A method for generating a geological exploration profile for rail transit construction based on BIM

Abstract

The present invention provides a method for generating a geological exploration profile for rail transit construction based on BIM, belonging to the technical field of surveying and mapping. It includes: comprehensively improving the accuracy of geological exploration for rail transit construction through advanced data collection and intelligent analysis methods. At the construction site, multi-dimensional data such as geological drilling, ground-penetrating radar, and geophysical exploration tests are collected through various precision instruments, and a three-dimensional coordinate system is first established. Deep learning algorithms are used to deeply extract features from complex data to accurately obtain key information such as geological stratification, geotechnical parameters, and groundwater levels. By constructing a geological stratification model, a geotechnical parameter model, and a groundwater level model, the intelligent fusion of multi-source data is realized to generate a comprehensive geological information model. The extracted geological profile data is imported into the BIM platform, and strict accuracy verification and optimization are carried out using a professional geological information collaboration equation set, and finally a highly accurate and reliable geological exploration profile for rail transit construction is formed.

Description

Technical Field

[0001] The present invention belongs to the technical field of surveying and mapping, and more specifically, relates to a method for generating a geological exploration profile for rail transit construction based on BIM. Background Art

[0002] As a complex infrastructure project, rail transit engineering construction requires a comprehensive exploration and analysis of the geological environment at the construction site to provide a reliable geological reference for subsequent engineering design and construction. Traditional geological exploration methods mainly include geological drilling, geological radar detection, and geophysical exploration tests. Key information such as stratum distribution, geotechnical parameters, and groundwater levels can be obtained by analyzing these data. However, these geological exploration data usually have problems such as fragmented information, inconsistent data formats, and poor spatial correlation, making it difficult to provide complete three-dimensional geological information for the BIM (Building Information Modeling) platform.

[0003] On the other hand, with the wide application of BIM technology in the field of rail transit engineering, there is an urgent need to seamlessly integrate geological information into the BIM system to support the management of the geological environment throughout the design and construction processes. Existing BIM geological information integration methods mainly rely on simple geometric data import, which is difficult to reflect the actual distribution characteristics of strata and the spatial variation law of geotechnical parameters, thus affecting the accuracy of subsequent engineering design and construction. In addition, as an important constraint factor in rail transit engineering construction, the geological environment needs to be coupled with factors such as track alignment, structural dimensions, and loads for analysis, but existing BIM technology is difficult to meet this requirement.

[0004] Therefore, there is an urgent need for a new method that integrates geological exploration, geological information modeling, and rail transit geological fusion analysis to provide more accurate and reliable geological reference support for rail transit engineering construction. Summary of the Invention

[0005] In view of this, the present invention provides a method for generating a geological exploration profile for rail transit construction based on BIM, which can solve the technical problem in the prior art that it is difficult to achieve geological exploration, geological information modeling, and rail transit geological fusion analysis.

[0006] The present invention is implemented as follows: The present invention provides a method for generating a geological exploration profile for rail transit construction based on BIM, including the following steps:

[0007] S10. Collect geological exploration data at the rail transit construction site to obtain geological drilling data, geological radar data, and geophysical exploration test data;

[0008] S20. Establish a three-dimensional coordinate system for geological exploration points based on the geological drilling data, the geological radar data, and the geophysical exploration test data;

[0009] S30. Use a deep learning algorithm to extract features from the geological borehole data, the geological radar data, and the geophysical exploration test data to obtain geological stratification feature data, geotechnical parameter data, and groundwater level data;

[0010] S40. Establish a geological stratification model based on the geological stratification feature data, establish a geotechnical parameter model based on the geotechnical parameter data, and establish a groundwater level model based on the groundwater level data;

[0011] S50. Perform data fusion on the geological stratification model, the geotechnical parameter model, and the groundwater level model to generate a geological information model;

[0012] S60. Extract geological profile data of the rail transit construction route based on the geological information model;

[0013] S70. Import the geological profile data into BIM to generate a geological exploration profile for rail transit construction;

[0014] S80. Use the rail transit geological information collaborative equations to verify and optimize the accuracy of the geological exploration profile for rail transit construction to obtain the final geological exploration profile for rail transit construction;

[0015] S90. Update the optimized geological exploration profile data for rail transit construction to the BIM to complete the generation of the geological exploration profile for rail transit construction.

[0016] Further, the rail transit geological information collaborative equations include a track alignment constraint equation, a buried depth stress equation, a cross-section deformation equation, a formation bearing capacity equation, and a comprehensive optimization equation;

[0017] The track alignment constraint equation is used to calculate the matching degree between the track alignment and the geological conditions. The input parameters of the track alignment constraint equation include the track plane alignment data, the longitudinal section alignment data, and the line curvature data extracted from the BIM. The output parameter of the track alignment constraint equation is the track alignment geological adaptability index;

[0018] The buried depth stress equation is used to analyze the ground stress distribution state under the buried depth condition. The input parameters of the buried depth stress equation include the station structure buried depth data and the tunnel structure buried depth data extracted from the BIM, as well as the soil physical and mechanical parameters extracted from the geotechnical parameter model and the surface building load data extracted from the geological information model. The output parameter of the buried depth stress equation is the ground stress distribution data;

[0019] The cross-section deformation equation is used to predict the stratum deformation during construction. The input parameters of the cross-section deformation equation include the tunnel structure cross-section data extracted from the BIM, the support structure parameters, the soil physical and mechanical parameters extracted from the geotechnical parameter model, and the groundwater level data extracted from the groundwater level model. The output parameter of the cross-section deformation equation is the stratum deformation prediction data;

[0020] The stratum bearing capacity equation is used to evaluate the foundation bearing state. The input parameters of the stratum bearing capacity equation include the foundation soil parameters extracted from the geotechnical parameter model, the foundation structure parameters extracted from the BIM, and the structural load data. The output parameter of the stratum bearing capacity equation is the stratum bearing capacity checking data;

[0021] The comprehensive optimization equation is used to optimize the geological exploration profile of the rail transit construction. The input parameters of the comprehensive optimization equation include the track alignment geological adaptability index, the in-situ stress distribution data, the stratum deformation prediction data, and the stratum bearing capacity checking data. The output parameter of the comprehensive optimization equation is the optimized geological exploration profile data of the rail transit construction.

