Road and bridge design method and system based on BIM real scene model

Abstract

The invention relates to the technical field of building information models, in particular to a road and bridge design method and system based on a BIM real scene model. Comprising the following steps: S1, performing data acquisition on a project area through a data acquisition module; s2, noise is removed, classification is carried out, photogrammetry software Pix4D is used for splicing the images, and an orthoimage map is generated; s3, converting the processed point cloud data into a triangular mesh; s4, importing the processed data by using BIM software, and constructing a three-dimensional real scene model; s5, a road design module in the BIM software is used, route parameters are set, a road center line is generated, and cross section design is carried out; and S6, visually displaying the design scheme by using a three-dimensional model so as to help all parties to better understand and cooperate. Through denoising and classification processing on the original data, unnecessary interference factors can be effectively removed, and the availability and reliability of the data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a road and bridge design method and system based on a BIM real-scene model. Background Art

[0002] The road and bridge design method and system based on BIM real-life model is a comprehensive solution that has emerged to cope with the challenges of increasingly complex engineering projects under the background of the rapid development of modern information technology. With the acceleration of urbanization and the development of social economy, the requirements for infrastructure construction are constantly increasing. The traditional two-dimensional drawings and manual calculation methods can no longer meet the complexity and refinement of modern engineering projects. In this context, building information modeling (BIM), as an advanced design concept and technical means, has gradually become the mainstream trend in the field of road and bridge design. BIM can not only realize the digitization and informatization of architectural design, but also transform the actual environmental data into a high-precision real-life model through advanced technologies such as three-dimensional scanning and photogrammetry, and then seamlessly connect with the BIM model to form an integrated design platform. This design method based on real-life model enables designers to accurately simulate and evaluate the actual effects of roads and bridges in a virtual environment, thereby optimizing the design plan, improving the design quality and efficiency, and also provides a solid foundation for simulation, resource allocation and later operation and maintenance management in the construction phase.

[0003] In the existing road and bridge design methods and systems based on BIM real-life models, although BIM technology can provide detailed data integration and visualization, in actual applications, due to compatibility issues between different software platforms, it may lead to obstacles in data exchange and sharing, thus affecting the overall collaborative efficiency of the project. Secondly, BIM real-life modeling usually relies on high-precision three-dimensional scanning data, and the cost of obtaining these data is high, and it is greatly affected by weather conditions and technical operation levels, which limits its widespread application in some areas with difficult conditions. Furthermore, although BIM technology can achieve relatively accurate design simulation, for the design of roads and bridges under complex geological conditions, existing technology may be difficult to fully predict all potential problems, especially in areas where geological disasters such as earthquakes and landslides are frequent, the prediction ability of the model still needs to be improved. In addition, the maintenance and updating of the BIM system also requires continuous technical support and the participation of professionals, which is a challenge for some projects with limited resources. Finally, in the actual construction stage, how to seamlessly connect the BIM model with the on-site construction situation is also an urgent problem to be solved, because design changes may occur during the construction process, and how to ensure that the changed design can still be efficiently reflected in the BIM model is a major problem facing current technology. Therefore, a road and bridge design method and system based on BIM real-scene model are provided. Summary of the invention

[0004] The purpose of the present invention is to provide a road and bridge design method and system based on BIM real-scene model, so as to solve the problem in the existing road and bridge design method and system based on BIM real-scene model proposed in the above background technology. Although BIM technology can provide detailed data integration and visualization, in actual application, due to compatibility issues between different software platforms, it may lead to obstacles in data exchange and sharing, thereby affecting the overall collaboration efficiency of the project. Secondly, BIM real-scene modeling usually relies on high-precision three-dimensional scanning data, and the cost of obtaining these data is high, and it is greatly affected by weather conditions and technical operation levels, which limits its wide application in certain areas with difficult conditions. Furthermore, although BIM technology can achieve more accurate design simulation, for road and bridge design under complex geological conditions, the existing technology may be difficult to fully predict all potential problems, especially in areas where geological disasters such as earthquakes and landslides occur frequently, and the prediction ability of the model still needs to be improved.

