Machine learning method for As-Is tunnel information model reconstruction

The maintenance of aging infrastructure requires advanced tools for efficient inspection and planning. This paper presents a methodology for segmenting and classifying point clouds of road tunnels to streamline maintenance operations. Processing large datasets, such as those generated by laser surveys, poses significant challenges without appropriate IT solutions. Data from four Italian tunnels were divided into segments and clustered into homogeneous groups. These clusters were manually classified to create a labeled dataset for training machine learning algorithms. The classified points, representing elements such as lining, road surfaces, and equipment, were then used to generate a 3D mesh model. By sharing the results in the OpenBIM format, the method facilitates seamless data exchange among infrastructure maintenance professionals, improving efficiency in planning and execution.