[0022] Further, the track alignment constraint equation is specifically expressed as follows:

[0023] ;

[0024] In the formula, is the track alignment geological adaptability index; is the horizontal curve radius of the th measuring point; is the vertical section elevation of the th measuring point; is the vertical section slope of the th measuring point; is the allowable minimum curve radius; is the allowable maximum elevation difference; is the allowable maximum slope; is the measuring point spacing; is the total number of measuring points; is the weight coefficient; is the attenuation coefficient; is the correction coefficient; is the geological condition correction term.

[0025] The buried depth stress equation is specifically expressed as follows:

[0026] ;

[0027] In the formula, is the total stress tensor; is the initial in-situ stress tensor; is the self-weight stress tensor of the th layer of soil; is the stress influence coefficient of the th layer of soil; is the number of soil layers; is the surface load influence coefficient; is the surface building load stress tensor.

[0028] The cross-section deformation equation is specifically expressed as follows:

[0029] ;

[0030] In the formula, is the deformation amount at the spatial position at time ; is the initial deformation distribution function; is the time effect coefficient; is the direction weight coefficient; is the number of spatial directions considered; is the water level influence coefficient; is the change amount of the underground water level.

[0031] The formation bearing capacity equation is specifically expressed as follows:

[0032] ;

[0033] In the formula, is the formation bearing capacity; is the cohesion of the soil; is the unit weight of the soil; is the foundation embedment depth; is the foundation width; is the foundation length; is the bearing capacity coefficient; is the safety factor; is the shape correction coefficient; is the inclination correction coefficient; is the base dip angle.

[0034] The comprehensive optimization equation is specifically expressed as follows:

[0035] ;

[0036] In the formula, is the overall optimization goal; is the calculated value of the th performance index; is the target value of the th performance index; is the weight coefficient; is the constraint condition; is the control variable; is the target value of the control variable; is the regularization coefficient; is the penalty coefficient; is the number of performance indicators; is the number of constraint conditions; is the number of control variables.

[0037] Explanation of equation principle:

[0038] 1. The track alignment constraint equation adopts an exponential decay form. The reason is that the influence of geological conditions on the alignment weakens with the increase of distance, and the exponential form can better describe this decay characteristic. The sub-item accumulation structure is adopted to separately consider the influences of plane, elevation, and slope, making the evaluation more comprehensive.

[0039] 2. The buried depth stress equation is expressed in tensor form. The reason is that the underground stress state has three-dimensionality and anisotropy, and the tensor form can completely describe the stress distribution state. The layered superposition principle is adopted to consider the combined action of soil self-weight and upper load.

[0040] 3. The cross-section deformation equation adopts a form combined with a time function and a second-order spatial derivative. The reason is that the deformation process has timeliness, and the deformation propagation conforms to the diffusion theory, and the second-order derivative can describe the spatial propagation characteristics of the deformation.

[0041] 4. The formation bearing capacity equation is based on the classical bearing capacity theory and introduces multiple correction coefficients, considering the influences of factors such as foundation shape, buried depth, and inclination, making the calculation results more in line with engineering practice.

[0042] 5. The comprehensive optimization equation adopts the least squares method structure and introduces a gradient regularization term and a penalty term. The reason is that it is necessary to balance multiple objectives while ensuring the smoothness of the solution and the satisfaction of constraint conditions.

[0043] Compared with the prior art, a method for generating a geological exploration profile for rail transit construction based on BIM provided by the present invention, first, the method uses various means such as drilling, ground penetrating radar, and geophysical exploration tests to obtain geological exploration data and establish a standardized geological data set. Then, through deep learning algorithms, feature extraction is performed on the geological exploration data to obtain key information such as stratum distribution, geotechnical parameters, and groundwater level, and a complete three-dimensional geological information model is established by integrating them. In addition, the method seamlessly integrates geological information into the BIM platform to generate a high-precision geological profile that meets the engineering requirements, providing a reliable geological reference for subsequent design and construction. It is worth mentioning that the method also introduces a collaborative equation system for rail transit geological information to verify and optimize the accuracy of the generated geological profile, ensuring the coordinated matching of geological information with factors such as track alignment, structural dimensions, and loads. This not only improves the reliability of geological exploration results but also creates a more favorable geological environment condition for the design and construction of rail transit projects.

