Enhancing Construction Management Digital Twins Through Process Mining of Progress Logs

Abstract

Large-scale infrastructure projects involve numerous complex processes, and even small construction management (CM) deficiencies can lead to significant resource waste. Digital twins (DTs) offer a potential solution to the management side of the problem. The current DT models focus on real-time physical space mapping, which causes the fragmentation of process data in servers and limits lifecycle algorithm implementation. In this paper, we propose a DT framework that integrates process twins to achieve process discovery through process mining and that serves as a supplement to DTs. The proposed framework was validated in a highway project. Based on BIM, GIS, and UAV physical entity twins, construction logs were collected, and process discovery was performed on them using process mining techniques, achieving process mapping and conformance checking for the process twins. The main conclusions are as follows: (1) the process twins accurately reflect the actual construction process, addressing the lack of process information in CM DTs; (2) process variants can be used to analyze abnormal changes in construction methods and identify potential construction risks in advance; (3) sudden changes in construction nodes during activities can affect resource allocation across multiple subsequent stages; (4) process twins can be used to visualize construction schedule risks, such as lead and lag times. The significance of this paper lies in the construction of process twins to complement the existing DT framework, providing a solution to the lost process relationships in DTs, enabling better process reproduction, and facilitating prediction and optimization. In future work, we will concentrate on conducting more in-depth research on process twins, drawing from a wider range of data sources and advancing intelligent process prediction techniques.