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RISRailway-Fastener Point Cloud Segmentation and Damage Quantification Based on Deep Learning and Synthetic Data Augmentation
Publisher: American Society of Civil Engineers
Abstract: Accurate detection and quantification of damage to railway fasteners are crucial for ensuring railway safety. The spatial damage defects caused by the complex shape of fasteners and the problem of data imbalance in actual ...
Multimodal Fusion Network for Crack Segmentation with Modified U-Net and Transfer Learning–Based MobileNetV2
Publisher: American Society of Civil Engineers
Abstract: This study introduces a state-of-the-art methodology for addressing crack segmentation challenges in structure health monitoring, a crucial concern in infrastructure maintenance. The main objective is to enhance real-time ...
Quantification and Evaluation of Roughness of Initial Support Using Terrestrial Laser Scanning
Publisher: American Society of Civil Engineers
Abstract: The control of initial support roughness is crucial to the structural waterproofing, durability, and safety of the drilling and blasting tunnel. The existing manual-based measurement methods and evaluation systems have ...
LiDAR-Simulated Multimodal and Self-Supervised Contrastive Digital Twin Approach for Probabilistic Point Cloud Generation of Rail Fasteners
Publisher: American Society of Civil Engineers
Abstract: This study presents a novel deep-learning framework designed to efficiently generate high-fidelity three-dimensional (3D) point clouds of rail fasteners. The proposed method overcomes limitations associated with traditional ...