Al-Sinan, M.A.; Bubshait, A.A.; Aljaroudi, Z. Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint. Buildings2024, 14, 934.
Al-Sinan, M.A.; Bubshait, A.A.; Aljaroudi, Z. Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint. Buildings 2024, 14, 934.
Al-Sinan, M.A.; Bubshait, A.A.; Aljaroudi, Z. Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint. Buildings2024, 14, 934.
Al-Sinan, M.A.; Bubshait, A.A.; Aljaroudi, Z. Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint. Buildings 2024, 14, 934.
Abstract
The fast-paced evolution of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), large language model (LLM) applications, and generative pre-trained transformer (GPT) have set the stage for improved and autonomous project scheduling models. This study demonstrates how project scheduling can be enhanced by employing building information modeling (BIM) to predict reliable schedules for each project element. The research is based on design science research (DSR) methodology and focus group discussion. The proposed system consists of six components: project analyzer, data warehouse, activity identifier and sequencing, activity magnitude and direct cost calculator, activity duration calculator, and schedule analyzer. Unlike other solutions, the proposed solution sequences activities based on an ML model rather than constraint matrices. The proposed solution thus enhances project performance through proactive planning and risk management. Integrating the autonomous project scheduling solution with the other components of an autonomous project management system would surmount schedule challenges and would result in greater cost efficiency and fewer delays.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
