Self-Organizing Optimization of Construction Project Management Based on Building Information Modeling and Digital Technology

The growth of technology has led to the involvement of machines, sensors, and intelligent systems in the design, construction, and monitoring processes of the construction industry. These capabilities can interact with each other during the building process. The use of these technologies, while increasing speed and accuracy, reduces operational costs. The main goal of this study is application of the self-organizing digitization concept in data mining and intelligent planning in construction project management linked with building information modeling. An intelligent self-organizing data mining system is proposed for optimizing the complexity of the design and construction process. The framework can be addressed as a decision-making system that considers human activities and economic considerations to improve the workflow. To evaluate the model efficiency, digital twin-driven intelligent construction and grounded theory methodology were incorporated. Results showed that the efficiency of the practical phase in building management was improved after application of the self-organizer model and had a direct effect on time prediction plans. Furthermore, the developed autoregressive integrated moving average model can predict construction progress.