Computationally efficient change analysis of piece-wise cylindrical building elements for proactive project control

•Spatial relationships between building objects assist reliable change detection.•Neighborhood searching has limitations when analyzing changes of packed pipes.•A relational-graph-based approach can augment neighborhood searching.•The hybrid approach can achieve both computational efficiency and precision.•A test case having 109 pipes shows the precision and efficiency of the new method.

The designs of large-scale building systems, such as Mechanical, Electrical, and Plumbing (MEP) systems, undergo spatial changes during design-construction coordination, and as a result, their as-built conditions deviate, in some cases significantly, from their as-designed conditions. Construction engineers need to detect and analyze the differences between as-designed and as-built conditions of building systems promptly for responsive change management. Existing data-model comparison approaches either cannot correctly detect changed objects packed in small spaces, or cannot handle the computational complexity of comparing detailed as-designed and as-built geometries of MEP systems that contain hundreds or even thousands of elements (e.g., ducts). This paper presents a computationally efficient spatial-change-detection approach that reliably compares as-designed Building Information Models (BIMs) and 3D as-built models derived from laser scan data. It integrates nearest neighbor searching and relational graph based matching approaches to achieve computationally efficient change detection and management. A case study using data collected from a campus building was conducted to compare the new change detection approach proposed in this paper against the state-of-the-art change detection techniques. The results indicate that the proposed approach is capable of making more precise data-model comparisons in a computationally efficient manner compared to existing data-model comparison techniques.

Graphical abstractRelational graph generation for spatial change detection and tolerance error redistribution.Download high-res image (172KB)Download full-size image