A Building Digital Twin (BDT) can maintain an up-to-date digital model reflecting physical world conditions and has become necessary for building applications. Recent studies on the BDT employed the Internet of Things to sense physical-world conditions. Although cameras are one of the most widely-used facilities in buildings, their adoption in the BDT remains unexplored. This study proposes a novel computer-vision(CV)-enabled BDT scheme using Building Information Modelling (BIM) taking camera videos as input, which addresses the dimension, coordinate system, and object inconsistencies between BIM and camera videos. Firstly, the proposed BDT scheme detects objects’ locations and rotations jointly using a two-dimensional (2D) object detection network and a three-dimensional (3D) object estimation network. Then, Theorem and Lemmas are presented to compute the 3D locations in BIM coordinate system using detected 2D locations. Thirdly, both cold-start object matching and run-time object matching schemes are proposed to address the object inconsistency between camera videos and BIM. Finally, experiments were conducted in the real-world environment. The experiment results showed that the proposed BDT scheme maintained average location errors of 0.181$m$ with distortions preserved and 0.165$m$ with distortions removed in the manual calibration scenario, 0.166$m$ with distortions preserved, and 0.195$m$ with distortions removed in automatic calibration scenario. This finding proved the effectiveness of the proposed BDT scheme. This study is the first to explore a BDT scheme on top of BIM using CV. It is anticipated that this study will inspire more intelligent studies in smart buildings jointly employing both CV and BIM.

Pathfinding is a fundamental problem for many areas, e.g., robotics, automation, computer-aided design, and computer graphics. Although outdoor pathfinding is fledged, indoor pathfinding remains a challenge due to the lack of indoor maps. Currently, some efforts have utilized Building Information Modelling (BIM) to generate either grid-based map or topological map. However, either grid-based map or topological map is not sufficient to provide accurate and efficient pathfinding service. This study proposes a novel Grid-Topological map and develops an accurate and efficient indoor pathfinding scheme. The Grid-Topological map is modeled jointly adopting the advantages of both grid-based map and topological map. On top of Grid-Topological map, an accurate and efficient pathfinding algorithm is developed. Empirical studies proved the effectiveness of the Grid-Topological map, as well as the accuracy and efficiency of the proposed indoor pathfinding algorithm.

Fire checking is indispensable for guaranteeing the fire safety of buildings as it reviews the compliance of the building with fire codes and regulations. Automated Compliance Checking (ACC) systems that check building data utilizing Building Information Modeling (BIM) against fire codes have emerged as an active field of research. Substantial efforts have focused on analyzing the properties of the building components. However, the analysis of the spatial geometric relationships of building components has received inadequate attention. The present study proposes a novel ACC system leveraging Natural Language Processing (NLP) techniques to review the spatial geometric relationships of building components in BIM models. First, a framework for a BIM-based ACC system is delineated and decomposes ACC into three constituent subtasks: building model parsing, code knowledge translation, and compliance check result reporting. Second, an approach for structured processing of spatial geometric stipulations in fire codes using NLP is presented to review the geometric relationships between components in building models. Finally, the system’s performance is assessed by testing fire code compliance across various building types utilizing BIM models. The empirical findings demonstrate that the system achieves superior recall compared with the manually formulated gold standard, with the ACC system enabling quick, accurate, and comprehensive automated compliance checking.

The integration of Building Information Modelling (BIM) and geographic information systems (GIS) is a promising but challenging topic to solve problems in construction industry. However, loading and rendering rich BIM geometric data and large-scale GIS spatial information in a unified system is still technologically challenging. Current efforts mainly simplify the geometry in BIM models, or convert BIM geometric data to a lower level of detail (LOD). By noticing that only exterior features of BIM models are visible from outdoor observation points, culling BIM interior facilities can dramatically reduce the computational burden when visualizing BIM models in GIS. This study explores the outline detection problem and presents the OutDet algorithm, which selects representative observation points, transforms & projects the BIM geometric data into the same coordinate system, and detects the visible facilities. Empirical study results show that OutDet can cull a large portion of unnecessary features when rendering BIM models in GIS. The use of outlines of BIM models is not an alternative but rather a supplementary approach for current solutions. Jointly using LOD and outer surface can help improve the efficiency of integrated BIM-GIS visualization.Because OutDet retains BIM geometry and semantics, it can be applied to more BIM-GIS applications..