ABSTRACT Architectural style positioning is an important part in the process of residential building design and project planning. However, in practice, due to the complexity and ambiguity of styles, style positioning often relies more on the subjective judgement of the designers and lacks scientificity. This paper proposes a method for the classification and prediction of residential building styles. Through structured interviews and questionnaire surveys on front-line designers and project planners, it refines the key morphological elements and the site economic factors that influence architectural style classification and positioning. Based on machine learning, after analysing the data of 372 newly built real estate projects in Hangzhou, the research finds t|TABE_A_1779728|TABE_A_1779728hat the current real estate styles can generally be divided into 8 categories. Whether it is a curved volume, the shape of the roof and the richness of the tones are the most important morphological variables that differentiate style categories, and the building height is the most important economic factor for style positioning. When using the selected five economic factors as independent variables to train a neural network model and predict the morphological elements and style categories, the average accuracy reaches 77.2%.

中文翻译：

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基于机器学习的住宅建筑风格分类与预测

摘要 建筑风格定位是住宅建筑设计和项目规划过程中的重要组成部分。但在实践中，由于风格的复杂性和模糊性，风格定位往往更依赖于设计师的主观判断，缺乏科学性。本文提出了一种住宅建筑风格的分类和预测方法。通过对一线设计师和项目规划师的结构化访谈和问卷调查，提炼出影响建筑风格分类定位的关键形态要素和场地经济因素。基于机器学习，对杭州372个新建房地产项目的数据进行分析后，研究发现t|TABE_A_1779728|TABE_A_1779728hat目前的楼盘风格大体可以分为8类。无论是弯曲的体量，屋顶的形状和色调的丰富程度都是区分风格类别的最重要的形态变量，而建筑高度是风格定位最重要的经济因素。当使用选定的五个经济因素作为自变量训练神经网络模型并预测形态元素和风格类别时，平均准确率达到77.2%。