[0044] Compared with traditional geological exploration methods and BIM geological information integration technologies, this method has the following advantages: 1) By integrating multi-source geological data, a comprehensive geological information model covering all elements of the geological environment is constructed; 2) The seamless integration of geological information with the BIM system is achieved, providing a visual geological reference for engineering design and construction; 3) By using the method of collaborative rail transit geological information, the coordinated matching of geological conditions and engineering requirements is ensured, improving the accuracy and reliability of engineering construction. It solves the technical problems in the prior art that it is difficult to achieve geological exploration, geological information modeling, and rail transit geological fusion analysis. Brief Description of the Drawings

[0045] Figure 1 is a flowchart of the method provided by the present invention;

[0046] Figure 2 is a geological borehole profile diagram in Embodiment 2;

[0047] Figure 3 is a diagram showing the variation of unit weight and cohesion with depth in Embodiment 2;

[0048] Figure 4 is a diagram showing the variation of internal friction angle with depth in Embodiment 2;

[0049] Figure 5 is a distribution diagram of formation bearing capacity and safety factor in Embodiment 2. Detailed Embodiment

[0050] As Figure 1 shown, it is a flowchart of a method for generating a geological exploration profile for rail transit construction based on BIM provided by the present invention. The following is a detailed description of the specific implementation of each step in this method:

[0051] The specific implementation of step S10 is to collect geological exploration data at the rail transit construction site. First, multiple geological drilling sampling points are arranged at the rail transit construction site, and drilling equipment is used for geological drilling to obtain soil samples and rock cores at different depths, record the information of rock and soil stratification, the physical and mechanical properties of rock and soil, and the groundwater level information, and form geological drilling data. Secondly, a geological radar detection grid is arranged along the rail transit construction route, and geological radar equipment is used to scan the underground medium to obtain the electromagnetic wave reflection signal of the underground medium, and a three-dimensional image of the underground geological structure is formed through signal processing to generate geological radar data. Thirdly, geophysical exploration test points are arranged, and high-density electrical method, transient electromagnetic method and seismic wave method are used for geological geophysical exploration tests to obtain the distribution of formation resistivity, the response characteristics of electromagnetic field and the distribution of seismic wave velocity, and form geophysical exploration test data. Finally, a quality control system for geological data collection is established to conduct quality inspection on geological drilling data, geological radar data and geophysical exploration test data, eliminate abnormal data, and form a standardized geological exploration data set. In this way, the geological exploration information of the rail transit construction site can be comprehensively obtained.

[0052] The specific implementation of step S20 is to establish a three-dimensional coordinate system for geological exploration points based on geological exploration data. First, a reference point at the rail transit construction site is selected to establish a global coordinate system, and a total station is used to measure the three-dimensional coordinates of geological drilling sampling points, geological radar detection grids and geophysical exploration test points. Secondly, a differential global positioning system is used to obtain the geodetic coordinates of the reference point, and the conversion relationship between the global coordinate system and the geodetic coordinate system is established. Then, according to the conversion relationship, the collection positions of geological drilling data, geological radar data and geophysical exploration test data are uniformly converted to the geodetic coordinate system. Finally, a spatial index for geological exploration data is established to realize the spatial positioning and retrieval functions of geological exploration data. In this way, the spatial association and collaborative analysis of geological exploration data can be realized.

[0053] The specific implementation of step S30 is to extract features from geological exploration data by using deep learning algorithms. First, a deep neural network model is constructed, including a convolutional layer, a pooling layer, a fully connected layer and an output layer, and a feature extraction network structure is designed according to the characteristics of geological exploration data. Secondly, the deep neural network model is used to extract features from geological drilling data to obtain the features of rock and soil stratification interfaces, the features of physical and mechanical parameters of rock and soil, and the features of groundwater level changes. Then, the deep neural network model is used to extract features from geological radar data to obtain the features of underground medium stratification, geological structure features and the distribution features of abnormal bodies. Then, the deep neural network model is used to extract features from geophysical exploration test data to obtain the features of formation physical property parameters, formation structure features and geological anomaly features. Through deep learning algorithms, the internal features of geological exploration data can be effectively extracted, laying a foundation for the subsequent establishment of geological information models.

[0054] The specific implementation of step S40 is to construct a geological information model based on the characteristics of geological exploration data. First, a three-dimensional geological stratification model is established based on the geological stratification characteristic data, including the formation interface morphology, formation thickness distribution, and geological structure characteristics. Secondly, a three-dimensional geotechnical parameter model is established based on the geotechnical parameter data, including the distribution of formation physical and mechanical parameters, formation bearing capacity distribution, and formation stability evaluation. Thirdly, a three-dimensional groundwater level model is established based on the groundwater level data, including the groundwater level distribution, groundwater flow direction, and characteristics of groundwater dynamic changes. Finally, a geological modeling software is used to perform three-dimensional visualization of the geological stratification model, geotechnical parameter model, and groundwater level model. By establishing a three-dimensional geological information model, the geological environment characteristics of the rail transit construction site can be systematically presented.

[0055] The specific implementation of step S50 is to perform data fusion on the geological information model. First, a multi-source data fusion framework for geological information is established, including a data preprocessing module, a feature matching module, a data fusion module, and a quality control module. Secondly, spatial registration is performed on the geological stratification model, geotechnical parameter model, and groundwater level model to establish the spatial correspondence relationship between the models. Then, a data fusion algorithm is used to fuse the registered model data to generate a unified geological information model. Finally, a spatio-temporal update mechanism for the geological information model is established to realize the dynamic update and maintenance of the model data. Through the fusion of multi-source geological data, a comprehensive geological information model covering all elements of the geological environment can be constructed.

[0056] The specific implementation of step S60 is to extract the geological profile data of the rail transit construction route from the geological information model. First, a three-dimensional path of the rail transit construction route is constructed in the geological information model. Secondly, profile extraction lines are set along the three-dimensional path, and the profile extraction parameters are determined, including the extraction width, extraction depth, and sampling interval. Then, a profile extraction algorithm is used to extract the geological profile data from the geological information model, including the formation distribution, geotechnical parameter distribution, and groundwater level distribution. Finally, interpolation processing is performed on the extracted geological profile data to form a continuous geological profile dataset. In this way, the geological profile information meeting the rail transit construction requirements can be obtained.

