Productivity monitoring in building construction projects: a systematic review

Alaloul, W.S. and Alzubi, K.M. and Malkawi, A.B. and Al Salaheen, M. and Musarat, M.A. (2021) Productivity monitoring in building construction projects: a systematic review. Engineering, Construction and Architectural Management.

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Abstract

Purpose: The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity. Design/methodology/approach: This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria. Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately. Originality/value: This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques. © 2021, Emerald Publishing Limited.

Item Type: Article
Impact Factor: cited By 4
Uncontrolled Keywords: Construction; Construction industry; Data acquisition; Learning algorithms; Machine learning; Monitoring; Productivity; Project management, Automated monitoring; Bibliometric analysis; Construction management; Construction productivity; Construction projects; Design/methodology/approach; Monitoring construction; Monitoring techniques, Architectural design
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 06:45
Last Modified: 25 Mar 2022 06:45
URI: http://scholars.utp.edu.my/id/eprint/30388

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