Version 1
: Received: 20 September 2024 / Approved: 20 September 2024 / Online: 21 September 2024 (06:13:34 CEST)
How to cite:
Shah Mansouri, T.; Lubarsky, G.; Finlay, D.; McLaughlin, J. Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network. Preprints2024, 2024091639. https://doi.org/10.20944/preprints202409.1639.v1
Shah Mansouri, T.; Lubarsky, G.; Finlay, D.; McLaughlin, J. Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network. Preprints 2024, 2024091639. https://doi.org/10.20944/preprints202409.1639.v1
Shah Mansouri, T.; Lubarsky, G.; Finlay, D.; McLaughlin, J. Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network. Preprints2024, 2024091639. https://doi.org/10.20944/preprints202409.1639.v1
APA Style
Shah Mansouri, T., Lubarsky, G., Finlay, D., & McLaughlin, J. (2024). Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network. Preprints. https://doi.org/10.20944/preprints202409.1639.v1
Chicago/Turabian Style
Shah Mansouri, T., Dewar Finlay and James McLaughlin. 2024 "Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network" Preprints. https://doi.org/10.20944/preprints202409.1639.v1
Abstract
Conventional approaches in Structural Health Monitoring (SHM) tend to be complex, destructive, and time-intensive. Additionally, they often require a large number of sensors to thoroughly assess structural integrity. In this study, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localization of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth communication. The framework is validated through empirical data collected from 3D carbon fiber-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. Strain data from five sensors were analyzed using a combination of Shewhart charts, Grubbs Test (GT), and a hierarchical clustering algorithm, specifically designed to evaluate and classify fractures. Our ML-based framework offers a streamlined and efficient alternative to traditional laboratory procedures, delivering precise crack detection with significant potential for applications in the composites industry.
Keywords
Structural Health Monitoring; Machine Learning; Bluetooth Low Energy Sensor; Shewhart Chart; Grubbs Test; Hierarchical Clustering; 3D Composite
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
