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Productivity monitoring in building construction projects: a systematic review

Wesam Salah Alaloul (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskanda, Malaysia)
Khalid M. Alzubi (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskanda, Malaysia) (Department of Civil Engineering, Al-Balqa' Applied University, Amman, Jordan)
Ahmad B. Malkawi (Department of Civil Engineering, Al-Balqa' Applied University, Amman, Jordan)
Marsail Al Salaheen (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskanda, Malaysia) (Department of Civil Engineering, Al-Balqa' Applied University, Amman, Jordan)
Muhammad Ali Musarat (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskanda, Malaysia)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 23 June 2021

Issue publication date: 3 August 2022

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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.

Keywords

Acknowledgements

Data Availability Statement: All data, models, and code generated or used during the study appear in the submitted article.The authors would like to thank Universiti Teknologi PETRONAS (UTP) for the support provided for this research.

Citation

Alaloul, W.S., Alzubi, K.M., Malkawi, A.B., Al Salaheen, M. and Musarat, M.A. (2022), "Productivity monitoring in building construction projects: a systematic review", Engineering, Construction and Architectural Management, Vol. 29 No. 7, pp. 2760-2785. https://doi.org/10.1108/ECAM-03-2021-0211

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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