在AEC行业中使用强化学习来支持多学科设计:评估马尔可夫决策过程的利用

IF 1.6 0 ARCHITECTURE
Samer BuHamdan, A. Alwisy, Thomas Danel, A. Bouferguene, Z. Lafhaj
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引用次数: 0

摘要

虽然建筑、工程和施工(AEC)行业的设计实践仍然是一项创造性活动,但从决策科学的角度来处理设计问题具有显著的潜力,这体现在提供高性能可持续结构方面。这些可能的收益可以归因于无数的决策工具和技术,这些工具和技术可以用来帮助设计工作,例如结合计算能力和数据智慧的人工智能。在AEC行业参与者面临越来越大的压力,要求他们提供经济、环保和社会关怀的结构之际,这种组合变得极其重要。尽管有着巨大的潜力,但利用人工智能,特别是强化学习(RL)来支持AEC行业的多学科设计工作仍处于起步阶段。因此,本研究讨论了开发和应用马尔可夫决策过程(MDP)模型,一种RL应用,以帮助AEC行业的初步多学科设计工作。实验工作表明,MDP模型可以在多学科设计的解决方案空间内加快确定可行的设计方案,同时最大限度地提高找到最佳设计的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process
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.
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来源期刊
CiteScore
3.20
自引率
17.60%
发文量
44
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