计算机辅助建筑设计中使用神经网络的机器学习三十年(1990–2021)

IF 1.8 Q3 ENGINEERING, MANUFACTURING
Design Science Pub Date : 2023-08-25 DOI:10.1017/dsj.2023.21
Jinmo Rhee, Pedro Veloso, Ramesh Krishnamurti
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引用次数: 0

摘要

摘要在过去的几年里,基于深度学习的计算方法——即具有多层神经网络的机器学习——已经成为计算机辅助建筑设计(CAAD)主要研究领域的最先进方法。为了通过深度学习了解CAAD的当前趋势,将其置于更广泛的历史背景下,并确定未来的研究挑战,本文对将神经网络应用于CAAD问题的出版物进行了系统综述。特别是从CAAD社区的主要开放访问存储库CumInCad收集了使用神经网络的研究论文,并将其归类为不同类型的研究问题。通过分析这些类别中论文的分布,即研究主题的组成、数据类型和神经网络模型,本文提出并讨论了几个历史和技术趋势。此外,它还指出,所分析的出版物通常对作为其深度学习方法一部分的重要研究内容提供有限的访问权限。文章指出了共享训练实验和数据、描述数据集、数据集参数、数据集样本、模型、学习参数和学习结果以支持再现性的重要性。它提出了一项旨在通过机器学习提高CAAD研究的质量和可用性的指导方针。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three decades of machine learning with neural networks in computer-aided architectural design (1990–2021)
Abstract Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.
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来源期刊
Design Science
Design Science ENGINEERING, MANUFACTURING-
CiteScore
4.80
自引率
12.50%
发文量
19
审稿时长
22 weeks
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