Current and future role of data fusion and machine learning in infrastructure health monitoring

Abstract

Rapid advances in infrastructure health monitoring and sensing technologies allow the monitoring of infrastructure assets continuously and in real-time 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 are 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.