[0057] The specific implementation of step S70 is to import the geological profile data into the BIM system. First, a BIM geological profile data import interface is established, and the data conversion rules and data format standards are defined. Secondly, the geological profile data is converted according to the predefined data format to generate geological profile data meeting the BIM standard. Then, the converted geological profile data is imported into the BIM system to establish a geological profile model. Finally, three-dimensional visualization display of the geological profile is performed in the BIM system to support the query and analysis functions of the profile data. In this way, the geological information can be seamlessly integrated into the BIM platform, providing a geological reference basis for subsequent rail transit construction.

[0058] The specific implementation of step S80 is to use the collaborative equations of rail transit geological information to verify and optimize the accuracy of the geological exploration profile for rail transit construction. First, use the collaborative equations of rail transit geological information to verify the profile accuracy and calculate the index values including the geological adaptability of the track alignment, the distribution of ground stress, the prediction of stratum deformation, and the bearing capacity of the stratum. Second, identify the abnormal areas and unreasonable areas in the geological profile according to the calculation results. Then, use the optimization algorithm to correct the abnormal areas and unreasonable areas to improve the accuracy and reliability of the geological profile. Finally, establish a record of the geological profile optimization, recording the optimization process and results. Through the verification and optimization of the collaborative equations of rail transit geological information, it can be ensured that the geological exploration profile meets the requirements of rail transit project construction.

[0059] The specific implementation of step S90 is to update the optimized geological exploration profile data to the BIM system. First, convert the format of the optimized geological profile data according to the BIM data standard. Second, update the original geological profile data in the BIM system to maintain the consistency and integrity of the data. Third, generate a geological profile update log, recording the update time, update content, and update personnel information. Finally, conduct a quality inspection on the updated geological profile to ensure the correctness of the update operation. By regularly updating the geological exploration profile information in the BIM system, it can be ensured that the rail transit project design and construction always have the latest geological condition data.

[0060] Specifically, the principle of the present invention is: systematic collection and feature extraction of geological exploration data, intelligent construction of geological information models, integration of geological profiles and BIM systems, and coupled collaborative analysis of rail transit geological information.

[0061] First, the method makes full use of existing geological exploration means, such as geological drilling, ground penetrating radar, and geophysical exploration tests, to systematically collect the geological environment data of the rail transit construction site. For these three types of data sources, namely geological borehole data, ground penetrating radar data, and geophysical exploration test data, deep learning algorithms are used for feature extraction to obtain key geological information such as stratum distribution, geotechnical parameters, and groundwater levels. This machine learning-based feature extraction method can automatically identify and extract the most critical geological features for engineering construction from a large amount of complex geological exploration data, laying a foundation for the subsequent establishment of geological information models.

[0062] Secondly, the method fuses the extracted geological feature information to establish three-dimensional geological stratification models, geotechnical parameter models, and groundwater level models, thereby constructing a comprehensive geological information model covering all elements of the geological environment. To ensure the spatial correlation and data consistency among these geological information models, technical means of data fusion are adopted, including steps such as spatial registration, information fusion, and dynamic update. This information fusion method based on multi-source geological data can overcome the limitations of single exploration means and generate a more complete and reliable description of the geological environment.

[0063] Furthermore, the method seamlessly integrates the constructed geological information model into the BIM platform to generate geological profile data that meets the requirements of rail transit projects. Through operations such as spatial path extraction and data format conversion, key geological information in the three-dimensional geological model, such as stratum distribution, geotechnical parameters, and groundwater level, can be effectively imported into the BIM system, providing intuitive and visual geological references for engineering design and construction. The integration of this geological information and the BIM system not only improves the application efficiency of geological results but also lays a foundation for subsequent joint optimization analysis.

[0064] Finally, the method introduces a collaborative equation system for rail transit geological information to verify and optimize the accuracy of the generated geological profiles. These collaborative equations include track alignment constraints, buried depth stress analysis, section deformation prediction, and stratum bearing capacity assessment, etc., which can comprehensively evaluate the matching degree between geological conditions and rail transit engineering design parameters. By optimizing and adjusting the geological profile data, ensure the coordination of factors such as track alignment, structural dimensions, and loads with the geological environment, thereby improving the overall reliability of engineering construction.

[0065] The following provides a specific embodiment 1 of the present invention. The specific implementation manners of each step in this embodiment 1 are described in detail as follows: The specific implementation manner of step S10 is to collect geological exploration data at the construction site of the rail transit. For geological drilling and sampling, the comprehensive information of each borehole sample point can be expressed by the following formula: ;

[0066] Among them, respectively represent the three-dimensional spatial positions of the borehole coordinates; represents the depth information of stratum division; represents the physical and mechanical property information of soil samples or rock cores; represents the buried depth information of the groundwater level; represents other supplementary information. By collecting and recording these borehole information, a complete geological borehole data set can be constructed.

[0067] For geological radar scanning, the three-dimensional geological structure image obtained by scanning can be expressed by the following formula: ;

[0068] Among them, represents the electromagnetic wave reflection signal of the underground medium; represents the formation resistivity distribution; represents the formation dielectric constant distribution; represents the formation magnetic permeability distribution. By performing inversion processing on these physical parameters, continuous three-dimensional geological structure information can be obtained.

[0069] For geological geophysical exploration tests, the formation parameter information obtained by various geophysical exploration methods can be described by the following formulas:

[0070] High-density electrical method: ; Transient electromagnetic method: ; Seismic wave method: ;

[0071] Among them, represents the formation resistivity distribution; represents the formation conductivity distribution; represent the longitudinal wave and transverse wave velocity distributions respectively. By testing and analyzing these formation physical property parameters, geological anomaly characteristics can be obtained.

