Big data analytics in online structural health monitoring

Abstract

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage.