect, this disclosure provides an intelligent site selection method for bridge pile foundations based on a geological BIM model, the method comprising:

[0006] Load a 3D geological BIM model containing topographic surfaces and stratigraphic volumes;

[0007] Data on at least one bridge pile foundation site selection evaluation index are obtained, and geological parameters are generalized to generate a pile foundation suitability score.

[0008] Spatial evaluation results of the engineering area are generated based on the scoring.

[0009] Visualize the three-dimensional distribution map of pile foundation suitability in the BIM model.

[0010] Optionally, the evaluation indicators include at least one of the following: bedrock burial depth, uniaxial saturated compressive strength of rock, fresh rock layer thickness ratio, topographic slope, stratum dip angle, characteristic value of soil bearing capacity, and groundwater burial depth.

[0011] Optionally, the spatialized evaluation results are implemented through grid cells, and the size of the grid cells can be dynamically adjusted.

[0012] Optionally, the generalized geological parameters employ a Kriging space interpolation algorithm, including:

[0013] Calculate the variability function based on borehole data and fit a theoretical model;

[0014] Construct a system of Kriging equations and solve for the weighting coefficients;

[0015] A continuous geological parameter field is generated by weighted summation.

[0016] Optionally, the generation of the pile foundation suitability score includes scoring using a single-factor analysis method, wherein the single-factor analysis method includes:

[0017] For each evaluation indicator, a score is calculated based on the preset optimal and worst critical values, where the optimal critical value corresponds to 100 points and the worst critical value corresponds to 0 points, and the intermediate value is obtained through linear interpolation.

[0018] Optionally, the generation of the pile foundation suitability score includes scoring using the Analytic Hierarchy Process (AHP), which includes:

[0019] Establish a hierarchical model consisting of the target layer, the criteria layer, and the indicator layer;

[0020] The target layer is for bridge pile foundation site selection; the criterion layer is a primary evaluation index, including at least one of engineering economy, construction safety and long-term durability; the index layer is a secondary evaluation index, including at least one of bedrock depth, uniaxial saturated compressive strength of rock, fresh rock layer thickness ratio, topographic slope, stratum dip angle, characteristic value of soil bearing capacity and groundwater depth.

[0021] The index weights are calculated using the judgment matrix;

[0022] The comprehensive score is obtained by weighted summation based on the weights.

[0023] Optionally, the step of calculating the index weights through the judgment matrix includes:

[0024] Calculate the weights of the primary evaluation indicators: determine the weight ratio of each indicator in the criterion layer to the target layer through the judgment matrix, and perform consistency checks;

[0025] Calculate the weights of the secondary evaluation indicators: For each primary evaluation indicator, construct the judgment matrix of its subordinate secondary evaluation indicators and calculate the local weights;

[0026] Synthesize global weights: Multiply the weights of the primary evaluation indicators with the corresponding local weights of the secondary evaluation indicators layer by layer to obtain the global comprehensive weights of all secondary indicators.

[0027] Optionally, visualizing the three-dimensional distribution map of pile foundation suitability in the BIM model includes:

[0028] The regions were divided into five levels based on the comprehensive score and assigned chromatograms.

[0029] Merge adjacent grids of the same level to generate polygonal blocks;

[0030] Project the blocks onto the terrain surface to render a 3D distribution.

[0031] Optionally, merging adjacent meshes is achieved by extracting boundary edges and constructing contour lines.

[0032] Secondly, this disclosure provides an intelligent site selection system for bridge pile foundations based on a geological BIM model. The system is used to execute the intelligent site selection method for bridge pile foundations based on a geological BIM model as described in various embodiments. The system includes:

[0033] The model loading module is used to load a 3D geological BIM model containing topographic surfaces and stratigraphic volumes.

[0034] The index calculation module is used to acquire data of at least one bridge pile foundation site selection evaluation index, generalize geological parameters, and generate a pile foundation suitability score.

[0035] The scoring and analysis module is used to generate spatial evaluation results for the engineering area based on the scoring.

[0036] The visualization module is used to visualize the three-dimensional distribution of pile foundation suitability in the BIM model.

