Laser scanning-based diagnostics of highway bridge crossings

Abstract

Highway bridge structures require regular condition assessment to ensure traffic safety and support timely maintenance planning. This study aimed to evaluate the effectiveness of laser scanning technology for the inspection of a highway bridge crossing and its approaches. The methodology combined a DJI Matrice 350 RTK unmanned aerial vehicle, a handheld laser scanner, LiDAR technology, RTK positioning, and photogrammetric processing of spatial data. As a result, an orthophoto map, a dense point cloud, and a three-dimensional digital model of the bridge were generated. Automated detection and classification of pavement defects, including transverse, longitudinal, thermal, and local cracks, were performed with the determination of their geometric characteristics and spatial coordinates. The generated digital datasets enabled the creation of a georeferenced defect database suitable for long-term structural condition monitoring. The findings demonstrate that integrating aerial and terrestrial laser scanning significantly improves the accuracy and objectivity of bridge inspections, reduces survey time, and provides a reliable digital foundation for bridge asset management and lifecycle monitoring.