No abstract

No abstract

This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.

The increase of home energy consumption levels is a major concern associated with designing smart cities. The use of innovative technology is meant to make our life easier but if not properly designed and utilised, can double our electricity bills. Lack of awareness and the inability to sense the energy being used in homes by tenants, is considered as the main contributor to this matter. Taking a hands-on experience approach, this workshop aims at increasing collaboration and engaging architects, designers, researchers and professionals with the Internet of Things (IoT) at a level where they can design and create prototypes. These prototypes can help them to understand, monitor and better utilise innovative technologies.

Non-Manifold Topology (NMT) has been previously proven to be an appropriate representation of interconnected architectural and spatial structures. This paper further explores the suitability of NMT for spatial reasoning purposes. A literature survey is done to identify the necessary components of a spatial reasoning framework, and an adaptation of such framework based on NMT is presented. This paper also describes the implementation of an NMT-based spatial reasoning framework, its integration into the Topologic software library which the authors develop, as well as the implementation challenges. Finally, a pathfinding study case on an NMT model, which has been generated from a Building Information Modelling (BIM) structure, is presented and analysed.