[0005] To achieve the above object, the present invention provides a road and bridge design method based on BIM real scene model, comprising the following steps: S1. Collect data in the project area through the data collection module; S2, remove noise, classify, and use the photogrammetry software Pix4D to stitch images and generate orthophotos; S3, converting the processed point cloud data into a triangular mesh; S4. Use BIM software to import processed data and build a 3D real-life model; S5. Use the road design module in the BIM software to set route parameters, generate the road centerline, and carry out cross-section design; S6. Use 3D models to visually display design solutions to help all parties better understand and collaborate.

[0006] As a further improvement of the technical solution, in S1, the data acquisition module specifically includes: Use total stations and GNSS equipment to determine the locations of ground control points; Obtain high-resolution images through drones for later orthophoto production; Use 3D laser scanners to obtain high-density point cloud data to reflect existing terrain and structural features.

[0007] As a further improvement of the technical solution, in S2, the specific steps of removing noise, classifying, and using the photogrammetry software Pix4D to stitch images and generate orthophotos are as follows: S2.1, pre-process the collected images to remove noise; S2.2, use image processing algorithms to classify images; S2.3. Use Pix4D software to stitch the images and generate orthophotos.

[0008] As a further improvement of the technical solution, in S2.3, the specific expression for stitching images by Pix4D software is: ; in, Indicates the final orthophoto map; Represents Pix4D software and its related processing flow; Indicates the Original image; Represents the process of image registration.

[0009] As a further improvement of the technical solution, in S3, the specific steps of converting the processed point cloud data into a triangular mesh are: S3.1, filtering the collected point cloud data to remove abnormal points; S3.2, use DBSCAN clustering algorithm to classify the point cloud and distinguish ground points from other non-ground points; S3.3, use Delaunay triangulation to convert point cloud data into triangular mesh; S3.4. Optimize the generated triangular mesh to ensure the quality of the model.

[0010] As a further improvement of the technical solution, in S3.2, the specific expression of the DBSCAN clustering algorithm for classifying the point cloud is: Define the field: ; in, Indicate point fields; Represents a data point; represents another data point; Represents a dataset; Indicate point and Point The distance between Indicates the radius of the field; Define the core points: ; in, Indicate point Whether it is a core point; Indicates the minimum number of neighborhood points required to become a core point; Define the boundary points: ; in, Indicate point Whether it is a boundary point; Define noise points: ; in, Indicate point Whether it is a noise point; Expanding the cluster: ; in, Indicates the current cluster; Represents an extended cluster Contains point .

[0011] As a further improvement of the technical solution, in S4, the specific steps of using BIM software to import the processed data and construct a three-dimensional real scene model are: S4.1. Import the processed point cloud data and orthophotos into BIM software; S4.2. In BIM software, point cloud data and orthophotos are combined to construct a three-dimensional real-scene model.

[0012] As a further improvement of the technical solution, in S5, the specific steps of using the road design module in the BIM software to set route parameters, generate the road centerline, and carry out the cross-section design are: S5.1. Define road design parameters in BIM software; S5.2. Use the functions of BIM software to generate road centerlines based on terrain and design parameters; S5.3. Carry out cross-section design along the centerline of the road to define the cross-sectional shape and size of the road.

[0013] As a further improvement of the technical solution, in S6, the specific steps of using the three-dimensional model to intuitively display the design solution are as follows: S6.1. Render the 3D model in BIM software; S6.2. Export 3D model views and animation videos from different perspectives; S6.3 Organize stakeholder meetings and use the exported views or videos to demonstrate the design solution.

[0014] On the other hand, the present invention provides a road and bridge design system based on the BIM real-scene model, including a collector, a processor, and BIM software imported into the BIM software and run on the processor, characterized in that the BIM software running on the processor implements any one of the above-mentioned road and bridge design methods based on the BIM real-scene model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the road and bridge design method and system based on BIM real-life model, high-resolution images are obtained by drones and high-density point cloud data are obtained by 3D laser scanners, ensuring the high accuracy and integrity of the data. This high-quality data provides a solid foundation for subsequent design. By denoising and classifying the original data, unnecessary interference factors can be effectively removed, improving the availability and reliability of the data.

