Research On Visualization Of Highway Pavement Distress Based On BIM

With the rapid development of China’s highways,China’s highway transportation industry has shifted from a large-scale construction period to a refined maintenance period,and road maintenance has faced huge challenges.Traditional road maintenance methods have problems such as long cycles and high costs.By combining Building Information Modeling(BIM)with Geographic Information System(GIS),the informatization and visualization of road maintenance work can be realized.Applying road inspection information to road maintenance work can effectively improve the efficiency of road maintenance work.This article studies the visualization of highway pavement diseases based on BIM.In the experimental section of G85 Yinkun Expressway,road information is collected through data collection methods such as vehicle-mounted laser scanners,GPS positioning,drone image shooting and depth camera scanning.Through the most popular BIM modeling technology at present,road maintenance work has moved towards informatization,visualization and efficiency.The main research contents are as follows:(1)This article first proposes different data collection methods for pavement information and disease information,and introduces the data collection principles of each collection equipment;secondly,by comparing the advantages and disadvantages of several mainstream BIM software,combined with the research content of this article,it is determined to use Civil3 D and Revit as road BIM modeling software and disease BIM modeling software respectively.(2)A three-dimensional disease detection method for roads based on Random Sample Consensus(RANSAC)and a-shape is proposed.First,use the road elevation information and GPS positioning information collected by the detection vehicle to preliminarily determine the location of the disease;secondly,use a depth camera to collect detailed point cloud data of the disease and preprocess the point cloud;thirdly,use RANSAC algorithm to analyze the local reference surface of the disease and extract the disease point cloud;from time to time,Use the a-shape algorithm to reconstruct the extracted disease point cloud.After multiple experiments,it was found that when the compromise parameter α=0.030,the reconstructed disease shape is most realistic;finally,calculate the curvature of the reconstructed disease surface and verify the type of disease according to the positive and negative values of average curvature and Gaussian curvature.(3)A road point cloud extraction method based on drones is proposed.First,use drones to collect image data from experimental sections and preprocess data.Through multiple experiments and comparisons,it proposes an optimal point cloud simplification ratio that guarantees that the geometric features of the original point cloud remain unchanged while improving subsequent experimental efficiency;secondly,segmenting roads from road environments after processing point clouds,proposed a road extraction algorithm based on RANSAC plane segmentation and angle segmentation.This algorithm combines the advantages of RANSAC plane segmentation algorithm and ground segmentation algorithm based on angle segmentation.It can better separate road surface point clouds from both sides of roads and green belts in the middle.The segmented road surface point clouds greatly reduce boundary noise points.(4)Based on BIM road disease visualization modeling,information interaction between various models in actual road scenes is realized.First of all,due to actual design needs and analysis of characteristics of various coordinate systems,a method for converting geodetic coordinates into Mercator projection coordinates is proposed to represent the specific location of road models and disease models on Earth;secondly,Civil 3D and Revit are used respectively to realize road BIM model design And disease BIM model design;finally,through a threedimensional GIS system,integrate well-created refined road models with disease models according to GPS information to configure various models in actual road scenes to achieve actual section BIM Road disease visualization.