Deep learning in the construction industry: A review of present status and future innovations

• Published in: Journal of Building Engineering Vol. 32; p. 101827
• Main Authors: Akinosho, Taofeek D., Oyedele, Lukumon O., Bilal, Muhammad, Ajayi, Anuoluwapo O., Delgado, Manuel Davila, Akinade, Olugbenga O., Ahmed, Ashraf A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• Subjects: Autoencoders; Construction industry; Convolutional neural networks; Deep learning; Generative adversarial networks; Deep learning; Generative adversarial networks; Convolutional neural networks; Autoencoders; Construction industry
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2020.101827

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed. •Deep learning and some of its famous architectures are presented.•Existing works for Deep Learning in the construction industry are discussed.•The Machine Learning/Deep Learning black box challenge is discussed.•Potential applications of Deep Learning in the construction industry are presented.•Pitfalls of adopting Deep Learning in the construction industry are pointed out.