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State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring
The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project’s status based on algorithms. This article investigates...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103984/ https://www.ncbi.nlm.nih.gov/pubmed/35591189 http://dx.doi.org/10.3390/s22093497 |
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author | ElQasaby, Ahmed R. Alqahtani, Fahad K. Alheyf, Mohammed |
author_facet | ElQasaby, Ahmed R. Alqahtani, Fahad K. Alheyf, Mohammed |
author_sort | ElQasaby, Ahmed R. |
collection | PubMed |
description | The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project’s status based on algorithms. This article investigates the construction progress monitoring (CPM) domain, including knowledge gaps and future research direction. Synthesis literature was conducted on 3D sensing technologies in CPM depending on crucial factors, including the scanning environment, assessment level, and object recognition indicators’ performance. The scanning environment is important to determine the volume of data acquired and the applications conducted in the environment. The level of assessment between as-planned and as-built models is another crucial factor that could precisely help define the knowledge gaps in this domain. The performance of object recognition indicators is an essential factor in determining the quality of studies. Qualitative and statistical analyses for the latest studies are then conducted. The qualitative analysis showed a shortage of articles performed on 5D assessment. Then, statistical analysis is conducted using a meta-analytic regression model to determine the development of the performance of object recognition indicators. The meta-analytic model presented a good sign that the performance of those indicators is effective where [p-value is = 0.0003 < 0.05]. The study is also envisaged to evaluate the collected studies in prioritizing future works from the limitations within these studies. Finally, this is the first study to address ranking studies of 3D sensing technologies in the CPM domain integrated with BIM. |
format | Online Article Text |
id | pubmed-9103984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91039842022-05-14 State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring ElQasaby, Ahmed R. Alqahtani, Fahad K. Alheyf, Mohammed Sensors (Basel) Review The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project’s status based on algorithms. This article investigates the construction progress monitoring (CPM) domain, including knowledge gaps and future research direction. Synthesis literature was conducted on 3D sensing technologies in CPM depending on crucial factors, including the scanning environment, assessment level, and object recognition indicators’ performance. The scanning environment is important to determine the volume of data acquired and the applications conducted in the environment. The level of assessment between as-planned and as-built models is another crucial factor that could precisely help define the knowledge gaps in this domain. The performance of object recognition indicators is an essential factor in determining the quality of studies. Qualitative and statistical analyses for the latest studies are then conducted. The qualitative analysis showed a shortage of articles performed on 5D assessment. Then, statistical analysis is conducted using a meta-analytic regression model to determine the development of the performance of object recognition indicators. The meta-analytic model presented a good sign that the performance of those indicators is effective where [p-value is = 0.0003 < 0.05]. The study is also envisaged to evaluate the collected studies in prioritizing future works from the limitations within these studies. Finally, this is the first study to address ranking studies of 3D sensing technologies in the CPM domain integrated with BIM. MDPI 2022-05-04 /pmc/articles/PMC9103984/ /pubmed/35591189 http://dx.doi.org/10.3390/s22093497 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review ElQasaby, Ahmed R. Alqahtani, Fahad K. Alheyf, Mohammed State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title | State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title_full | State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title_fullStr | State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title_full_unstemmed | State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title_short | State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring |
title_sort | state of the art of bim integration with sensing technologies in construction progress monitoring |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103984/ https://www.ncbi.nlm.nih.gov/pubmed/35591189 http://dx.doi.org/10.3390/s22093497 |
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