Seemo: A new tool for early design window view satisfaction evaluation in residential buildings
Seemo: A new tool for early design window view satisfaction evaluation in residential buildings
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Seemo：住宅建筑早期设计窗景满意度评估的新工具
DOI：
10.1016/j.buildenv.2022.108909
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
T. Dogan
中科院分区：
文献类型：
--
作者：
Jaeha Kim;M. Kent;Katharina Kral;T. Dogan
人们大约90%的时间都在室内度过，因此可以说，室内空间设计可以显著影响居住者的幸福感。充足的外部视野是与居住者幸福感相关的最常被引用的室内品质之一。然而，由于城市化和密集化的趋势，设计师可能很难为外部提供各种各样的内容，以支持居住者的需求。为了更好地了解居住者的视图满意度，并提供可靠的设计反馈给建筑师，现有的视图满意度数据必须进行扩展，以捕捉更广泛的视图场景和居住者。由于缺乏易于使用的早期设计分析工具，大多数相关研究在建筑实践中仍然具有挑战性。然而，早期的景观评估可能是有利的，因为早期设计中的设计决策，如建筑方向，平面布局和立面设计，可以提高景观质量。本文，因此，提出了一个181名参与者的视图满意度调查结果与590窗口视图。调查数据用于训练树回归模型来预测视图满意度。预测性能进行了比较，现有的视图评估框架，通过案例研究。结果表明，新的预测（RMSE = 0.65）比框架（RMSE = 3.78）更准确的调查结果。此外，对于大多数响应，预测性能通常较高（R2≥ 0.64），验证了可靠性。为了方便在早期设计的视图分析，本文介绍了集成的满意度预测模型和光线投射工具，在CAD环境中计算视图参数。
People spend approximately 90% of their lives indoors, and thus arguably, the indoor space design can significantly influence occupant well-being. Adequate views to the outside are one of the most cited indoor qualities related to occupant well-being. However, due to urbanization and densification trends, designers may have difficulties in providing vistas and views to the outside with an assortment of content, which can support the needs of their occupants. To better understand occupant view satisfaction and provide reliable design feedback to architects, existing view satisfaction data must be expanded to capture a wider variety of view scenarios and occupants. Most related research remains challenging in architectural practice due to a lack of easy-to-use early-design analysis tools. However, early assessment of view can be advantageous as design decisions in early design, such as building orientation, plan layout, and façade design, can improve the view quality. This paper, hence, presents results from a 181 participant view satisfaction survey with 590 window views. The survey data is used to train a tree-regression model to predict view satisfaction. The prediction performance was compared to an existing view assessment framework through case studies. The result showed that the new prediction (RMSE = 0.65) is more accurate to the surveyed result than the framework (RMSE = 3.78). Further, the prediction performance was generally high (R2≥ 0.64) for most responses, verifying the reliability. To facilitate view analysis in early design, this paper describes integrating the satisfaction prediction model and a ray-casting tool to compute view parameters in the CAD environment.
使用虚拟环境评估观看位置对视图感知的影响
影响因子：
7.4
作者：
Abd-Alhamid F
通讯作者：
Abd-Alhamid F