A Comprehensive Review on Asphalt Pavement Distress Detection and Assessment based on Artificial Intelligence

Abstract

Road transportation system facilitates the movement of people, goods and contributes to the national economy. This pavement network keeps on growing as years pass on. The failure of pavement may be due to heavy traffic, sunlight exposure, the seasonal changes causes unequal expansion and contraction of the surface, water intrusion and quality of construction material. Hence there is a high demand for effective pavement maintenance and rehabilitation in early stages. A lot of research is actively being conducted on detection and assessment of pavement distress. The manual approach depends on expert knowledge, consumes lot of time and it lacks the objectivity for quantification, then the automated distress detection using 3D laser technology uses hardware equipment which requires huge budget investment, hence AI based pavement distress detection and quantification methods are proposed as replacement. This paper presents a review of papers from the repositories like Google Scholar, Scopus, MDPI, ASCE (American Society of Civil Engineers) library, Hindawi of past eight years based on image processing and deep learning techniques that performs the task of detection and quantification of distress type, and advantages and disadvantages of the existing system are outlined and accounts for the interdisciplinary research to provide information for civil and the computer science enthusiast to know about the research challenges in this field needed for future research.