[0037] The beneficial effects of this disclosure are that, compared with the prior art, this disclosure has the following advantages:

[0038] 1) This invention loads a three-dimensional geological BIM model carrying the topological structure of the strata and uses the Kriging spatial interpolation algorithm to generalize discrete borehole data to construct a continuous geological parameter field covering the entire engineering domain. This breaks through the limitation of missing strata information caused by insufficient borehole density in traditional exploration, significantly reduces the spatial error of lithology identification and bedrock burial depth prediction, and provides high-precision geological basis for pile foundation bearing capacity calculation.

[0039] 2) This invention establishes a quantitative evaluation system based on the analytic hierarchy process or single-factor scoring method. By objectively allocating the weights of indicators through a judgment matrix, standardized scoring is achieved, replacing the manual experience-based scoring mode. This solves the problem of the lack of a unified standard for the weight allocation of indicators such as terrain slope and rock stratum dip angle, significantly reducing the differences in evaluation results among different teams and improving the scientific nature and reproducibility of site selection decisions.

[0040] 3) This invention integrates the entire process of geological parameter analysis, index evaluation, and visualization into a single BIM environment, directly calling the model's original data for analysis, skipping the cross-software format conversion process, fundamentally eliminating the problems of attribute loss and geometric accuracy reduction caused by differences in BIM and GIS data formats, ensuring the integrity of the stratigraphic structure expression, and ensuring strict consistency between the analysis results and the original geological model.

[0041] Through the synergistic effect of the above three aspects, this invention not only solves the complex bottlenecks of information blind spots, subjective fluctuations, and conversion distortion in traditional pile foundation site selection, but also empowers designers to make intuitive decisions through three-dimensional visualization and dynamic rendering, ultimately systematically improving the design efficiency and economic safety margin of bridge engineering. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0043] Figure 1 A flowchart illustrating an intelligent site selection method for bridge pile foundations based on a geological BIM model, provided in this embodiment of the disclosure;

[0044] Figure 2 A schematic diagram of the hierarchical analysis structure model provided in the embodiments of this disclosure;

[0045] Figure 3 This is a schematic diagram of merging adjacent grids of the same level provided in an embodiment of this disclosure;

[0046] Figure 4 This is a schematic diagram of the intelligent site selection visualization results for bridge engineering provided in the embodiments of this disclosure;

[0047] Figure 5 This is a structural schematic diagram of an intelligent site selection system for bridge pile foundations based on a geological BIM model, provided in an embodiment of this disclosure.

[0048] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0049] The present disclosure will be further described below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present disclosure more clearly, and should not be used to limit the scope of protection of the present disclosure.

[0050] Figure 1 This is a flowchart of an intelligent site selection method for bridge pile foundations based on a geological BIM model, provided according to an embodiment of this disclosure.

[0051] like Figure 1 As shown in this embodiment, a smart site selection method for bridge pile foundations based on a geological BIM model may include:

[0052] S100, load a 3D geological BIM model carrying stratigraphic information;

[0053] BIM (Building Information Modeling) is a building model built upon various relevant information and data of a building project. Through digital information simulation, it simulates the real information of the building. A geological model organizes three-dimensional spatial data, simulating the geometric shape and relationships between real geological bodies, and most models carry geological information. In the process of loading and implementing a 3D geological BIM model, it is essential to first clarify that the model is a digital information simulation system built upon multi-source geological exploration data of the building project. Its core function is to achieve a spatial digital representation of real geological structures by 3D simulating the geometric shape and relationships between geological bodies. In practice, engineers import 3D geological model files containing stratigraphic structure information through the model loading module of the BIM platform. It is important to note that common geological models include various types such as borehole point cloud models, exploration line profile models, and stratigraphic models. Among them, the stratigraphic model, as the core carrier for pile foundation site selection analysis, must completely include the spatial topology of key stratigraphic units such as the bedrock surface, weathering zone, and overburden. This invention mainly selects bridge pile foundation sites by analyzing and calculating strata and topographic surfaces. Therefore, the geological model loaded must include a strata model. In this embodiment, strata and topographic data are loaded, and the entire process strictly follows the geological model data standard to ensure that the strata attribute information remains completely associated during the loading process.

