Mechanical, electrical, and plumbing (MEP) systems are crucial to a building, which directly affect the building safety, energy saving, and operational efficiency. Building information models (BIMs) help engineers to view the connection structure of MEP elements, reducing the time for reading drawings and training costs. However, existing MEP systems bring a tremendous challenge to monitoring due to issues with the complicated spatial structure, large scale, and intuitiveness. In addition, there is still a lack of feasible methods to model a representative graph in MEP systems. To address this problem, this study proposes an approach to model a directed representative graph of MEP systems using BIM data. The proposed approach contains two parts, the representative edge extraction and the direction identification. Firstly, MEP elements are converted into triangular meshes on which boundary points are extracted. Secondly, representative sets are developed to extract the representative points. Thirdly, representative points are connected to generate representative edges. Meanwhile, there are topological connection relationships among MEP elements and the flow directions of MEP ports, all of which are extracted to obtain the graph direction based on Industry Foundation Classes (IFC). Subsequently, representative edges and directions are combined to obtain the directed representative graph. Finally, experiments of directed representative graph extraction are evaluated on six BIM models. The experimental results show that directed representative graphs are extracted successfully. Furthermore, a simulated system is developed to integrate the directed representative graph and the Internet of Things (IoT) to realize the intelligent monitoring of MEP systems. The proposed directed representative graph model lays a solid foundation for the development of MEP systems monitoring management in smart buildings.

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.