Automated Detection and Analysis of Minor Deformations in Flat Walls Due to Railway Vibrations Using LiDAR and Machine Learning

2024-06-24

International Conference on Computing Communication and Networking Technologies

AI Summary

To address the challenge of automated monitoring of minute deformations in adjacent walls induced by railway-induced vibrations, this study proposes an end-to-end structural health assessment framework integrating high-density terrestrial laser scanning (TLS) with machine learning. Methodologically, it innovatively combines TLS point cloud segmentation (RANSAC plus region-growing), geometry-adaptive wall extraction, automatic ground-reference alignment, and vibration-response-driven deformation modeling—enabling label-free identification of planar wall segments and millimeter-level quantitative deformation analysis. Field validation at the RGIPT campus demonstrated accurate detection of maximum displacements of 7–8 cm (mean: 3–4 cm) in wall sections proximal to the railway, while negligible deformation was observed at distal locations, confirming millimeter-scale accuracy and engineering applicability. This approach significantly advances automated, high-precision, non-destructive health assessment of existing structures under dynamic loading conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Segmentation
• Machine Learning: Structured Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and $mathrm{AI} / mathrm{ML}$ techniques to collect and analyze data. The scan data is processed into a detailed point cloud, which is segmented to distinguish ground points, trees, buildings, and other objects. The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation. Findings from the study, conducted at the RGIPT campus, reveal significant deformations in walls close to the railway corridor, with the highest deformations ranging from 7 to $mathbf{8 c m}$ and an average of 3 to 4 cm. In contrast, walls further from the corridor show negligible deformations. The developed automated process for feature extraction and deformation monitoring demonstrates potential for structural health monitoring. By integrating LiDAR data with machine learning, the methodology provides an efficient system for identifying and analyzing structural deformations, highlighting the importance of continuous monitoring for ensuring structural integrity and public safety in urban infrastructure. This approach represents a substantial advancement in automated feature extraction and deformation analysis, contributing to more effective management of urban infrastructure.

Problem

• Automated Detection
• Railway Proximity
• Structural Health Monitoring

Innovation

• Laser Radar Scanning
• Machine Learning
• Automated Deformation Detection