[0016] 2. In the road and bridge design method and system based on BIM real-scene model, Pix4D software is used to stitch images and generate orthophotos, and combined with the triangular mesh converted from point cloud data, an accurate three-dimensional terrain model can be constructed. This model not only provides an intuitive visual presentation, but can also be used for accurate engineering calculations and simulations. Through accurate design and simulation, resource utilization can be optimized, material waste can be reduced, and project sustainability can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, this embodiment provides a road and bridge design method based on a BIM real-life model, comprising the following steps: S1. Collect data in the project area through the data collection module; In this example, the data acquisition module specifically includes: Use total stations and GNSS equipment to determine the locations of ground control points; Obtain high-resolution images through drones for later orthophoto production; Use 3D laser scanners to obtain high-density point cloud data to reflect existing terrain and structural features.

[0020] Specifically, a total station is a multifunctional instrument used to measure distance, angle and elevation. It usually contains an electronic rangefinder, an electronic theodolite and a built-in data processor that can automatically record measurement data. A drone is an unmanned aircraft that can carry a variety of sensors (such as high-definition cameras, infrared cameras, etc.) for aerial photography. Drones are widely used in remote sensing, surveying and mapping and other fields. A 3D laser scanner is a device used to obtain 3D coordinate data on the surface of an object. It calculates the distance of each point on the surface of an object by emitting a laser beam and receiving the reflected signal, thereby obtaining high-density point cloud data.

[0021] S2, remove noise, classify, and use the photogrammetry software Pix4D to stitch images and generate orthophotos; In this example, the specific steps to remove noise, classify, and use the photogrammetry software Pix4D to stitch images and generate orthophotos are as follows: S2.1, pre-process the collected images to remove noise; Specifically, the preprocessing and noise removal process is as follows: import cv2 import numpy as np #Read the image image = cv2.imread('noisy_image.jpg', cv2.IMREAD_GRAYSCALE) # Apply median filter kernel_size = 3 # Choose a suitable kernel size denoised_image = cv2.medianBlur(image, kernel_size) # Display the original image and the denoised image cv2.imshow('Original Image', image) cv2.imshow('Denoised Image', denoised_image) cv2.waitKey(0) cv2.destroyAllWindows() # Save the denoised image cv2.imwrite('denoised_image.jpg', denoised_image) S2.2, use image processing algorithms to classify images; Specifically, the process of using image processing algorithms to classify images is as follows: import cv2 import mahotas from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import accuracy_score def extract_lbp_features(image_path): """Extract LBP features from the image.""" image = cv2.imread(image_path) gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) lbp = mahotas.features.lbp(gray_image, 8, 1) return lbp def prepare_training_data(training_data): """Extract features and labels from annotated data.""" features = [] labels = [] for data in training_data: feature, label = data features.append(feature) labels.append(label) return features, labels def train_svm_classifier(X_train, y_train): """Train a SVM classifier.""" scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) classifier = SVC(kernel='linear') classifier.fit(X_train_scaled, y_train) return classifier, scaler def evaluate_classifier(classifier, scaler, X_test, y_test): """Evaluate classifier performance.""" X_test_scaled = scaler.transform(X_test) predictions = classifier.predict(X_test_scaled) accuracy = accuracy_score(y_test, predictions) return accuracy, predictions def classify_new_image(classifier, scaler, new_image_path): """Use the trained classifier to classify new images.""" lbp_new = extract_lbp_features(new_image_path) scaled_lbp_new = scaler.transform([lbp_new]) predicted_class = classifier.predict(scaled_lbp_new) return predicted_class # Assume that there is already labeled training data training_data = [ (extract_lbp_features('images / grass_1.jpg'), 1), (extract_lbp_features('images / road_1.jpg'), 2), (extract_lbp_features('images / water_1.jpg'), 3), # More labeled data... ] # Extract features and labels from training data features, labels = prepare_training_data(training_data) # Divide the dataset X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42) # Train SVM classifier classifier, scaler = train_svm_classifier(X_train, y_train) # Evaluate classifier performance accuracy, _ = evaluate_classifier(classifier, scaler, X_test, y_test) print(f'Accuracy: {accuracy * 100:.2f}%') # Use the classifier to classify new images new_image_path = 'images / new_image.jpg' predicted_class=classify_new_image(classifier,scaler,new_image_path) print(f'Predicted class: {predicted_class[0]}') S2.3. Use Pix4D software to stitch the images and generate orthophotos.

