A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

Abstract

Concrete stands as the most widely used construction material globally due to its versatility, encompassing applications ranging from pavement, multifloor structures, and bridges to dams. However, these concrete structures endure structural stress and require close monitoring to prevent accidents and ensure sustainability throughout their complete life cycle. In recent years, artificial intelligence (AI) and computer vision (CV) have demonstrated considerable potential in diverse applications within construction engineering, including structural health monitoring (SHM) and inspection processes such as crack and damage detection, as well as rebar exposure. While it is undeniable that CV and deep learning models are transforming the construction industry by offering robust solutions for complex scenarios, there remain numerous challenges pertinent to their applications that require attention. This paper aims to systematically and critically review the literature of the past decade on the application of deep learning models in the construction industry for SHM purposes in concrete structures. The review delves into proposed methodologies and technologies while identifying opportunities and challenges associated with these applications in practice. Additionally, the paper provides insights to bridge the gap between theory and application.