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Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review
Omar S Sonbul1, Muhammad Rashid1
1Computer Engineering Department, Umm Al Qura University, Makkah 21955, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 13, 2023
Summary
This systematic literature review analyzes feature extraction techniques and machine learning algorithms for structural health monitoring (SHM) of bridges. It identifies 45 recent practices and 25 techniques, offering guidance for SHM system design.
Area of Science:
- Engineering
- Computer Science
Background:
- Structural Health Monitoring (SHM) is crucial for infrastructure analysis, with feature extraction and pattern recognition being key stages.
- Selecting appropriate signal processing and machine learning techniques for SHM is vital but challenging due to numerous options and varying system requirements.
Approach:
- A Systematic Literature Review (SLR) was conducted, analyzing 45 research practices from 2016-2023 on SHM for bridges.
- Research studies were classified into supervised learning, neural networks, and hybrid approaches for in-depth analysis.
- The review also examined feature extraction techniques, analytical approaches, operational functionality, and system implementation.
Key Points:
- Identified 25 distinct feature extraction techniques used in bridge SHM.
- Analyzed the application of various machine learning algorithms within different SHM system categories.
- Explored design considerations including classification/regression, diagnosis/prognosis, and data-driven/model-based implementations.
Conclusions:
- This SLR provides a comprehensive overview and comparative study of machine learning in bridge SHM, addressing a gap in existing literature.
- The findings aim to guide researchers and practitioners in selecting optimal feature extraction methods and machine learning algorithms for specific SHM system needs.
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