Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review
The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically adv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/7/1037 |
| _version_ | 1827619097044582400 |
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| author | Muhammad Sami Ur Rehman Muhammad Tariq Shafiq Fahim Ullah |
| author_facet | Muhammad Sami Ur Rehman Muhammad Tariq Shafiq Fahim Ullah |
| author_sort | Muhammad Sami Ur Rehman |
| collection | DOAJ |
| description | The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices. |
| first_indexed | 2024-03-09T10:21:26Z |
| format | Article |
| id | doaj.art-a180396266e545479e742122b3b6f6cf |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T10:21:26Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-a180396266e545479e742122b3b6f6cf2023-12-01T21:58:49ZengMDPI AGBuildings2075-53092022-07-01127103710.3390/buildings12071037Automated Computer Vision-Based Construction Progress Monitoring: A Systematic ReviewMuhammad Sami Ur Rehman0Muhammad Tariq Shafiq1Fahim Ullah2Department of Architectural Engineering, College of Engineering, United Arab Emirates University, Al Ain, Abu Dhabi 15551, United Arab EmiratesDepartment of Architectural Engineering, College of Engineering, United Arab Emirates University, Al Ain, Abu Dhabi 15551, United Arab EmiratesSchool of Surveying and Built Environment, University of Southern Queensland, Springfield, QLD 4300, AustraliaThe progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices.https://www.mdpi.com/2075-5309/12/7/1037construction progress monitoringprocess assessmentcomputer visionvision-based automationautomated progress monitoringsystematic review |
| spellingShingle | Muhammad Sami Ur Rehman Muhammad Tariq Shafiq Fahim Ullah Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review Buildings construction progress monitoring process assessment computer vision vision-based automation automated progress monitoring systematic review |
| title | Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review |
| title_full | Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review |
| title_fullStr | Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review |
| title_full_unstemmed | Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review |
| title_short | Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review |
| title_sort | automated computer vision based construction progress monitoring a systematic review |
| topic | construction progress monitoring process assessment computer vision vision-based automation automated progress monitoring systematic review |
| url | https://www.mdpi.com/2075-5309/12/7/1037 |
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