[0072] Finally, by establishing a quality control system for geological data acquisition, quality inspection and outlier rejection of the obtained geological exploration data are carried out to form a standardized geological exploration data set.

[0073] The specific implementation manner of step S20 is to establish a three-dimensional coordinate system for geological exploration points based on geological exploration data. First, the three-dimensional coordinates of each geological exploration point can be measured using a total station , and a global coordinate system for the rail transit construction site is established . Then, the geodetic coordinates of the reference point are obtained using a differential global positioning system , and the conversion relationship between the global coordinate system and the geodetic coordinate system is established: ;

[0074] Among them, is the coordinate of the reference point in the global coordinate system; is the coordinate of the reference point in the geodetic coordinate system; is the element of the coordinate transformation matrix. Finally, a spatial index of the geological exploration data is established to achieve rapid retrieval of data for each sampling point.

[0075] The specific implementation manner of step S30 is to use a deep learning algorithm to extract features from geological exploration data. The following deep neural network model can be constructed:

[0076] ;

[0077] Among them, represents the convolutional layer, represents the pooling layer, represents the fully connected layer; are the weight parameters of the convolutional layer and the fully connected layer respectively. By training this deep neural network model, the following features can be extracted from the geological borehole data, geological radar data, and geophysical exploration test data respectively:

[0078] Geological borehole data features: ;

[0079] Geological radar data features: ;

[0080] Geophysical exploration test data features: ;

[0081] These feature data lay the foundation for the subsequent construction of the geological information model.

[0082] The specific implementation manner of step S40 is to construct a geological information model according to the geological exploration data features. First, a three-dimensional geological stratification model can be established based on the geological stratification feature data :

[0083] ;

[0084] Among them, is the three-dimensional geometric shape function of the th stratum, describing the stratum interface shape and the stratum thickness distribution.

[0085] Secondly, a three-dimensional geotechnical parameter model can be established based on the geotechnical parameter data :

[0086] ;

[0087] Among them, respectively represent the three-dimensional distributions of soil cohesion, unit weight, internal friction angle, elastic modulus, Poisson's ratio, and ultimate bearing capacity.

[0088] Thirdly, a three-dimensional groundwater level model can be established based on the groundwater level data :

[0089] ;

[0090] Among them, represents the groundwater level elevation distribution, represents the components of the groundwater flow velocity on the three coordinate axes.

[0091] Finally, a geological modeling software can be used to visually display these three-dimensional models.

[0092] The specific implementation of step S50 is to perform data fusion on the geological information model. An optional specific implementation is described as follows: First, establish the following data fusion framework:

[0093] ;

[0094] Among them, the functions of each module are as follows: Preprocessing module: preprocess the original model data, such as data cleaning, format conversion, etc.; FeatureMatching module: establish the spatial association between different models and determine the corresponding relationship between models; DataFusion module: adopt data fusion algorithms, such as Kalman filtering, Dempster-Shafer theory, etc., to fuse the corresponding model data; QualityControl module: perform quality inspection on the fused comprehensive geological information model to ensure the consistency and integrity of the data.

[0095] Then, establish the spatial correspondence between models:

[0096] ;

[0097] Finally, use the Kalman filtering algorithm to perform spatio-temporal update on the fused geological information model:

[0098] ;

[0099] Among them, are the relevant parameters of the Kalman filtering algorithm, are the system input and measurement values. Through this spatio-temporal update mechanism, it can be ensured that the geological information model always remains in the latest state.

[0100] The specific implementation of step S60 is to extract the geological profile data of the rail transit construction route from the geological information model. First, construct the three-dimensional space path of the rail transit construction route in the three-dimensional geological information model , where , and represents the path length coordinate. Then, set the profile extraction line along the path , and determine the width , depth and sampling interval of the profile extraction. Next, use the following profile extraction algorithm to extract the geological profile data from the geological information model:

[0101] ;

[0102] Finally, perform secondary interpolation on the extracted geological profile data to generate a continuous and smooth geological profile dataset:

[0103] ;

[0104] In this way, the geological profile information meeting the requirements of rail transit construction is obtained.

[0105] The specific implementation of step S70 is to import the geological profile data into a BIM system (Autodesk BIM360 or Bentley ProjectWise can be used). An optional specific implementation is as follows: First, establish the following BIM geological profile data import interface:

[0106] ;

[0107] Among them, the DataTransformRule module defines the rules for converting geological profile data into the standard data format of BIM, and the DataFormatStandard module defines the standard data format of geological profile data in the BIM system. Then, import the converted geological profile data into the BIM system to generate a three-dimensional geological profile model:

[0108] ;

[0109] Finally, display the geological profile model in the visualization interface of the BIM system and support querying and analysis of geological profile data.

[0110] The specific implementation of step S80 is to use the rail transit geological information collaborative equations to verify and optimize the accuracy of the geological exploration profile for rail transit construction. First, use the following track alignment constraint equations to evaluate the alignment adaptability of the geological profile:

[0111] ;

[0112] Among them, respectively represent the plane curve radius, vertical section elevation, and slope of the th measurement point; is the allowable minimum curve radius, maximum elevation difference, and maximum slope; are the weighting coefficient and correction term.

[0113] Secondly, use the following buried depth stress equation to calculate the underground stress distribution:

[0114] ;

[0115] Among them, is the total stress tensor; is the initial geostress tensor; For the Self-weight stress tensor of soil layer; For the Layer soil stress influence coefficient; is the surface load influence coefficient; is the surface building load stress tensor.

