Bridge Health Monitoring #SHM

 

Introduction
Bridges play a pivotal role in ensuring national mobility and supporting economic activity, making their structural health a critical concern. Structural health monitoring (SHM) systems are essential for maintaining bridge safety and durability. However, sensor faults, environmental noise, and transmission issues can compromise the quality of SHM data. To mitigate these challenges, anomaly detection methods are widely employed. Despite their popularity, there is no comprehensive evaluation comparing these techniques. This review addresses that gap by systematically analyzing recent research in SHM anomaly detection.

Motivation and Research Gap
Existing reviews on bridge SHM anomaly detection are limited in scope and often fail to synthesize comparative insights. Most studies overlook real-time performance and multivariate data analysis, which are crucial for practical deployment. Moreover, previous work rarely provides a structured framework to classify detection methods comprehensively. These limitations highlight the need for a systematic review that evaluates different approaches across multiple dimensions. Understanding these gaps motivates the development of a more thorough taxonomy and evaluation methodology.

Methodology
This systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, selected from eight reputable databases. Studies were evaluated using a four-dimensional taxonomy, including real-time capability, multivariate support, analysis domain, and detection method. Detection methods were further categorized into distance-based, predictive, and image-processing approaches. The review also assessed five key performance dimensions: robustness, scalability, real-world feasibility, interpretability, and data dependency. This methodology ensures a holistic comparison of contemporary anomaly detection techniques.

Taxonomy of Detection Methods
The review introduces a novel four-dimensional taxonomy to organize anomaly detection methods. Distance-based methods rely on similarity measures but are sensitive to environmental and dimensional variations. Predictive models balance performance with interpretability, making them practical in certain contexts. Image-processing techniques dominate the field, achieving high accuracy but requiring significant computational resources. Each category is evaluated in terms of real-time application and support for multivariate analysis. This taxonomy helps researchers identify suitable approaches for different SHM scenarios.

Comparative Evaluation
A detailed comparative evaluation revealed distinct strengths and weaknesses among methods. Image-processing methods are most frequently applied (22 studies) but face scalability challenges. Predictive models provide a balanced trade-off between interpretability and accuracy. Distance-based methods are less common due to sensitivity issues. Only 11 studies demonstrate real-time anomaly detection capabilities, while multivariate analysis remains underutilized. Time-domain signal processing dominates, whereas frequency and time-frequency methods are rarely applied, despite their potential advantages.

Challenges and Future Directions
Current SHM anomaly detection approaches face challenges in scalability, robustness, interpretability, and practical deployment. Existing models often lack adaptability and fail to handle multi-modal or uncertain data efficiently. Future research should focus on developing adaptive, interpretable frameworks suitable for real-world monitoring. Standardized evaluation protocols and cross-environment testing are necessary to validate performance. Combining predictive, distance-based, and image-processing strategies may enhance accuracy and reliability. The field needs innovation to ensure SHM systems remain practical and effective.

Conclusion
This review systematically examined recent studies in bridge SHM anomaly detection, highlighting trends, strengths, and gaps. Image-processing methods dominate but require computational optimization. Predictive and distance-based approaches offer trade-offs between accuracy and interpretability. Real-time and multivariate analysis remain underexplored. Future work should focus on adaptive, scalable, and interpretable models with multi-modal capabilities. Implementing such frameworks will enhance bridge safety and ensure reliable monitoring across diverse environments.

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