[0022] In this example, the specific expression for image stitching by Pix4D software is: ; in, Indicates the final orthophoto map; Represents Pix4D software and its related processing flow; Indicates the Original image; Represents the process of image registration; S3, converting the processed point cloud data into a triangular mesh; In this example, the specific steps for converting the processed point cloud data into a triangular mesh are: S3.1, filtering the collected point cloud data to remove abnormal points; Specifically, the steps of filtering and removing outliers are as follows: Load raw point cloud data from storage devices; Compute the local density around each point and remove those points where the density is below a threshold: ; in, Represents point cloud data after statistical filtering; represents the number of nearest neighbors considered; represents the density threshold; represents the statistical filter function; Remove points that are too far away from their nearest neighbors; Fit a local plane and remove points that are inconsistent with the plane.

[0023] S3.2, use DBSCAN clustering algorithm to classify the point cloud and distinguish ground points from other non-ground points; In this example, the specific expression of the DBSCAN clustering algorithm for classifying point clouds is: Define the field: ; in, Indicate point fields; Represents a data point; represents another data point; Represents a dataset; Indicate point and Point The distance between Indicates the radius of the field; Define the core points: ; in, Indicate point Whether it is a core point; Indicates the minimum number of neighborhood points required to become a core point; Define the boundary points: ; in, Indicate point Whether it is a boundary point; Define noise points: ; in, Indicate point Whether it is a noise point; Expanding the cluster: ; in, Indicates the current cluster; Represents an extended cluster Contains point .

[0024] Specifically, DBSCAN is a density-based spatial clustering algorithm. The main difference between it and other clustering algorithms is that it can find clusters of any shape and does not require the number of clusters to be specified in advance. DBSCAN determines which areas belong to clusters and which areas are noise by measuring the local density of sample points. S3.3, use Delaunay triangulation to convert point cloud data into triangular mesh; S3.4. Optimize the generated triangular mesh to ensure the quality of the model.

[0025] S4. Use BIM software to import processed data and build a 3D real-life model; In this example, the specific steps of using BIM software to import processed data and build a 3D real scene model are as follows: S4.1. Import the processed point cloud data and orthophotos into BIM software; S4.2. In BIM software, point cloud data and orthophotos are combined to construct a three-dimensional real-scene model.

[0026] S5. Use the road design module in the BIM software to set route parameters, generate the road centerline, and carry out cross-section design; In this example, the specific steps of using the road design module in the BIM software to set route parameters, generate the road centerline, and carry out cross-section design are as follows: S5.1. Define road design parameters in BIM software; S5.2. Use the functions of BIM software to generate road centerlines based on terrain and design parameters; S5.3. Carry out cross-section design along the centerline of the road to define the cross-sectional shape and size of the road.

[0027] S6. Use 3D models to visually display design solutions to help all parties better understand and collaborate.

[0028] In this example, the specific steps for using a 3D model to visually display the design solution are as follows: S6.1. Render the 3D model in BIM software; S6.2. Export 3D model views and animation videos from different perspectives; S6.3 Organize stakeholder meetings and use the exported views or videos to demonstrate the design solution.

