Formalized knowledge of construction sequencing for visual monitoring of work-in-progress via incomplete point clouds and low-LoD 4D BIMs

Over the last few years, new methods that detect construction progress deviations by comparing laser scanning or image‐based point clouds with 4D BIM are developed. To create complete as‐built models, these methods require the visual sensors to have proper line‐of‐sight and field‐of‐view to building elements. For reporting progress deviations, they also require Building Information Modeling (BIM) and schedule Work‐Breakdown‐Structure (WBS) with high Level of Development (LoD). While certain logics behind sequences of construction activities can augment 4D BIM with lower LoDs to support making inferences about states of progress under limited visibility, their application in visual monitoring systems has not been explored. To address these limitations, this paper formalizes an ontology that models construction sequencing rationale such as physical relationships among components. It also presents a classification mechanism that integrates this ontology with BIM to infer states of progress for partially and fully occluded components. The ontology and classification mechanism are validated using a Charrette test and by presenting their application together with BIM and as‐built data on real‐world projects. The results demonstrate the effectiveness and generality of the proposed ontology. It also illustrates how the classification mechanism augments 4D BIM at lower LoDs and WBS to enable visual progress assessment for partially and fully occluded BIM elements and provide detailed operational‐level progress information.