Structural health monitoring based on three-dimensional point cloud technology: A systematic review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: Structural Health Monitoring ensures the safety and longevity of critical infrastructure. Traditional methods face limitations such as high subjectivity and incomplete coverage. Recent advances in three-dimensional point cloud technology offer an intelligent solution due to its non-contact data acquisition, high accuracy, and full-field coverage. This paper reviews the latest research, methodologies, and applications of 3D point cloud technology in SHM, based on 223 studies published from 2010 to 2024. It outlines the entire process, from data acquisition to modeling and visualization, and compares six core methods: geometric morphology analysis, multi-temporal differential analysis, feature extraction, mapping, machine learning, and deep learning. Case studies show successful applications in damage detection, deformation monitoring, and health assessment of structures like bridges, tunnels, and historical buildings. Future efforts should optimize intelligent algorithms to shift 3D point cloud technology from "single technological breakthrough" to "systemic capability development," supporting automated structural health monitoring and resilient urban development.
Source: Results in Engineering
Authors: Yanzong Zhang, Guibo Nie, Duozhi Wang
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4412774146