BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study
Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Infor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/14/2451 |
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| author | Heap-Yih Chong Xinyi Yang Cheng Siew Goh Yan Luo |
| author_facet | Heap-Yih Chong Xinyi Yang Cheng Siew Goh Yan Luo |
| author_sort | Heap-Yih Chong |
| collection | DOAJ |
| description | Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. |
| format | Article |
| id | doaj-art-118921cb33d04e11aec5ac28ec6cba8f |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-118921cb33d04e11aec5ac28ec6cba8f2025-08-20T03:58:31ZengMDPI AGBuildings2075-53092025-07-011514245110.3390/buildings15142451BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case StudyHeap-Yih Chong0Xinyi Yang1Cheng Siew Goh2Yan Luo3School of Engineering Audit, Nanjing Audit University, Nanjing 211815, ChinaSchool of Engineering Audit, Nanjing Audit University, Nanjing 211815, ChinaDepartment of Architecture and Built Environment, Northumbria University, Newcastle NE1 8ST, UKSchool of Engineering Audit, Nanjing Audit University, Nanjing 211815, ChinaTraditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption.https://www.mdpi.com/2075-5309/15/14/2451building information modeling (BIM)artificial intelligence (AI)dynamic schedule managementconstruction project management |
| spellingShingle | Heap-Yih Chong Xinyi Yang Cheng Siew Goh Yan Luo BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study Buildings building information modeling (BIM) artificial intelligence (AI) dynamic schedule management construction project management |
| title | BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study |
| title_full | BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study |
| title_fullStr | BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study |
| title_full_unstemmed | BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study |
| title_short | BIM and AI Integration for Dynamic Schedule Management: A Practical Framework and Case Study |
| title_sort | bim and ai integration for dynamic schedule management a practical framework and case study |
| topic | building information modeling (BIM) artificial intelligence (AI) dynamic schedule management construction project management |
| url | https://www.mdpi.com/2075-5309/15/14/2451 |
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