Data-driven monitoring and predictive maintenance for engineering structures : technologies, implementation challenges, and future directions

Abstract

Estimating engineering structures’ health conditions and predicting their future behaviors are fundamental problems for a city’s safe and efficient operations. Data-driven solutions estimate the health conditions using statistical models generated from measurement data. They have attracted growing interest recently because advances in information and communication technologies (ICTs) have enabled numerous real-world measurement data, and the flourishing big data community has provided enormous state-of-the-art data analytics algorithms. Nevertheless, most existing studies remain in numerical simulation while neglecting their real-world implementation, restricting the extensive development of data-driven methods. In this survey, we provide a structural overview of the past decade’s data-driven structural health monitoring (SHM) systems and algorithms from the perspective of real-world implementation. Specifically, we cover various aspects of the design and implementation of monitoring systems, including sensing technologies, communication technologies, and processing software. In addition, we classify the used data sources and statistical models with fined details. Under the proposed taxonomy, their limitations and advantages are thoroughly discussed. Based on our insights into existing studies, we clarify two major implementation challenges: 1) the digressive performance in the real-world environment and 2) inefficient computing systems for real-time data analytics. Possible solutions are then proposed to mitigate those challenges and promote the implementation of data-driven methods. Finally, we raise our outlook on future trends and suggest promising directions for further investigation.