A Data-Driven Framework for Structural Crack Identification in 3D Mobile LiDAR Scans Using Deep Learning Classification Models

Abstract. In cold-climate regions like Canada, pavement infrastructure deteriorates rapidly due to extreme freeze-thaw cycles and heavy use of de-icing salts, accelerating the formation of structural cracks and imposing a financial burden on municipal budgets. By providing an automated LiDAR (Light Detection and Ranging)-based detection framework, this research offers a cost-effective, high-precision monitoring tool that enables early intervention, reducing long-term repair costs and enhancing road safety across Canadian provincial networks. This study evaluates the performance of Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs) for crack classification in a multi-dimensional feature space. We propose integrating geometric height (H) with a novel set of radiometric indices, including the Normalized Difference Intensity Index (NDII) and the Green Ratio (GR), to enhance classification stability. Results demonstrate that both SVM and MLP achieved comparable accuracies of 87% and 86%, respectively, in low-dimensional feature spaces. A critical analysis of the MLP learning curves reveals that the introduction of NDII acted as a numerical stabilizer, mitigating the oscillations caused by raw brightness fluctuations. Furthermore, the study identifies an information ceiling, as architectural expansion of the MLP improved convergence stability but did not exceed the 87% accuracy threshold. These findings provide a robust framework for automated road maintenance using stabilized radiometric features in LiDAR-based distress identification.