Scientometric analysis and critical review on the application of deep learning in the construction industry.

Deep learning in construction industry has attracted increasing attention among researchers. In this article, scientometric analysis and critical review are performed to analyze the state-of-the-art literature on the application of deep learning in construction. This research used the science mapping method to quantitatively and systematically analyze 423 related bibliographic records retrieved from the Scopus database, and further, a critical review is performed on collected themes. The results of the critical review indicate that deep convolution neural networks, you only look once, residual neural networks and fast region-based convolution neural networks have been the most widely used methods in the construction industry. The most commonly addressed problems in the construction industry using deep learning methods includes classification of construction equipment, worker's safety helmet detection and ergonomics analysis. This paper provides an in-depth understanding of the existing literature along with the challenges and future direction of deep learning in the construction industry.