A framework of structural health monitoring integrated with deep learning schemes to estimate the displacement of 3D LiDAR scanning on bridges

  • Wael A. Altabey orcid

    Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Article ID: 4028
DOI: https://doi.org/10.59400/sv4028
Keywords: structural health monitoring; LiDAR scanning; bridge displacement; deep learning; recurrent neural network; long short-term memory blocks

Abstract

In recent decades, the rapid development of transportation infrastructure safety, such as highways, bridges, and tunnels has greatly promoted the development of the regional economy. The structures with safety hazards and emergencies need continuous monitoring over time. The integrated artificial intelligence algorithms with sensor responses can provide real-time information for further analysis and decision-making for the transportation system, improving the circulation efficiency of the transportation network, ensuring the stability of road structures, and avoiding irreparable damage. This paper aims to develop an efficient and low-cost method to help detect early-stage transportation infrastructure damage through permanent or periodic monitoring. In this research, we used LiDAR scanning units (terrestrial LiDAR fixed on holders and movable units fixed on UAVs) integrated with a novel deep neural network (DNN) for structural monitoring of bridges based on the 3D mapping of bridge displacement compiled from LiDAR scanning over time. The monitoring model is based on a recurrent neural network with long short-term memory blocks (RNN-LSTM) since the LiDAR scanning datasets have a time-dependent and memory-dependent behavior. The response of the proposed DNN achieved a high accuracy rate, regression rate, and F-score equal to 96.43%, 93.77%, and 91.65%, respectively. A deep analysis of the confusion matrix and a side-by-side look at predicted and actual conditions highlight how well the model can tell apart different traditional methods to estimate the bridge displacement in literature. So, the data from LiDAR and DNN models can be combined to analyze the monitoring of transportation infrastructure.

Published
2026-06-30
How to Cite
Altabey, W. A. (2026). A framework of structural health monitoring integrated with deep learning schemes to estimate the displacement of 3D LiDAR scanning on bridges. Sound & Vibration, 60(4). https://doi.org/10.59400/sv4028
Issue
Vol. 60 No. 4 (2026): In Progress
Section
Article

Copyright (c) 2026 Wael A. Altabey

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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