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

Abstract

The application of deep learning in construction has attracted increasing attention among researchers in recent years. In this review article, comprehensive scientometric analysis and critical review were 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 was performed on the collected themes of all the related publications. The results of the critical review indicate that deep convolution neural networks, you only look once, single-shot detectors, recurrent neural networks, residual neural networks, and fast region-based convolution neural networks have been the most widely used deep-learning methods in the construction industry. The most commonly addressed problems in the construction industry using deep-learning methods include classification of construction equipment, worker's safety helmet detection, ergonomics analysis, image enhancement, and feature extraction. This paper provides an in-depth understanding and big-picture overview of the existing literature along with the challenges and future direction of research on deep learning in construction.