A REVIEW ON THE IDENTIFICATION OF CRACKING BEHAVIOR OF RC STRUCTURES USING ELASTIC WAVE AND DEEP LEARNING-BASED APPROACH

Abstract

Structural health monitoring and crack detections in RC structures is one of the prime requirements for serviceability of civil engineering infrastructures as well as to avoid mega economic and people loss. The current article provides a detailed review of cracks detection in RC structures based on SHM techniques. Among them, different types of advance and classical techniques such as elastic waves and deep learning methods were thoroughly reviewed to analyze their applications in SHM of civil engineering infrastructures in recent research. Elastic waves have further been classified into two approaches namely acoustic emission (AE) and guided wave method. The comparison was made between the classical method of crack detections in several RC structures and the recent advanced method called the Deep Learning approach. Based on the case studies and recent literature work thorough observations were made and concluded that the Deep Learning approach is most suited in the field to detect cracks initiations in civil engineering infrastructures by using the advanced computer technology in terms of the programming language of Machine learning and hence proved up to 97% accurate and reliable method. Based on the concrete evidence of smart applications of Deep Learning tools from the literature review in the detection of cracks in RC structure, few future research directions were also recommended to enhance the scope of SHM in the field of civil engineering infrastructures.