Machine learning in construction: From shallow to deep learning

Abstract

The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial intelligence, playing a pivotal role in the process of making construction “smart”. The application of machine learning in construction has the potential to open up an array of opportunities such as site supervision, automatic detection, and intelligent maintenance. However, the implementation of machine learning faces a range of challenges due to the difficulties in acquiring labeled data, especially when applied in a highly complex construction site environment. This paper reviews the history of machine learning development from shallow to deep learning and its applications in construction. The strengths and weaknesses of machine learning technology in construction have been analyzed in order to foresee the future direction of machine learning applications in this sphere. Furthermore, this paper presents suggestions which may benefit researchers in terms of combining specific knowledge domains in construction with machine learning algorithms so as to develop dedicated deep network models for the industry.