To fully understand how a construction project actually proceeds, a novel framework for automated process discovery from building information modeling (BIM) event logs is developed. The significance of the work is to manage and optimize the complex construction process towards the ultimate goal of narrowing the gap between BIM and process mining. More specifically, meaningful information is retrieved from prepared event logs to build a participant-specific process model, and then the established model with executable semantics and fitness guarantees provides evidence in process improvement through identifying deviations, inefficiencies, and collaboration features. The proposed method has been validated in a case study, where the input is an as-planned event log from a real BIM construction project. The process model is created automatically by the inductive mining and fuzzy mining algorithms, which is then analyzed deeply under the joint use of conformance checking, frequency and bottleneck analysis, and social network analysis (SNA). The discovered knowledge contributes to revealing potential problems and evaluating the performance of workflows and participants objectively. In the discussion part, as-built data from the internet of things (IoT) deployment in construction site monitoring is automatically compared with the as-planned event log in the BIM platform to detect the actual delays. It turns out that the participant playing a central role in the network tends to overburden with heavier workloads, leading to more undesirable discrepancies and delays. As a result, extensive investigations based on process mining supports data-driven decision making to strategically smooth the construction process and increase collaboration opportunities, which also help in reducing the risk of project failure ahead of time.

为了充分理解建筑项目的实际进行过程，开发了一种用于从建筑信息模型（BIM）事件日志中自动发现过程的新颖框架。这项工作的意义是管理和优化复杂的施工过程，以缩小BIM和工艺采矿之间的差距为最终目标。更具体地说，从准备好的事件日志中检索有意义的信息以构建特定于参与者的流程模型，然后所建立的具有可执行语义和适用性保证的模型通过识别偏差，效率低下和协作功能，为流程改进提供了证据。案例研究中验证了所提出的方法，其中输入是来自实际BIM建设项目的按计划事件日志。过程模型由归纳挖掘和模糊挖掘算法自动创建，然后在一致性检查，频率和瓶颈分析以及社交网络分析（SNA）的联合使用下进行深入分析。发现的知识有助于揭示潜在的问题，并客观地评估工作流程和参与者的绩效。在讨论部分中，会自动将建筑工地监控中来自物联网（IoT）部署的竣工数据与BIM平台中的计划事件日志进行比较，以检测实际延迟。事实证明，在网络中扮演中心角色的参与者往往会负担较重的工作量，从而导致更多不合需要的差异和延迟。因此，