Automated progress measurement using computer vision technology in UK construction

Bozorgzadeh, Abbas; Umar, Tariq

Abstract

A critical concern with the UK's construction project progress monitoring and control techniques is their dependency on data collection, which is time consuming and unproductive and may lead to various circumstances in managing projects. However, collecting and accurately analysing information from construction sites requires the development of technologies. As key artificial intelligence (AI) technology, computer vision (CV) is a powerful tool for big data analysis that can address the aforementioned challenges. This study explores the status of CV-based construction progress monitoring (CV-CPM) adoption and the main barriers to and incentives for its adoption within the UK construction sites. In this respect, after an extensive review of the literature covering the AI technology in construction management and the concept, function and usage of CV and its integration with construction progress monitoring, including its benefits and drivers and technical challenges, a questionnaire was administered to UK construction professionals to collect their perceptions. The study results indicated that construction practitioners were relatively aware of CV-CPM but lacked competencies and skills. CV-CPM has been perceived to be relatively better than the traditional approach. Implications such as the cost of implementation, lack of expertise and resistance to change were the major challenges in CV-CPM adoption. Meanwhile, technological development, decision making and competitiveness were classified as incentives for its adoption.

• Journal Article Type: Article
• Acceptance Date: Jul 5, 2023
• Online Publication Date: Jul 6, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Jul 6, 2023
• Publicly Available Date: Jul 7, 2024
• Journal: Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
• Electronic ISSN: 2397-8759
• Publisher: Thomas Telford (ICE Publishing)
• Peer Reviewed: Peer Reviewed
• Volume: 176
• Issue: 4
• Pages: 165-182
• DOI: https://doi.org/10.1680/jsmic.22.00026
• Keywords: Project Management, Construction Management, Artificial Intelligence, UN SDG 9
• Public URL: https://uwe-repository.worktribe.com/output/10907566
• Publisher URL: https://www.icevirtuallibrary.com/doi/abs/10.1680/jsmic.22.00026