Structural health monitoring by using AI algorithms

Abstract

Bridges play an important role in civil engineering and its infrastructure asset management is done to evaluate the bridge in the different phases from construction to maintenance. The evaluation is done because any damage in the structure component is negatively affect the integrity, safety, and longevity of the bridges. In the real scenario, due to various conditions like varying temperature, road salt, heavy traffic, and abrasive forces the bridge is damaged. Therefore, it is monitored using the structure health monitoring system to enhance the lifecycle of the bridges. The main motive of the structure health monitoring system is to measure the bridge data, process it, and define the health structure of the bridge. However, the collected data is large, and processing in the manual process is time-consuming, high-cost, and human error-related. In order to overcome this challenge, artificial intelligence algorithms are deployed, which efficiently process the data and identify patterns in the dataset. In this paper, we will review the various structure health monitoring systems designed in the literature based on artificial intelligence algorithms. Besides that, a detailed description of various phases of the artificial intelligence algorithm is given to understand how it is used in the structure health monitoring system. Further, a critical analysis of various structure health monitoring systems is done based on various factors. Finally, based on critical analysis, open research challenges are defined, which helps other authors contribute to this field.