[0054] S200, Select at least one evaluation index for bridge pile foundation site selection and obtain its data;

[0055] S210, Select evaluation indicators;

[0056] Numerous geological factors influence the selection of pile foundation sites, including lithology, stratigraphic attitude, and weathering degree. In one optional implementation, engineering economy, construction safety, and long-term durability are used as primary evaluation indicators. Based on the main influencing factors of the primary evaluation indicators, the following seven secondary evaluation indicators are determined:

[0057] (1) Based on the impact of engineering economics, three indicators are extracted as representative factors: bedrock burial depth, uniaxial saturated compressive strength of rock, and fresh rock layer thickness ratio. In this embodiment, the engineering economics do not consider the unit price of materials, but only the impact of construction usage.

[0058] ① Bedrock depth represents the vertical distance from the natural surface to the top of the bedrock. The bedrock must be unweathered or slightly weathered, and its wave velocity is generally > 500 m / s when it is relatively stable. Typically, pile foundations need to be embedded in stable rock strata; therefore, as the bedrock depth and pile length increase, the drilling depth and concrete pouring volume also increase. The pouring volume formula is: V = S h, where S is the base area of ​​the pile foundation and h is the pile length.

[0059] ② Uniaxial saturated compressive strength of rock, characterized by the ultimate compressive strength measured by a uniaxial compressive strength test when the rock sample reaches saturation. The uniaxial saturated compressive strength of rock is inversely proportional to the reduction factor of the rock mass bearing capacity. When the compressive strength is greater than 60 MPa, the corresponding value of the reduction factor ranges from 0.05 to 0.07; when the compressive strength is less than 60 MPa but greater than 10 MPa, the corresponding value ranges from 0.05 to 0.10; when the compressive strength is less than 10 MPa, the corresponding value ranges from 0.10 to 0.15. Under otherwise identical conditions, low-strength rock layers require larger pile diameters or deeper embedment depths to compensate for the bearing capacity loss. According to the aforementioned grouting volume formula, the larger the pile diameter and the deeper the pile foundation, the larger the grouting volume will be.

[0060] ③ Fresh rock layer thickness ratio, which represents the ratio of the thickness of the fresh rock layer to the pile length in the strata into which the pile is embedded. Fresh rock layer refers to rock strata that have not undergone significant weathering, including undifferentiated and slightly differentiated rock strata. Chinese bridge codes require that the pile foundation tip extend into bedrock above the moderately weathered layer, i.e., fresh rock layer. Therefore, the smaller the fresh rock layer thickness ratio, the longer the required pile length. According to the aforementioned grouting volume formula, the longer the pile length, the larger the grouting volume.

[0061] (2) Based on the impact of construction safety, three indicators were extracted as representative factors: topographic slope, stratum dip angle, and soil foundation bearing capacity characteristic value.

[0062] ① Slope, which characterizes the steepness of a terrain surface, is usually defined as the ratio of the vertical height of the slope to its horizontal distance. A steep slope is prone to sideslip during operations and has poor stability. The Swedish slice method formula for calculating the safety factor of slope stability is: ,in The angle between the tangent to the sliding surface and the horizontal is the angle between the slope and the horizontal. The greater the slope, the larger the angle, and the lower the safety factor.

[0063] ② Stratum dip angle, which represents the angle between the dip line of a stratum and its projection onto the horizontal plane, is an important parameter of geological structure. The presence of a dip angle may lead to uneven thickness of the bearing stratum at the pile tip, resulting in poor stability of the pile foundation. The formula for the impact of stratum dip angle on safety is similar to that for topographic slope.

[0064] ③ The characteristic value of soil bearing capacity is a measure of the pressure value corresponding to a specified deformation within the linear deformation segment of the soil pressure-deformation curve determined by load tests. It is a crucial parameter for assessing foundation stability and directly affects the safety of engineering structures. The most basic definition of the safety factor is: When the working load (i.e. the external load) is constant, the ultimate load is represented by the characteristic value of the soil's foundation bearing capacity. Therefore, the larger the characteristic value, the larger the safety factor.

[0065] (3) Based on the impact of long-term durability, the groundwater burial depth index is extracted as a representative factor.

[0066] ① Groundwater depth represents the vertical distance from the groundwater surface to the earth's surface. Due to the presence of chemical elements in groundwater, it corrodes the steel reinforcement and concrete used in the foundation piles, making it impossible to maintain the stability of the superstructure of a bridge in the long term. Shallow groundwater contains higher levels of chloride ions and oxygen, which induces the fastest rate of steel reinforcement corrosion. The oxygen content in groundwater decreases with increasing depth, and the chloride ion content is lower in all layers, thus reducing the corrosion rate.

[0067] S220, Obtain evaluation indicator data;

[0068] Apart from the topographic slope and the dip angle of the strata, the data for other indicators can be obtained by searching the database.

[0069] In one optional implementation, the terrain slope is calculated by projecting the calculation point onto the terrain surface, finding the triangular facet containing the calculation point, and then using the plane normal vector to calculate the angle between the triangular facet and the horizontal plane. Since the dip angle of the strata ranges from 0-90°, the dip angle is determined by the following formula:

[0070]

[0071] in, The angle between the triangular facet and the horizontal plane. The dip angle of the strata.

[0072] For example, the center point coordinates of the mesh to be calculated are (2565398.170, 469766.390). The vertical projection yields the coordinates of the three corner points of the triangular facet as (2565395.711, 469757.472, 155.669), (2565405.711, 469767.472, 157.562), and (2565393.460, 469771.650, 160.470). The normal vector is then calculated. Next, calculate the normal vector and the horizontal plane normal vector. The cosine of the angle between The bottom dip angle is obtained. Spend.

[0073] In one alternative implementation, the calculation method for terrain slope is similar to that for stratum dip, the only difference being that the stratum model is generally a mesh, and the calculation point is projected onto two triangular facets. The upper triangular facet is selected as the stratum dip. Other repetitive details will not be repeated.

[0074] S230, generalized geological parameter field;

[0075] In the generalization of the geological parameter field stage, it is necessary to distinguish the data acquisition methods for two types of evaluation indicators: For indicators such as topographic slope and stratigraphic dip angle, which can be directly calculated based on a 3D model, a grid scoring of the entire region is achieved by uniformly subdividing the topographic surface and stratigraphic grid model; while for indicators such as bedrock depth, fresh rock layer thickness ratio, and groundwater level depth, which depend on borehole sampling data, due to the limited number of actual boreholes, a continuous geological parameter field needs to be constructed using spatial interpolation algorithms. Taking groundwater level depth as an example, the specific implementation process is as follows: First, the measured values ​​of groundwater level depth at each borehole point are extracted. Then, a simulated groundwater level surface covering the entire engineering domain is generated based on the Kriging interpolation algorithm, thereby obtaining the predicted depth value at any coordinate point. Other borehole-dependent indicators are all spatially generalized using the same technical approach.

[0076] The Kriging interpolation algorithm used in this embodiment is based on utilizing the spatial autocorrelation characteristics between sampling points, quantifying the structural characteristics of regionalized variables through a variogram function, and ultimately achieving the optimal unbiased linear estimation of the attribute values ​​of unknown points. Its mathematical model is expressed as follows: Among them, Z x For the estimated value of the point to be found, Z represents the weighting coefficient. i These are observed values ​​from known points. The advantage of this algorithm lies in its dynamic allocation of weights based on the spatial distance between unknown points and known sample points (higher weight for closer points), achieving a realistic reconstruction of the geological parameter field through weighted averaging. The weighting coefficients... The calculation process includes the following key steps:

[0077] S231: Calculate the variogram model;

[0078] The variogram is a crucial tool in Kriging interpolation, used to represent the spatial variation characteristics and intensity of regionalized variables. In the variogram modeling phase of the Kriging interpolation algorithm, the spatial variation characteristics of the regionalized variable must first be clearly defined. For example, suppose the distance between two points in space is h, and their spatial locations are denoted as x and x+h, respectively. Then the variogram expression for this regionalized variable is:

[0079] ,

[0080] When the regionalized variable satisfies the second-order stationarity assumption (i.e., the covariance function) (Existing), the mutation function can be transformed into:

[0081] .

[0082] The variogram values ​​calculated based on discrete sample points in the above formula only characterize the spatial variability of the data itself. To systematically describe the structural relationships between data, a theoretical variogram model needs to be selected to fit the discrete observations. In engineering practice, the exponential, spherical, and Gaussian models are commonly used as fitting functions.

