Roadway Disease Identification Based on RANSAC and A-Shape in the Context of BIM Technology

Most road surfaces in China inevitably experience deformation, cracks, or the formation of potholes due to long-term erosion by natural climate and pressure caused by vehicle driving. Traditional road maintenance usually uses manual disease identification and safety estimation. Traditional disease identification methods are not only inefficient, but also have certain safety hazards. In order to achieve safe and efficient road disease identification, an intelligent road disease identification model was constructed using BIM technology as the foundation, and random sample consensus algorithm and convex hull extension algorithm were used. After experiments, it was found that the proposed model achieved an average road disease recognition accuracy of 91.03% and 87.97% on two different datasets, which was 2.24% and 3.26% higher than the average accuracy of the control model, respectively. At the same time, based on the elevation analysis results of the collected data, it is found that there is a connection between pothole diseases and package diseases, providing new ideas for road maintenance and management. Based on the above experimental results, it can be concluded that the model proposed in the study has better accuracy in identifying road diseases than the control model, therefore the proposed model has certain feasibility.