Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020

IF 1.6 0 ARCHITECTURE
Gizem Özerol, Semra Arslan Selçuk
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引用次数: 5

Abstract

Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.
建筑学科中的机器学习:2014年至2020年研究趋势综述
通过第四次工业革命中的最新技术发展,人工智能(AI)研究对社会科学、信息通信技术(ICT)、建筑、工程和建筑(AEC)等各个学科产生了巨大影响。特别是在决策和预测系统方面,人工智能和机器学习(ML)技术为改善机器和人类之间的相互关系提供了机会。当考虑ML和体系结构之间的联系时,可以声称没有像其他学科那样的并行加速。在这项研究中,考虑到最新的突破,我们专注于揭示ML和架构的共同点。我们的重点是通过描述ML在建筑中的潜力,对现有文献进行分类和分析,揭示共同点。本文使用了ML技术和人工智能技术子集进行的研究,并使用文献计量分析方法对所得数据进行了解释。为了讨论2014年至2020年间发表的最新研究文章,通过搜索引擎对主要主题、子集和关键词进行了提炼。统计数字被展示为巨大的数据集,结果通过桑基图清晰地描绘出来。由于对WOS(Web of Science)、CUMINCAD(由兄弟协会ACADIA、CAADRIA、eCAADe、SIGraDi、ASCAAD和CAAD futures支持的计算机辅助建筑设计出版物的累积索引)的现有文献进行了文献计量学分析,已经提供了可预测的数据,为研究人员未来可能的研究提供了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.20
自引率
17.60%
发文量
44
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