[0029] This embodiment also provides a road and bridge design system based on the BIM real-scene model, including a collector, a processor, and BIM software imported into the BIM software and capable of running on the processor, wherein the BIM software running on the processor implements any one of the above-mentioned road and bridge design methods based on the BIM real-scene model.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A road and bridge design method based on BIM real scene model, characterized in that: The following steps are involved: S1. Collect data in the project area through the data collection module; S2, remove noise, classify, and use the photogrammetry software Pix4D to stitch images and generate orthophotos; S3, converting the processed point cloud data into a triangular mesh; S4. Use BIM software to import processed data and build a 3D real-life model; S5. Use the road design module in the BIM software to set route parameters, generate the road centerline, and carry out cross-section design; S6. Use 3D models to visually display design solutions to help all parties better understand and collaborate.

2. The road and bridge design method based on BIM real scene model according to claim 1 is characterized in that: In S1, the data acquisition module specifically includes: Use total stations and GNSS equipment to determine the locations of ground control points; Obtain high-resolution images through drones for later orthophoto production; Use 3D laser scanners to obtain high-density point cloud data to reflect existing terrain and structural features.

3. The road and bridge design method based on BIM real scene model according to claim 1 is characterized in that: In S2, the noise is removed, classification is performed, and the photogrammetry software Pix4D is used to stitch the images and generate the orthophoto map. The specific steps are as follows: S2.1, pre-process the collected images to remove noise; S2.2, use image processing algorithms to classify images; S2.

3. Use Pix4D software to stitch the images and generate orthophotos.

4. The road and bridge design method based on BIM real scene model according to claim 1 is characterized in that: In S2.3, the specific expression for image splicing by Pix4D software is: ； in, Indicates the final orthophoto map; Represents Pix4D software and its related processing flow; Indicates the Original image; Represents the process of image registration.

5. The road and bridge design method based on BIM real scene model according to claim 1 is characterized in that: In S3, the specific steps of converting the processed point cloud data into a triangular mesh are: S3.1, filtering the collected point cloud data to remove abnormal points; S3.2, use DBSCAN clustering algorithm to classify the point cloud and distinguish ground points from other non-ground points; S3.3, use Delaunay triangulation to convert point cloud data into triangular mesh; S3.

4. Optimize the generated triangular mesh to ensure the quality of the model.

6. The road and bridge design method based on BIM real scene model according to claim 5 is characterized in that: In S3.2, the specific expression of the DBSCAN clustering algorithm for classifying point clouds is: Define the field: ； in, Indicate point fields; Represents a data point; represents another data point; Represents a dataset; Indicate point and Point The distance between Indicates the radius of the field; Define the core points: ； in, Indicate point Whether it is a core point; Indicates the minimum number of neighborhood points required to become a core point; Define the boundary points: ； in, Indicate point Whether it is a boundary point; Define noise points: ； in, Indicate point Whether it is a noise point; Expanding the cluster: ； in, Indicates the current cluster; Represents an extended cluster Contains point .

7. The road and bridge design method based on BIM real scene model according to claim 1 is characterized by: In S4, the specific steps of using BIM software to import the processed data and construct a three-dimensional real scene model are: S4.

1. Import the processed point cloud data and orthophotos into BIM software; S4.

2. In BIM software, point cloud data and orthophotos are combined to construct a three-dimensional real-scene model.

8. The road and bridge design method based on BIM real scene model according to claim 1 is characterized by: In S5, the specific steps of using the road design module in the BIM software to set route parameters, generate the road centerline, and carry out the cross-section design are as follows: S5.

1. Define road design parameters in BIM software; S5.

2. Use the functions of BIM software to generate road centerlines based on terrain and design parameters; S5.

3. Carry out cross-section design along the centerline of the road to define the cross-sectional shape and size of the road.

9. The road and bridge design method based on BIM real scene model according to claim 1 is characterized in that: In S6, the specific steps of using the three-dimensional model to intuitively display the design solution are as follows: S6.

1. Render the 3D model in BIM software; S6.

2. Export 3D model views and animation videos from different perspectives; S6.3 Organize stakeholder meetings and use the exported views or videos to demonstrate the design solution.

10. A road and bridge design system based on a BIM real-scene model, comprising a collector, a processor, and BIM software imported into the BIM software and capable of running on the processor, characterized in that: The BIM software running on the processor implements the road and bridge design method based on the BIM real-scene model as described in any one of claims 1 to 9.

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