[0116] Again, the following section deformation equation is used to predict the formation deformation:

[0117] ;

[0118] in, For point In time The amount of deformation; is the initial deformation distribution; is the time effect coefficient; is the spatial direction weight; is the groundwater level influence coefficient; is the groundwater level change.

[0119] Finally, the bearing capacity equation of the ground is used to evaluate the bearing state of the foundation:

[0120] ;

[0121] in, is the cohesion and weight of the soil; is the bearing capacity coefficient; is the safety factor; is the shape correction factor; is the tilt correction factor; is the base inclination angle.

[0122] According to the calculation results of the above indicators, the abnormal areas in the geological profile are identified and corrected using the following comprehensive optimization equation:

[0123] ;

[0124] in, For the The calculated value of the performance indicator; is the corresponding target value; is the indicator weight; is a constraint condition; is the control variable; is the regularization and penalty coefficient. The geological profile is adjusted through the optimization algorithm to meet the requirements of rail transit engineering construction.

[0125] The specific implementation of step S90 is to update the optimized geological exploration profile data to the BIM system. An alternative implementation is as follows: First, convert the optimized geological profile data into the BIM standard data format to generate . Then, update the original geological profile model in the BIM system so that it is consistent with . Next, generate a geological profile update log to record the update time, update content, and information of the update personnel. Finally, conduct a quality inspection on the updated geological profile model to ensure the integrity and accuracy of the data. Through this series of steps, it can be ensured that the geological profile data in the BIM system always remains up-to-date, providing a reliable geological reference basis for subsequent rail transit engineering design and construction.

[0126] To better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: The project of a certain urban rail transit Line 5 is in the preliminary exploration stage. In order to comprehensively understand the geological environment conditions at the construction site, the project department decides to adopt the method for generating a geological exploration profile for rail transit construction based on BIM proposed by the present invention.

[0127] First, 20 geological borehole sampling points are arranged at key positions along Line 5. These sampling points cover the entire length of the track line of about 30 km, with an average spacing of about 1.5 km. Geological drilling and coring to a depth of 50 m are carried out at each sampling point using drilling equipment, and about 1000 meters of geotechnical samples are obtained. During the drilling process, the staff detailedly records the geotechnical layering information, geotechnical physical and mechanical properties, and the groundwater level conditions at each depth layer. For example, at a sampling point K3+450, the geological borehole data obtained is shown in Table 1:

[0128] Table 1 Geological Borehole Data

[0129]

[0130] As Figure 2 shown, it is a geological borehole profile diagram, showing the distribution of geological layers at different depths, including four main strata: plain fill, silty clay, moderately weathered granite, and strongly weathered granite. At the same time, the groundwater level line (blue dotted line) is marked. The vertical axis represents the depth (in meters), and the horizontal axis represents the distance. Each stratum is filled with a different color for easy visual identification.

[0131] In addition, 30 geological radar detection grid points were also arranged along Line 5. The underground medium was scanned at these grid points using geological radar equipment, and electromagnetic wave reflection signals of the underground medium were obtained. By processing and analyzing these signals, a three-dimensional underground geological structure image along the track line was constructed. Taking a detection grid point G2+800 as an example, the geological radar data obtained is shown in Table 2:

[0132] Table 2 Geological Radar Data

[0133]

[0134] Meanwhile, 15 geophysical exploration test points were arranged along Line 5. Geological geophysical exploration tests were carried out at these points using the high-density resistivity method, transient electromagnetic method, and seismic wave method, and information such as formation resistivity distribution, electromagnetic field response characteristics, and seismic wave velocity distribution was obtained. Taking a test point P1+200 as an example, the geophysical exploration data obtained is shown in Table 3:

[0135] Table 3 Geophysical Exploration Test Data

[0136]

[0137] After completing the collection work of geological exploration data, the engineering department established a corresponding quality control system. First, a total station was used to measure the three-dimensional coordinates of geological borehole sampling points, geological radar detection grid points, and geophysical exploration test points, and their spatial position information at the rail transit construction site was obtained. Then, the differential GPS technology was used to determine the geodetic coordinates of the reference points at the construction site, and the conversion relationship between the global coordinate system and the geodetic coordinate system was established. Next, the collection position information of the geological exploration data was uniformly converted to the geodetic coordinate system, and a spatial index of the geological data was established. Finally, quality inspections were carried out on the obtained geological borehole data, geological radar data, and geophysical exploration test data, and outliers were removed to form a standardized geological exploration data set.

[0138] With the standardized geological exploration data, the engineering department immediately began to construct a three-dimensional geological information model. First, a deep learning algorithm was used to extract features from the geological exploration data. For the geological borehole data, this algorithm can automatically identify key features such as rock and soil stratification interfaces, rock and soil physical and mechanical parameters, and groundwater level changes; for the geological radar data, this algorithm can extract feature information such as underground medium stratification, geological structures, and abnormal body distributions; for the geophysical exploration test data, this algorithm can obtain formation physical property parameters, formation structures, and geological anomalies.

[0139] Based on the above characteristic data, the engineering department then constructed a three-dimensional geological stratification model, a three-dimensional geotechnical parameter model, and a three-dimensional groundwater level model. The three-dimensional geological stratification model describes the spatial distribution pattern of the formation interface and the variation law of the formation thickness, as shown in Table 4:

[0140] Table 4 Geological Stratification Model

[0141]

[0142] The three-dimensional geotechnical parameter model describes the spatial distribution of the physical and mechanical properties of each formation, as shown in Table 5:

[0143] Table 5 Geotechnical Parameter Model

[0144]

[0145] As Figure 3 and Figure 4 shown, Figure 3 is the variation diagram of unit weight and cohesion with depth, Figure 4 is the variation diagram of internal friction angle with depth. Different parameters are distinguished by different marks and colors, and a double axis is equipped to display parameters of different magnitudes simultaneously.

