Current and future role of data fusion and machine learning in infrastructure health monitoring
Rapid advances in infrastructure health monitoring and sensing technologies allow monitoring of infrastructure assets continuously and in realtime throughout their life span. However, smart and automated techniques for decision making (e.g. maintaining or improving infrastructure performance) are in their infancy. The revolution in new sensing capabilities has led to rapidly increasing volumes of data, which makes traditional data analysis techniques inadequate. Adoption of Big Data (BD) analytics and Artificial Intelligence (AI) techniques is urgently needed to automatically integrate information from multiple sensors, extract knowledge and inform decision-making. The objective of this work was to provide a state-of-the-art review of data fusion and machine learning techniques applied to infrastructure health monitoring. In contrast to the previously published, related review articles, the focus of this review is on the techniques implemented by machine learning algorithms, their applications at each data processing stage in a machine learning framework, and their advantages and limitations. Finally, challenges and future trends for machine learning techniques and infrastructure health monitoring systems are discussed. As a review, this paper offers meaningful suggestions for employing data fusion and machine learning techniques in infrastructure health monitoring.
Philip Leverhulme Prize (PLP-2019-017)
- Architecture, Building and Civil Engineering
Published inStructure and Infrastructure Engineering
PublisherTaylor & Francis
- VoR (Version of Record)
Rights holder© The Author(s)
Publisher statementThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.