A BIM-data mining integrated digital twin framework for advanced project management

• Main Authors: Pan, Yue, Zhang, Limao
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Civil engineering; Digital Twin; Building Information Modeling
• Online Access: https://hdl.handle.net/10356/160758
• Institution: Nanyang Technological University

With the focus of smart construction project management, this paper presents a closed-loop digital twin framework under the integration of Building Information Modeling (BIM), Internet of Things (IoT), and data mining (DM) techniques. To be specific, IoT connects the physical and cyber world to capture real-time data for modeling and analyzing, and data mining methods incorporated in the virtual model aim to discover hidden knowledge in collected data. The proposed digital twin has been verified in a practical BIM-based project. Based on large inspection data from IoT devices, the 4D visualization and task-centered or worker-centered process model are built as the virtual model to simulate both the task execution and worker cooperation. Then, the high-fidelity virtual model is investigated by process mining and time series analysis. Results show that possible bottlenecks in the current process can be foreseen using the fuzzy miner, while the number of finished tasks in the next phase can be predicted by the multivariate autoregressive integrated moving average (ARIMAX) model. Consequently, tactic decision-making can realize to not only prevent possible failure in advance, but also arrange work and staffing reasonably to make the process adapt to changeable conditions. In short, the significance of this paper is to build a data-driven digital twin framework integrating with BIM, IoT, and data mining for advanced project management, which can facilitate data communication and exploration to better understand, predict, and optimize the physical construction operations. In future works, more complex cases with multiple data streams will be used to test the developed framework, and more detailed interpretations with the actual observations of construction activities will be given.