In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

Widespread human-induced or natural threats on buildings and their users have made preparedness and rapid response crucial issues for saving human lives. The ability to identify the paths of egress during an emergency is critical for rescue and emergency services. Quality models supporting real, or near-real, time decision making and allowing the implementation of automated methods are very important. In this paper, we propose a novel automated construction of the Variable Density Network (VDN) for determining egress paths in dangerous environments. VND is used for deriving a navigable network in an indoor building environment, including a full 3D topological model. The accuracy of the proposed paths prediction tool was compared with key methods for navigable network generation, using the actual floor plan of Doha World Trade Centre. Findings revealed that in comparison to prevailing approaches, a key benefit from this approach is an increased prediction accuracy of egress route planning.This research/publication was made possible by a National Priority Research Program NPRP award [NPRP-06-1208-2-492] from the Qatar National Research Fund (a member of The Qatar Foundation)

The documentation of vanishing architectural heritage includes intensive information and data gathering, filtering, classification and digitization. Enabling such an endeavor necessitates the act of documentation, which requires the cooperation of several professionals from different fields, extensive resources, precise organization, and robust structuring. Therefore, the design and development of an innovative digital platform to facilitate the interaction between users from different specialties is highly desirable to enable dynamic real-time preservation and protection of such invaluable heritage. The aim of this study is to investigate the existing Qatari built heritage, urban conservation methods and the principles of modern digitizing and archiving techniques to create a sustainable and interactive archiving platform—Qatar Historic Buildings Information Modeling (Q-HBIM) platform. This unique digital platform aims to respond to the needs of the society as well as the experts in the fields of urban heritage conservation and buildings restoration. A literature review was conducted on topics relating to Qatar urban heritage and the principles of three-dimensional digitization technology including an exploration of the aspects of sustainable urban preservation. Accordingly, site analysis (physical, photogrammetric and 3D scanning techniques were used), and in-depth interviews were adopted as the main data collection techniques. The findings of this study elucidate the principles of innovative architectural heritage digitization tools involving Building Information Modeling (BIM) technology and processes. It is anticipated that the present work will formulate an advanced and unique interactive archiving tool in the context of Qatar and GCC/MENA regions in the shape of the: Qatar Historic Building Information Modeling (Q-HBIM) platform.