[0146] The three-dimensional groundwater level model reflects the spatial distribution characteristics of the groundwater level, as shown in Table 6:

[0147] Table 6 Groundwater Level Model

[0148]

[0149] After having the above three-dimensional geological information model, the engineering department then integrated it into the BIM platform. First, a three-dimensional construction path of Line 5 was constructed in the BIM system, and relevant parameters for geological profile extraction were set, such as the profile width being 50m, the extraction depth being 60m, the sampling interval being 10m, etc. Then, a geological profile extraction algorithm was used to extract geological profile data from the three-dimensional geological information model along the construction path, including information such as formation distribution, geotechnical parameter distribution, and groundwater level distribution. Finally, the extracted geological profile data was formatted into the BIM standard and imported into the BIM system to generate a three-dimensional geological profile model that meets the engineering requirements.

[0150] To further improve the accuracy and reliability of the geological profile, the engineering department also introduced a collaborative equation set for rail transit geological information to conduct an optimization analysis of the geological profile. First, the track alignment constraint equation was used to evaluate the adaptability of the geological conditions to the track alignment, and the results are shown in Table 7:

[0151] Table 7 Evaluation of the Adaptability of Track Alignment to Geology

[0152]

[0153] Secondly, the stress distribution of underground structures under geological conditions was calculated using the buried depth stress equation, as shown in Table 8:

[0154] Table 8 Stress analysis of underground structures

[0155]

[0156] Again, the cross-section deformation equation was used to predict the deformation of the stratum during the construction process, as shown in Table 9:

[0157] Table 9 Prediction of formation deformation

[0158]

[0159] Finally, the bearing capacity equation of the stratum was used to evaluate the bearing state of the foundation, as shown in Table 10:

[0160] Table 10 Verification of stratum bearing capacity

[0161]

[0162] like Figure 5 As shown in the figure, a bar chart is used to show the bearing capacity of different locations (station foundation, viaduct pier foundation, tunnel lining structure), and a broken line is used to show the corresponding safety factor. The left vertical axis represents the bearing capacity (kPa), and the right vertical axis represents the safety factor.

[0163] Based on the verification results of the above indicators, the engineering department identified some abnormal and unreasonable areas in the geological profile, mainly concentrated in K6+800 and K9+200. For these problem areas, the optimization algorithm was used to make targeted corrections, focusing on: 1) adjusting the track line shape, optimizing the curvature and slope indicators; 2) optimizing the structural dimensions of stations and tunnels to reduce the underground stress level; 3) strengthening the design of the supporting structure to control the deformation of the stratum. After multiple rounds of optimization calculations, a precision-verified and optimized rail transit construction geological survey profile was finally generated, providing a reliable geological reference for subsequent engineering design and construction.

[0164] By updating the optimized geological profile data to the BIM system and combining it with the visualization function of the BIM platform, engineering designers can more intuitively understand the geological environment conditions of the construction site. For example, they can view information such as stratum distribution, geotechnical parameters, and groundwater levels through a three-dimensional model, and design foundation structures, support schemes, and drainage systems in a targeted manner; they can combine the structural model in BIM to analyze the matching degree between geological conditions and engineering design parameters, and provide a basis for optimizing the design scheme; they can also dynamically track the impact of geological environment changes on engineering construction and take corresponding countermeasures in a timely manner.

[0165] Generally speaking, the method for generating geological exploration profiles for rail transit construction based on BIM fully integrates multiple technical means such as geological exploration technology, geological information modeling, and rail transit geological coupling analysis, providing strong geological reference support for the design and construction of the Line 5 project. Compared with traditional geological exploration methods, this method has the following advantages: 1) By integrating multi-source geological data, a more complete and reliable geological information model is constructed, providing a comprehensive description of the geological environment for engineering applications. 2) The seamless integration of geological information with the BIM system is achieved, enabling geological reference information to be intuitively presented to engineering designers and constructors, greatly improving the application efficiency of geological results. 3) The method of collaborative analysis of rail transit geological information is adopted to ensure the coordinated matching of geological conditions and engineering design requirements, improving the overall reliability of engineering construction. 4) The method for obtaining and optimizing geological information based on intelligent computing technology improves the accuracy and reliability of geological exploration results, creating more favorable geological conditions for engineering construction.

[0166] It should be noted that the variable explanations involved in the description of the present invention are shown in Table 11 below.

[0167] Table 11 Variable Explanation Table

[0168]

[0169] As mentioned above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for generating geological survey profiles for rail transit construction based on BIM, characterized in that: The following steps are involved: S10, collecting geological survey data at the rail transit construction site, obtaining geological drilling data, geological radar data, and geophysical test data; S20, establishing a three-dimensional coordinate system of geological survey points based on the geological drilling data, the geological radar data, and the geophysical test data; S30, using a deep learning algorithm to extract features from the geological drilling data, the geological radar data, and the geophysical test data to obtain geological stratification feature data, geotechnical parameter data, and groundwater level data; S40, establishing a geological stratification model according to the geological stratification characteristic data, establishing a geotechnical parameter model according to the geotechnical parameter data, and establishing a groundwater level model according to the groundwater level data; S50, fusing the geological stratification model, the geotechnical parameter model, and the groundwater level model to generate a geological information model; S60, extracting geological profile data of the rail transit construction route based on the geological information model; S70, importing the geological profile data into BIM to generate a geological survey profile for rail transit construction; S80, using a rail transit geological information collaborative equation group to verify and optimize the accuracy of the rail transit construction geological survey profile to obtain a final rail transit construction geological survey profile; S90, updating the optimized rail transit construction geological survey profile data into the BIM to complete the generation of the rail transit construction geological survey profile.