[0083] In this embodiment, the more known points, the more accurate the fitting result. To illustrate the calculation process, we will take four known groundwater depth points as an example:

[0084] Z1 (463182.983,3151574.304,2.8)

[0085] Z2 (463048.294,3151525.913,4.8)

[0086] Z3 (462980.65, 3151498.466, 3.8)

[0087] Z4 (462925.053,3151475.908,4.8),

[0088] The first two columns represent the X and Y coordinates of the plane, and the third column represents the burial depth. To determine the groundwater depth at the unknown point Z5 (463090.32, 3151536.706), the discrete values ​​of the variogram for the sample point pairs must first be calculated, as shown in Table 1 below. Based on the variogram calculation formula, the discrete points of the relationship r = r(h) can be calculated.

[0089] Table 1

[0090]

[0091] Assuming a linear model is used for fitting The slope 'a' is obtained by fitting the data using the least squares method:

[0092]

[0093] Therefore, the fitting function model is: .

[0094] S232: Constructing a system of equations

[0095] Solving for the weighting coefficients requires satisfying two conditions: unbiasedness and optimality. Unbiasedness requires that the expected value between the estimated value and the true value of the unknown data be zero. Optimality requires that the expected value of the squared deviation between the estimated value and the true value of the unknown data be minimized. Based on the unbiasedness condition, the constraint relationship for the weighting coefficients can be derived. By combining the extremum problem constructed with the optimality condition, a system of Kriging linear equations for solving the weight coefficients is finally established:

[0096]

[0097] Where, r ij Let μ represent the variogram value between point i and point j, and μ be a Lagrange multiplier.

[0098] Based on the variogram model r(h) established in step S411, calculate the variogram r between each pair of known sample points, and the variogram r between each known point and the unknown point. The equation can be formed as follows:

[0099]

[0100] Obtained through matrix calculations =0.1, =0.55, =0.05, =0.3.

[0101] S233: Calculate the interpolation result;

[0102] By solving the system of equations constructed in step S412 above, the weight coefficients of each known sample point are obtained. And the Lagrange multiplier μ. Based on this set of weight coefficients, the known point observations Z... i By performing a weighted summation calculation, the attribute estimate of the unknown point Z5 can be obtained: Substitute into the weight system =0.1, =0.55, =0.05, =0.3 and the corresponding burial depths Z1=2.8, Z2=4.8, Z3=3.8, Z4=4.8, we can calculate:

[0103] Z5 = (0.10 × 2.8) + (0.55 × 4.8) + (0.05 × 3.8) + (0.30 × 4.8) = 4.55. This calculation result achieves the optimal spatial estimate of the groundwater depth at the unknown point.

[0104] S300, constructing an evaluation system;

[0105] This embodiment allows users to freely combine evaluation methods for site selection analysis. At least one of the seven evaluation indicators included in step S200 can be selected for individual or comprehensive analysis, including single-factor bridge selection analysis and multi-factor bridge site selection analysis, so as to improve the system's adaptability to actual engineering projects.

[0106] S310, Single-factor bridge site selection analysis;

[0107] This embodiment includes multiple evaluation indicators, with different evaluation indicators corresponding to different units and dimensions, and the scoring standards also differ.

[0108] Based on the "Technical Specification for Building Pile Foundations" and expert questionnaire survey, a score range of 0 to 100 is taken as the scoring range, with 0 and 100 as the critical values. The scoring criteria shown in Table 2 below are set, and the scores in the middle range of 0 to 100 are calculated using linear correlation.

[0109] For example, areas with a slope less than 5° are assigned a value of 100, while areas with a slope greater than 35° are assigned a value of 0, with a linear decrease in the value in between. (The above describes the slope characteristics.) The fraction of degree is .

[0110] Table 2

[0111]

[0112] S320, Multi-factor bridge site selection analysis;

[0113] In this embodiment, when performing multi-factor location analysis, the final result is obtained by weighted summation based on the weight of each factor. Therefore, the weight setting of each scoring indicator is particularly important. To improve user convenience, this embodiment uses the Analytic Hierarchy Process (AHP) to quantify expert experience and provide a more universal weight allocation scheme. Optionally, users can also freely set the weights of each evaluation indicator according to application needs. The AHP of this embodiment will be described in detail below.

[0114] S321: Establish a hierarchical structure model;

[0115] Based on the ultimate goal and the factors that need to be considered in achieving it, construct a system as follows: Figure 2 The model shown is a hierarchical model composed of a target layer, a criterion layer, and an indicator layer. The hierarchical model includes the primary and secondary evaluation indicators selected in step S200. The primary evaluation indicators serve as the criterion layer, the secondary evaluation indicators as the indicator layer, and the target layer is the bridge pile foundation site selection. In this embodiment, based on the evaluation indicators described in step S200, three primary evaluation indicators (including engineering economy, construction safety, and long-term durability) and seven secondary evaluation indicators (including bedrock depth, uniaxial saturated compressive strength of rock, fresh rock layer thickness ratio, topographic slope, stratum dip angle, characteristic value of soil bearing capacity, and groundwater depth) are selected to construct the hierarchical model.

[0116] S322: Construct the judgment matrix;

[0117] The judgment matrix is ​​used to determine the weight ratio of each criterion layer to the target layer by comparing each element pairwise. For example, if engineering economics is more important than long-term durability, then the ratio of engineering economics to long-term durability is 3. Through an expert consultation questionnaire survey, experts in the field were invited to score the importance of each indicator. The scoring results were then discussed and summarized internally, resulting in the judgment matrix shown in Table 3 below.

[0118] Table 3

[0119]

[0120] Standardizing the judgment matrix yields the weights of each indicator. First, the column vectors of the matrix are normalized. Then, each row is summed and normalized again to obtain the weight matrix w. The calculation process is as follows:

[0121] ==> ==> =w.

[0122] S323: Hierarchical single sorting and its consistency test;

[0123] First, calculate the largest eigenvalue of the judgment matrix. :

[0124] ;

[0125] Then, a consistency check is performed, and the consistency index (CI) is calculated:

[0126] ;

[0127] When N=3, the average random consistency index Calculate the random consistency ratio CR:

[0128]

[0129] Since CR < 0.1, the construction of the judgment matrix can be considered reasonable. The weights of the indicators are calculated as shown in Table 4 below:

[0130] Table 4

[0131]

[0132] S324: For each primary evaluation indicator, repeat the hierarchical analysis process of steps S321 to S323 for its subordinate secondary evaluation indicators.

[0133] For example, the judgment matrix and weight allocation for the secondary evaluation indicators corresponding to the economic efficiency of the project are shown in Tables 5 and 6 below:

[0134] Table 5

[0135]

[0136] Table 6

[0137]

[0138] The judgment matrix and weight allocation for the secondary evaluation indicators corresponding to construction safety are shown in Tables 7 and 8 below:

[0139] Table 7

[0140]

[0141] Table 8

[0142]

[0143] S325: Based on the weights of the primary evaluation indicators and the local weights of their subordinate secondary evaluation indicators, the global comprehensive weights of all secondary evaluation indicators relative to the target layer are obtained by calculating layer by layer through the multiplication synthesis rule.

[0144] For example, based on the local weights of the above-mentioned evaluation indicators and their subordinate secondary evaluation indicators, the global comprehensive weights of all secondary evaluation indicators relative to the target layer are calculated, as shown in Table 9 below:

[0145] Table 9

[0146]

[0147] S326: Normalized score results;

[0148] To convert evaluation indicators with different dimensions into a unified score, normalization is required. This embodiment uses range normalization to unify the scores of the selected evaluation indicators, using the formula... Calculate the corresponding normalized score, where x i x is the measured value. min and x max The worst and best critical values ​​are defined.

[0149] S400 generates regional evaluation results and visualizes suitable pile foundation site selection areas in the BIM model;

[0150] In the pile foundation site selection analysis and visualization stage, firstly, based on the mandatory requirements for pile foundation dimensions in the "Code for Design of Highway Bridge and Culvert Foundations," including that the design diameter of bored piles should not be less than 0.8m, the minimum side width of excavated piles should not be less than 1.2m, and the diameter range of concrete pipe piles should be 0.4-1.2m with a wall thickness of not less than 80mm, the boundary of the influence range of the geological evaluation is determined. Based on these design constraints, this embodiment uses a 3m×3m grid to divide the engineering area into units by default. This size parameter can be dynamically adjusted by the user according to the actual engineering needs. For each grid unit, the single-factor evaluation system established in step S320 is called to calculate the score value of each indicator participating in the scoring, and it is multiplied by the weight ratio obtained through the analytic hierarchy process or the weight ratio set by the user, finally generating the comprehensive score of pile foundation site selection for that grid. Specifically, the following steps are included:

