Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities
Abstract This paper presents a comprehensive scientific analysis of research on automatic monitoring in construction projects. Through a methodical examination of 857 bibliographic records from three databases, we find important trends, novel themes, and key research areas in this field. Our finding...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-02-01
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| Series: | Human-Centric Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s44230-025-00089-3 |
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| author | Amir Hossein Dalir Zahra Pezeshki Mehdi Ravanshadnia Eugene Krinitsky Ildar Aidarovich Sultanguzin |
| author_facet | Amir Hossein Dalir Zahra Pezeshki Mehdi Ravanshadnia Eugene Krinitsky Ildar Aidarovich Sultanguzin |
| author_sort | Amir Hossein Dalir |
| collection | DOAJ |
| description | Abstract This paper presents a comprehensive scientific analysis of research on automatic monitoring in construction projects. Through a methodical examination of 857 bibliographic records from three databases, we find important trends, novel themes, and key research areas in this field. Our findings reveal that machine learning (ML), building information modelling (BIM), and deep learning are the most the most popular techniques for automatic monitoring. Furthermore, we identify construction technologies and artificial intelligence (AI) as the primary research foci. So, the study uncovers collaboration patterns among researchers and institutions, highlighting key players and their contributions, identifies research gaps and challenges, such as the need for integrating AI, big data, and cloud computing into construction project monitoring, and proposes future research directions to address these challenges and enhance the effectiveness of automatic monitoring systems. By providing a systematic review and insightful analysis, this study contributes to the advancement of construction project monitoring. It offers valuable insights for researchers, practitioners, and policymakers to foster innovation, improve project performance, and ensure sustainable construction practices. |
| format | Article |
| id | doaj-art-d7d0f1875c734ffab0aee08e5aff0b3f |
| institution | OA Journals |
| issn | 2667-1336 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj-art-d7d0f1875c734ffab0aee08e5aff0b3f2025-08-20T02:20:05ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362025-02-0151214310.1007/s44230-025-00089-3Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research ActivitiesAmir Hossein Dalir0Zahra Pezeshki1Mehdi Ravanshadnia2Eugene Krinitsky3Ildar Aidarovich Sultanguzin4Department of Civil, Science and Research Branch, Islamic Azad UniversityInstitute of Energy Efficiency and Hydrogen Technologies (IEEHT), Department of Industrial Heat Engineering Systems (IHPES), National Research University “Moscow Power Engineering Institute”Department of Civil, Science and Research Branch, Islamic Azad UniversityHeat and Mass Transfer Department, National Research University “Moscow Power Engineering Institute”Institute of Energy Efficiency and Hydrogen Technologies (IEEHT), Department of Industrial Heat Engineering Systems (IHPES), National Research University “Moscow Power Engineering Institute”Abstract This paper presents a comprehensive scientific analysis of research on automatic monitoring in construction projects. Through a methodical examination of 857 bibliographic records from three databases, we find important trends, novel themes, and key research areas in this field. Our findings reveal that machine learning (ML), building information modelling (BIM), and deep learning are the most the most popular techniques for automatic monitoring. Furthermore, we identify construction technologies and artificial intelligence (AI) as the primary research foci. So, the study uncovers collaboration patterns among researchers and institutions, highlighting key players and their contributions, identifies research gaps and challenges, such as the need for integrating AI, big data, and cloud computing into construction project monitoring, and proposes future research directions to address these challenges and enhance the effectiveness of automatic monitoring systems. By providing a systematic review and insightful analysis, this study contributes to the advancement of construction project monitoring. It offers valuable insights for researchers, practitioners, and policymakers to foster innovation, improve project performance, and ensure sustainable construction practices.https://doi.org/10.1007/s44230-025-00089-3Automatic monitoringDeep learningBIMBuilding information modellingBEPBIM execution planning |
| spellingShingle | Amir Hossein Dalir Zahra Pezeshki Mehdi Ravanshadnia Eugene Krinitsky Ildar Aidarovich Sultanguzin Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities Human-Centric Intelligent Systems Automatic monitoring Deep learning BIM Building information modelling BEP BIM execution planning |
| title | Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities |
| title_full | Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities |
| title_fullStr | Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities |
| title_full_unstemmed | Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities |
| title_short | Automatic Monitoring in Construction Projects: Scientometric Analysis and Visualization of Research Activities |
| title_sort | automatic monitoring in construction projects scientometric analysis and visualization of research activities |
| topic | Automatic monitoring Deep learning BIM Building information modelling BEP BIM execution planning |
| url | https://doi.org/10.1007/s44230-025-00089-3 |
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