Deep Learning in Construction: Review of Applications and Potential Avenues

Neural networks (NNs) have seen an increase in popularity in the last few years. As found in several papers, they outperformed other machine learning algorithms and have improved their applicability. NNs have shown potential when working with prediction and detection algorithms by addressing a large variety of problems, such as recognition of heavy machinery, project success prediction, workers’ pose assessment, price estimations, and project productivity estimation. To understand the future potential of NNs, we completed a bibliometric analysis of the existing literature, including publications since 2010. The areas in which NNs have been used in construction applications are categorized to understand their connections and underlying architectures. This work found a wider field of applications for NNs in the construction industry than originally known. New architectures such as transformer networks have not been explored fully in construction research but could lead to higher-performing networks. As far as the authors know, this is the first review to solely focus on construction, excluding areas such as structural engineering, indoor climate, occupancy modeling, and energy analysis. The limitations of NNs are discussed, and a path forward is proposed, which includes real-time models and examination of new architectures, which would allow the construction research to fully exploit the potential of NNs.