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Productivity monitoring in building construction projects: a systematic review

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 er...

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Main Authors: Alaloul, W.S., Alzubi, K.M., Malkawi, A.B., Al Salaheen, M., Musarat, M.A.
Format: Article
Published: Emerald Group Holdings Ltd. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108456365&doi=10.1108%2fECAM-03-2021-0211&partnerID=40&md5=1191803de16f601485bc172cbe3563c5
http://eprints.utp.edu.my/29518/
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spelling my.utp.eprints.295182022-03-25T02:08:12Z Productivity monitoring in building construction projects: a systematic review Alaloul, W.S. Alzubi, K.M. Malkawi, A.B. Al Salaheen, M. Musarat, M.A. 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. Emerald Group Holdings Ltd. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108456365&doi=10.1108%2fECAM-03-2021-0211&partnerID=40&md5=1191803de16f601485bc172cbe3563c5 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 . http://eprints.utp.edu.my/29518/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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.
format Article
author Alaloul, W.S.
Alzubi, K.M.
Malkawi, A.B.
Al Salaheen, M.
Musarat, M.A.
spellingShingle Alaloul, W.S.
Alzubi, K.M.
Malkawi, A.B.
Al Salaheen, M.
Musarat, M.A.
Productivity monitoring in building construction projects: a systematic review
author_facet Alaloul, W.S.
Alzubi, K.M.
Malkawi, A.B.
Al Salaheen, M.
Musarat, M.A.
author_sort Alaloul, W.S.
title Productivity monitoring in building construction projects: a systematic review
title_short Productivity monitoring in building construction projects: a systematic review
title_full Productivity monitoring in building construction projects: a systematic review
title_fullStr Productivity monitoring in building construction projects: a systematic review
title_full_unstemmed Productivity monitoring in building construction projects: a systematic review
title_sort productivity monitoring in building construction projects: a systematic review
publisher Emerald Group Holdings Ltd.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108456365&doi=10.1108%2fECAM-03-2021-0211&partnerID=40&md5=1191803de16f601485bc172cbe3563c5
http://eprints.utp.edu.my/29518/
_version_ 1738656977118035968
score 13.211869

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