The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process. (June 2022)

• Record Type: Journal Article
• Authors: BuHamdan, Samer; Alwisy, Aladdin; Danel, Thomas; Bouferguene, Ahmed; Lafhaj, Zoubeir
• Is Part Of: International journal of architectural computing. Volume 20:Number 2(2022)
• Page Start: 216
• Page End: 237
• Publication Date: 2022-06
• Subjects: Design evaluation -- multidisciplinary design -- reinforced learning -- Markov Decision Process -- social impact -- architecture, engineering, and construction industry; Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1177/14780771211069999
• Languages: English
• ISSNs: 1478-0771

Abstract

While the design practice in the architecture, engineering, and construction (AEC) industry continues to be a creative activity, approaching the design problem from a perspective of the decision-making science has remarkable potentials that manifest in the delivery of high-performing sustainable structures. These possible gains can be attributed to the myriad of decision-making tools and technologies that can be implemented to assist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom. Such combination comes to extreme importance amid the mounting pressure on the AEC industry players to deliver economic, environmentally friendly, and socially considerate structures. Despite the promising potentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary design endeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing and applying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expedite identifying viable design alternatives within the solutions space in multidisciplinary design while maximizing the likelihood of finding the optimal design.