Predictive Maintenance and Damage Mitigation in Bridge Structures using Integrated Acoustic Emission and Digital Image Correlation Techniques
Bilal Hussain, Samson Oluyomi Akintola
Current Journal of Applied Science and Technology · pp. 24–35 · Published 9 Oct 2024
10.9734/cjast/2024/v43i104434Abstract
Aim: To examine the predictive maintenance and damage mitigation in bridge structures using integrated acoustic emission and digital image correlation techniques. Problem Statement: Bridges are essential infrastructural facilities in any society. However, cases of loss of lives coupled with massive financial losses have been reported resulting from bridge failures. The acts of using bridge structures beyond their life expectancy and original design loads have been prevailing. Significance of Study: Dependable techniques are needed to monitor bridges and ensure their efficiency and safety because they are essential part of society’s infrastructure. Numerous non-destructive methods are present for local structural health monitoring whose mechanisms of operation are based on the principle of electromagnetic waves, mechanical waves and fiber optics. However, the advantages of acoustic emission and digital image correlation techniques have been found promising over other methods. Methodology: Recent relevant published articles in the area of predictive maintenance and damage mitigation in bridge structures using integrated acoustic emission and digital image correlation techniques were consulted. Discussion: In this critical review, predictive maintenance and damage mitigation in bridge structures using integrated acoustic emission and digital image correlation were discussed. Digital image correlation is a sensing technique having a full field, non-contact, optical measuring technique that utilizes digital cameras to evaluate the displacement, surface geometry and strain. Concrete and steel, being the most commonly utilized materials for building, were referenced and considered under integrated acoustic emission technique. The basic principle of DIC image tracking involves its coding with a matrix of natural numbers (such as using 0 to represent white areas and 100 to stand for black). The use of a suitable algorithm via specialized software is subsequently encouraged in order to identify the region with a matching distribution of pixel shades. In short, this review has presented the research gaps that should be bridged in the future studies. Conclusion: In conclusion, integrated acoustic emission and digital image correlation are effective techniques for predictive maintenance and damage mitigation in bridge structures.
Cited by 0
No indexed citations yet.
Related research
- Mining in the Age of Artificial Intelligence (AI): Harnessing Big Data for Environmental Stewardship — shares topic coverage
- Enhancing Smart Urban Mobility through Digital Twin-driven Autonomous Transportation and Predictive Maintenance — shares topic coverage
- AI-Enabled Digital Twin Framework for Predictive Maintenance and Performance Optimisation of Mechatronic Mechanical Systems — shares topic coverage
- Development of an AI-Driven Digital Twin Framework for Predictive Maintenance of Urban Infrastructure Systems — shares topic coverage
- AI-Driven Automation and Digital Twin Framework for Smart Transportation Systems and Predictive Infrastructure Maintenance — shares topic coverage
Article metrics
Real usage data collected on this platform.
9
Page views
0
PDF downloads
6
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.