The 3D information in Building Information Modeling (BIM) has received significant interest for smart city applications. Recently, employing Industry Foundation Classes (IFC) for BIM in data-driven methods for Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. However, despite extensive studies on applying the IFC data in BECE analysis, employing the full potential of the BIM remains poor due to its complex data model and incompatibility with data-driven algorithms. This paper proposes a framework to extract accurate semantic, geometry, and topology information from the room-level (space) IFC schema by introducing new geo-computation algorithms to deal with these challenges. Additionally, we define a new topological weighted relationship between spaces in different stories by combining common geometry area with energy resistance value. Eventually, the proposed weighted space-based graph will be constructed to decrease the original complexity of the IFC model, and it is compatible with graph-based machine learning algorithms. The results are promising, with more than 90% accuracy in extracting the geometry information for the convex and non-convex polyhedron rooms and 100% accuracy in detecting vertical and horizontal adjacent rooms. This study confirms the proposed approach’s efficiency, accuracy, and feasibility for space-based BECE analysis.

<p><strong>Abstract.</strong> Urban and population growth results in increasing pressure on the public utilities like transport, energy, healthcare services, crime management and emergency services in the realm of smart city management. Smart management of these services increases the necessity of dealing with big data which is come from different sources with various types and formats like 3D city information, GPS, traffic, mobile, Building Information Model (BIM), environmental, social activities and IoT stream data. Therefore, an approach to mine/analysis/interpret these data and extract useful knowledge from this diverse big data sources emerges in order to extract the hidden pattern of data using computational algorithms from statistics, machine learning and information theory. However, inconsistency, duplication and repetition and misconducting with the different type of discrete and continuous data can cause erroneous decision-making. This paper focuses on providing a rules extraction and supervised-decision making methods for facilitating the fusion of BIM and 2D and 3D GIS-based information coupling with IoT stream data residing in a spatial database and 3D BIM data. The proposed methods can be used in those applications like Emergency Response, Evacuation Planning, Occupancy Mapping, and Urban Monitoring to Smart Multi-Buildings so that their input data mostly come from 2D and 3D GIS, BIM and IoT stream. This research focus on proposing the unified rules extraction and decision engine to help smart citizens and managers using BIM and GIS data to make smart decision rather than focus on applications in certain field of BIM and GIS.</p>

Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.