Big Data-Based Structural Health Monitoring of Concrete structures—A Perspective Review

Abstract

Structural health monitoring (SHM) comes with the real data that is collected and calculated from the structures, which is not possible by mere visual inspection. The working principle of SHM is basically collecting essential data, including strain, temperature conditions, moisture content, etc., which are further transformed into digital data for the interpretation. It serves a tool in the synchronizing of the collected data, also with the responsibility of making it available at all times. It stores the data for the meticulous research and analysis. SHM is enabled with the leading-edge technology of big data, which has an added advantage of collecting and storing extensive data related to the structures. Different types of sensors are used for collecting the data; hence, with these data, real-time information about the structures is extracted. The elaborate process involves the big data, for exploring and analyzing a variety of datasets with different patterns to determine damages, and defects lying under the structures. Hence, health monitoring of the existing structures is possible and with high precision and information. SHM is a continuous process of measurement, collection, processing, and storage of massive amounts of data of the existing structures for diagnosing structural health. Hence, in this paper, various examples of the contemporary structural monitoring system and the ongoing efforts being made in the big data-based structural health monitoring of concrete structures has been presented. The work is dedicated to present the different monitoring sensors of structures to evaluate damage, defects, and serviceability by taking account of available literature.