[0151] S410, Visualize site selection results;

[0152] S411: Visualization Standard Definition;

[0153] In one optional implementation, a five-level evaluation standard is first established based on the comprehensive suitability score of the pile foundation: a score of 80 to 100 is considered excellent, 60 to 80 is good, 40 to 60 is average, 20 to 40 is poor, and below 20 is very poor. Pile foundation construction is recommended for excellent and good areas, while pile foundation construction is prohibited in poor and very poor areas. To visually distinguish the evaluation levels, a standard color spectrum is used to fill the grid cells: green for excellent areas, cyan for good areas, blue for average areas, purple for poor areas, and red for very poor areas.

[0154] S412: Merge adjacent grids of the same level;

[0155] In one optional implementation, to optimize the region analysis effect and facilitate block-based analysis of the region, adjacent grids of the same level are topologically merged. This process is achieved by analyzing the sharing characteristics of grid edges: the four edges of each rectangular grid are divided into three categories according to their usage status: boundary edges B1 exclusively used by a single grid, boundary edges B2 shared by two grids of different levels, and internal edges B3 shared by two grids of the same level. By extracting all B1 edges and B2 edges to construct the outer contour line of the region, and simultaneously deleting B3 edges to eliminate redundant structures, discrete grids of the same level are merged into continuous polygonal blocks, such as... Figure 3 As shown.

[0156] S413: Projecting a plane point onto a terrain surface;

[0157] Based on the above step S412 of merging continuous polygonal blocks to obtain regions of different levels, the two-dimensional evaluation blocks are integrated with the three-dimensional terrain model: First, the outer contour closed line of the merged block is extracted, and each control point on the line is vertically projected onto the BIM terrain surface to generate a three-dimensional spatial coordinate point set, which is then connected in sequence to form a three-dimensional closed boundary line; then, the surface cutting interface of the BIM platform is called to divide the terrain triangular mesh (Mesh) along the three-dimensional boundary line and extract independent surface patches; finally, the surface patches are filled with color according to the evaluation level standard to generate a model such as... Figure 4 The diagram shows a 3D distribution of pile foundation suitability. This visualization solution allows engineers to directly identify suitable pile foundation placement areas from a surface perspective, achieving deep integration of geological data and engineering design.

[0158] The technical solution of this disclosure bypasses the traditional cross-platform data conversion process and directly relies on the BIM environment to build a full-process analysis system, fundamentally solving the problem of model attribute loss and geometric accuracy reduction caused by the difference in data formats between BIM and GIS software. This method integrates the entire chain of geological parameter analysis, index quantification evaluation, and 3D visualization into a single BIM platform. This not only ensures the integrity of key geological data such as stratigraphic lithology and bedrock depth, but also significantly improves the accuracy of pile foundation bearing capacity calculation and the reliability of site selection decisions through dynamic analysis in a unified data environment. Ultimately, while avoiding data conversion losses, it systematically optimizes the design efficiency and economy of bridge engineering.

[0159] Figure 5 This is a structural schematic diagram of an intelligent bridge pile foundation site selection system based on a geological BIM model, provided according to an embodiment of this disclosure. This system is used to run an intelligent bridge pile foundation site selection method based on a geological BIM model, as described in the above embodiments. (Refer to...) Figure 5 The system may include:

[0160] The model loading module is used to load a 3D geological BIM model containing topographic surfaces and stratigraphic volumes.

[0161] The index calculation module is used to acquire data of at least one bridge pile foundation site selection evaluation index, generalize geological parameters, and generate a pile foundation suitability score.

[0162] The scoring and analysis module is used to generate spatial evaluation results for the engineering area based on the scoring.

[0163] The visualization module is used to visualize the three-dimensional distribution of pile foundation suitability in the BIM model.

[0164] According to embodiments of this disclosure, an electronic device is also provided, which may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor can invoke logical instructions in the memory to execute the methods described above.

[0165] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] On the other hand, this disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to perform the methods described above.