2. The method according to claim 1, characterized in that The step S10 specifically includes: Step 101: Arrange multiple geological drilling sampling points, use drilling equipment to perform geological drilling and coring, obtain soil samples and rock cores at different depths, record rock and soil stratification information, rock and soil physical and mechanical properties, and groundwater level information, and form geological drilling data; Step 102: deploy a geological radar detection grid along the rail transit construction route, use geological radar equipment to scan the underground medium, obtain the electromagnetic wave reflection signal of the underground medium, form a three-dimensional image of the underground geological structure through signal processing, and generate geological radar data; Step 103: Arrange geophysical exploration test sites, use high-density electrical method, transient electromagnetic method and seismic wave method to conduct geological geophysical exploration tests, obtain formation resistivity distribution, electromagnetic field response characteristics and seismic wave velocity distribution, and form geophysical exploration test data; Step 104: Establish a geological data acquisition quality control system, perform quality inspection on the geological drilling data, the geological radar data and the geophysical test data, eliminate abnormal data, and form a standardized geological exploration data set.

3. The method according to claim 2, characterized in that The step S20 specifically includes: Step 201, select the reference points of the rail transit construction site, establish a global coordinate system, and use a total station to measure the three-dimensional coordinates of the geological drilling sampling points, the geological radar detection grid and the geophysical detection test points; Step 202: using a differential global positioning system to obtain the geodetic coordinates of the reference point, and establishing a conversion relationship between the global coordinate system and the geodetic coordinate system; Step 203, uniformly converting the acquisition positions of the geological drilling data, the geological radar data and the geophysical test data into a geodetic coordinate system according to the conversion relationship; Step 204: Establish a spatial index of geological exploration data to realize spatial positioning and retrieval functions of geological exploration data.

4. The method according to claim 3, characterized in that The step S30 specifically includes: Step 301: construct a deep neural network model, including a convolutional layer, a pooling layer, a fully connected layer, and an output layer, and design a feature extraction network structure according to the characteristics of geological exploration data; Step 302: Use the deep neural network model to extract features from the geological drilling data to obtain rock and soil stratification interface features, rock and soil physical and mechanical parameter features, and groundwater level change features; Step 303: Use the deep neural network model to extract features from the geological radar data to obtain underground medium stratification features, geological structure features, and abnormal body distribution features; Step 304: Use the deep neural network model to extract features from the geophysical test data to obtain formation physical property parameter features, formation structure features, and geological anomaly features.

5. The method according to claim 4, characterized in that The step S40 specifically includes: Step 401: Establish a three-dimensional geological stratification model based on the geological stratification feature data, including stratum interface morphology, stratum thickness distribution and geological structure characteristics; Step 402: establishing a three-dimensional geotechnical parameter model based on the geotechnical parameter data, including distribution of formation physical and mechanical parameters, distribution of formation bearing capacity, and evaluation of formation stability; Step 403: establishing a three-dimensional groundwater level model based on the groundwater level data, including groundwater level distribution, groundwater flow direction, and groundwater dynamic change characteristics; Step 404: Use geological modeling software to perform three-dimensional visualization on the geological layering model, the geotechnical parameter model, and the groundwater level model.

6. The method according to claim 5, characterized in that The step S50 specifically includes: Step 501, establishing a geological information multi-source data fusion framework, including a data preprocessing module, a feature matching module, a data fusion module and a quality control module; Step 502: spatially align the geological layering model, the geotechnical parameter model and the groundwater level model to establish a spatial correspondence between the models; Step 503: using a data fusion algorithm to fuse the registered model data to generate a unified geological information model; Step 504: Establish a spatiotemporal update mechanism for the geological information model to achieve dynamic update and maintenance of model data.

7. The method according to claim 6, characterized in that The step S60 specifically includes: Step 601: construct a three-dimensional path of a rail transit construction route in the geological information model; Step 602: setting a profile extraction line along the three-dimensional path, and determining profile extraction parameters, including extraction width, extraction depth, and sampling spacing; Step 603: extracting geological profile data from the geological information model using a profile extraction algorithm, including stratum distribution, geotechnical parameter distribution, and groundwater level distribution; Step 604: interpolate the extracted geological profile data to form a continuous geological profile data set.

8. The method according to claim 7, characterized in that The step S70 specifically includes: Step 701: Establish a BIM geological profile data import interface and define data conversion rules and data format standards; Step 702: convert the geological profile data according to a predefined data format to generate geological profile data that complies with the BIM standard; Step 703: import the converted geological profile data into the BIM system to establish a geological profile model; Step 704: Perform a three-dimensional visual display of the geological profile in the BIM system, and support query and analysis functions of the profile data.

9. The method according to claim 8, characterized in that The step S80 specifically includes: Step 801: Use the rail transit geological information collaborative equation group to verify the profile accuracy and calculate various index values; Step 802: Identify abnormal areas and unreasonable areas in the geological profile according to the calculation results; Step 803: Use optimization algorithms to correct abnormal areas and unreasonable areas to improve the accuracy and reliability of geological profiles; Step 804: Establish a geological profile optimization record to record the optimization process and optimization results.

10. The method according to claim 9, characterized in that The step S90 specifically includes: Step 901: convert the optimized geological profile data into a format according to the BIM data standard; Step 902: Update the original geological profile data in the BIM system to maintain the consistency and integrity of the data; Step 903: Generate a geological profile update log to record the update time, update content, and updater information; Step 904: Perform a quality check on the updated geological profile to ensure the correctness of the update operation.

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