Year: 2025
Type: article
Abstract: Continuous spalling exposure can weaken the performance of structures. Therefore, the development of methods for detecting wall spall damage remains essential in the field of Structural Health Monitoring. Currently, researchers mainly rely on 2D information for spall detection and predominantly use manual data collection methods in the complex environment of residential buildings, which are usually inefficient. To address this challenge, an automated 3D image processing system for wall spalls is proposed in this study. First, UGV path planning was performed in order to collect information about the surrounding environmental defects. Second, to address the shortcomings of RandLA-Net, a dynamic enhanced dual-branch structure is established based on which consistency constraints are introduced, a lightweight attention module is added, and the loss function is optimized in order to enhance the ability of the model in extracting feature information of the point cloud. Finally, spalls are quantitatively evaluated to determine the damage to buildings. The results show that the Randla-Spall achieves 94.71% Recall and 84.20% mIoU on the test set, improved by 4.25% and 5.37%. An integrated process using a lightweight device is achieved in this study, which is capable of efficiently extracting and quantifying spalling defects and provides valuable references for SHM.
Source: Applied Sciences
DOI: 10.3390/app15042140
Authors: Junjie Wang, Yunfang Pang, Xinyu Teng
Institution: Ocean University of China
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G6855542605
OpenAlex ID: W4407710165