Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review
The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to pres...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/12/2944 |
| _version_ | 1797381767123959808 |
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| author | Sergio Zabala-Vargas María Jaimes-Quintanilla Miguel Hernán Jimenez-Barrera |
| author_facet | Sergio Zabala-Vargas María Jaimes-Quintanilla Miguel Hernán Jimenez-Barrera |
| author_sort | Sergio Zabala-Vargas |
| collection | DOAJ |
| description | The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to present a systematic review of the literature on the use of these technologies in the architecture, engineering, and construction industry. A methodology of collection, purification, evaluation, bibliometric, and categorical analysis was used. A total of 224 articles were found, which, using the PRISMA method, finally generated 57 articles. The categorical analysis focused on determining the technologies used, the most common methodologies, the most-discussed project management areas, and the contributions to the AEC industry. The review found that there is international leadership by China, the United States, and the United Kingdom. The type of research most used is quantitative. The areas of knowledge where ETs are most used are Cost, Quality, Time, and Scope. Finally, among the most outstanding contributions are as follows: prediction in the development of projects, the identification of critical factors, the detailed identification of risks, the optimization of planning, the automation of tasks, and the increase in efficiency; all of these to facilitate management decision making. |
| first_indexed | 2024-03-08T20:56:21Z |
| format | Article |
| id | doaj.art-10905c61cf564d479b15e13460493ab1 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-08T20:56:21Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-10905c61cf564d479b15e13460493ab12023-12-22T13:57:54ZengMDPI AGBuildings2075-53092023-11-011312294410.3390/buildings13122944Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic ReviewSergio Zabala-Vargas0María Jaimes-Quintanilla1Miguel Hernán Jimenez-Barrera2Especialización en Gerencia de Proyectos/Ingeniería Industrial, Rectoría Virtual, Corporación Universitaria Minuto de Dios, Bogotá D.C 111021, ColombiaEspecialización en Gerencia de Proyectos/Ingeniería Industrial, Rectoría Virtual, Corporación Universitaria Minuto de Dios, Bogotá D.C 111021, ColombiaEspecialización en Gerencia de Proyectos/Ingeniería Industrial, Rectoría Virtual, Corporación Universitaria Minuto de Dios, Bogotá D.C 111021, ColombiaThe high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to present a systematic review of the literature on the use of these technologies in the architecture, engineering, and construction industry. A methodology of collection, purification, evaluation, bibliometric, and categorical analysis was used. A total of 224 articles were found, which, using the PRISMA method, finally generated 57 articles. The categorical analysis focused on determining the technologies used, the most common methodologies, the most-discussed project management areas, and the contributions to the AEC industry. The review found that there is international leadership by China, the United States, and the United Kingdom. The type of research most used is quantitative. The areas of knowledge where ETs are most used are Cost, Quality, Time, and Scope. Finally, among the most outstanding contributions are as follows: prediction in the development of projects, the identification of critical factors, the detailed identification of risks, the optimization of planning, the automation of tasks, and the increase in efficiency; all of these to facilitate management decision making.https://www.mdpi.com/2075-5309/13/12/2944artificial intelligencearchitecture, engineering, and construction (AEC) industrybig datadata sciencedigital twinsInternet of Things |
| spellingShingle | Sergio Zabala-Vargas María Jaimes-Quintanilla Miguel Hernán Jimenez-Barrera Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review Buildings artificial intelligence architecture, engineering, and construction (AEC) industry big data data science digital twins Internet of Things |
| title | Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review |
| title_full | Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review |
| title_fullStr | Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review |
| title_full_unstemmed | Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review |
| title_short | Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review |
| title_sort | big data data science and artificial intelligence for project management in the architecture engineering and construction industry a systematic review |
| topic | artificial intelligence architecture, engineering, and construction (AEC) industry big data data science digital twins Internet of Things |
| url | https://www.mdpi.com/2075-5309/13/12/2944 |
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