The application of machine learning (ML) in the building domain has rapidly evolved due to developments in ML algorithms. Abundant studies have reviewed the use of ML algorithms to address building-domain-related challenges, but some research questions remain unclear: (i) what is the landscape of ML application topics in building domain, (ii) what are the preferences among different ML application topics and algorithms, and (iii) how these topics, ML algorithms, and their preferences evolve until forming current landscape. To address these aspects, an ML-based topic modeling (TM) approach was used in this paper to identify all ML application topics, elucidate the horizontal correlation and vertical knowledge hierarchy among the topics to reveal their static correlation and dynamic evolution with ML algorithms. Several findings that answered each research question were drawn, and recommendations that can facilitate balanced and rational ML advancements in the building domain are proposed for future research.

中文翻译：

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机器学习应用在建筑中的神经主题建模：关键主题、算法和进化模式


由于 ML 算法的发展，机器学习 （ML） 在建筑领域的应用迅速发展。大量研究回顾了使用 ML 算法解决与构建领域相关的挑战，但一些研究问题仍不清楚：（i） 构建领域中 ML 应用主题的前景如何，（ii） 不同 ML 应用主题和算法之间的偏好是什么，以及 （iii） 这些主题、ML 算法及其偏好如何演变，直到形成当前的格局。为了解决这些方面问题，本文使用了基于 ML 的主题建模 （TM） 方法来识别所有 ML 应用主题，阐明主题之间的水平相关性和垂直知识层次结构，以揭示它们与 ML 算法的静态相关性和动态演变。得出了回答每个研究问题的几个发现，并为未来的研究提出了可以促进建筑领域平衡和理性 ML 进步的建议。