Research and applications of artificial neural network in pavement engineering: A state-of-the-art review

Abstract

Given the great advancements in soft computing and data science, artificial neural network (ANN) has been explored and applied to handle complicated problems in the field of pavement engineering. This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering, including pavement design, construction, inspection and monitoring, and maintenance. This study focused on the papers published over the last three decades, especially the studies conducted since 2013. Through literature retrieval, a total of 683 papers in this field were identified, among which 143 papers were selected for an in-depth review. The ANN architectures used in these studies mainly included multi-layer perceptron neural network (MLPNN), convolutional neural network (CNN) and recurrent neural network (RNN) for processing one-dimensional data, two-dimensional data and time-series data. CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor. While ANN has been proved to be an effective tool for pavement material design, cost analysis, defect detection and maintenance planning, it is facing huge challenges in terms of data collection, parameter optimization, model transferability and low-cost data annotation. More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities. • Frontiers of artificial neural network (ANN) in pavement design, construction, inspection and maintenance were reviewed. • Three mainstream ANN architectures for different application scenarios were summarized. • Five research challenges and prospects of ANN application in pavement engineering were analyzed. • Standardized literature search and classification methods were implemented.