[0167] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0168] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the various embodiments or the methods described in the embodiments.

[0169] It should be understood that the above embodiments are only used to illustrate the technical solutions of this disclosure, and not to limit them; although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A bridge pile foundation intelligent site selection method based on a geological BIM model, characterized in that, The method comprises: loading a three-dimensional geological BIM model comprising a terrain surface and a stratum body; obtaining data of at least one bridge pile site selection evaluation index, and generalizing geological parameters to generate a pile foundation suitability score; generating a spatialized evaluation result of an engineering area based on the score; visualizing a three-dimensional distribution map of pile foundation suitability in the BIM model; the spatialized evaluation result is realized by a grid cell, and the size of the grid cell supports dynamic adjustment; the generalized geological parameters adopt a Kriging spatial interpolation algorithm, comprising: calculating a variogram and fitting a theoretical model based on drilling data; constructing a Kriging equation set to solve weight coefficients; generating a continuous geological parameter field through weighted summation; the visualizing a three-dimensional distribution map of pile foundation suitability in the BIM model comprises: dividing five-level regions according to the comprehensive score and assigning a color spectrum; merging adjacent grid cells of the same level to generate polygon blocks; projecting the blocks onto the terrain surface to render a three-dimensional distribution.

2. The bridge pile intelligent site selection method based on a geological BIM model according to claim 1, characterized in that, The evaluation index comprises at least one of bedrock burial depth, rock uniaxial saturated compressive strength, fresh rock layer thickness ratio, terrain slope, stratum dip angle, soil foundation bearing capacity characteristic value, and groundwater depth.

3. The bridge pile intelligent site selection method based on a geological BIM model according to claim 1, characterized in that, The generating a pile foundation suitability score comprises using a single factor analysis method for scoring, and the single factor analysis method comprises: for each evaluation index, performing score calculation based on preset optimal critical values and worst critical values, wherein the optimal critical value corresponds to 100 points, the worst critical value corresponds to 0 points, and intermediate values are obtained through linear interpolation.

4. The bridge pile intelligent site selection method based on a geological BIM model according to claim 1, characterized in that, The generating a pile foundation suitability score comprises using an analytic hierarchy process for scoring, and the analytic hierarchy process comprises: establishing a hierarchical model of a target layer, a criterion layer, and an index layer; the target layer is bridge pile site selection, the criterion layer is a first-level evaluation index, and the index layer is a second-level evaluation index; the first-level evaluation index comprises at least one of engineering economy, construction safety, and long-term durability; and the second-level evaluation index comprises at least one of bedrock burial depth, rock uniaxial saturated compressive strength, fresh rock layer thickness ratio, terrain slope, stratum dip angle, soil foundation bearing capacity characteristic value, and groundwater depth; calculating index weights through a judgment matrix; 5. The bridge pile intelligent site selection method based on the geological BIM model according to claim 4, characterized in that, obtaining a comprehensive score through weighted summation based on the weights. The calculating index weights through a judgment matrix comprises: calculating first-level evaluation index weights: determining the weight proportion of each index of the criterion layer to the target layer through a judgment matrix, and performing consistency check; calculating second-level evaluation index weights: for each first-level evaluation index, constructing a judgment matrix of its subordinate second-level evaluation indexes, and calculating local weights; 6. The bridge pile intelligent site selection method based on a geological BIM model according to claim 1, characterized in that, synthesizing global weights: multiplying the first-level evaluation index weights and the corresponding second-level evaluation index local weights layer by layer to obtain global comprehensive weights of all second-level indexes.

7. A bridge pile foundation intelligent site selection system based on a geological BIM model, characterized in that, The merging adjacent grid cells of the same level is realized by extracting boundary edges and constructing contour lines. The system is used to perform a geological BIM model-based bridge pile intelligent site selection method according to any one of claims 1-6, and the system comprises: a model loading module for loading a three-dimensional geological BIM model comprising a terrain surface and a stratum body; an index calculation module for obtaining data of at least one bridge pile site selection evaluation index, and generalizing geological parameters to generate a pile foundation suitability score; and A scoring analysis module is configured to generate a spatialized evaluation result of the engineering area based on the scores. A visualization module is configured to visualize the three-dimensional distribution map of the pile foundation suitability in the BIM model.

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