Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era.
Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era.
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DOI：
10.1016/j.heliyon.2023.e21598
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发表时间：
2023-11
期刊：
影响因子：
4
通讯作者：
Deng, Qiaoming
中科院分区：
文献类型：
--
作者：
Liu, Yubo;Chen, Kaifan;Ni, Eryu;Deng, Qiaoming
关键词：
全球新冠肺炎大流行使人们更加关注建筑环境与健康之间的关系，尤其是在学生花费大量时间进行教育的环境中。学校使用的传统侧采光虽然经济实惠且易于施工，但会导致室内采光不均匀。为解决这一问题，本文借鉴20世纪初一场历史性的呼吸道疫情中的“露天学校运动”的设计经验，提出了广州市中小学梯田教学楼的设计模式。拟议的设计依靠天窗照明，每个教室都有一个户外平台。采用基于空间日光自主性(SDA)、日照均匀度(UOD)、年日光曝光量(ASE)、室外平台面积(OPA)、山墙长度(GWL)和空间利用率(SU)的优化算法，得到建筑的最佳混凝土形式。为了加快仿真过程，提出了一套基于人工神经网络的复杂形状快速预测网络模型。这种分组预测方法将模拟速度提高了357倍，并在设计初期大幅加快了基于6个指标的优化过程，最终得到了满足上述标准的4栋梯形教学楼。总体而言，建议的设计提供了一种新颖的建筑形式，在确保整体视觉舒适性的同时，促进了学生的学习和身体健康。大流行后，我们提倡以历史教育建筑为灵感的梯田教室，强调空间和采光。开发了基于人工神经网络的模型，实现了357倍的优化速度，对复杂模式具有很强的适应性。经过优化的梯田教室在采光性能方面有了显著的提高。
The global COVID-19 pandemic has increased attention to the relationship between the built environment and health, particularly in educational settings where students spend a significant amount of their time. Traditional side daylighting used in schools, while cost-effective and easy to construct, can result in uneven indoor daylighting. To address this issue, this paper proposes a terraced teaching building design model for primary and secondary schools in Guangzhou based on the design experience of an “open-air school movement” during a historical respiratory epidemic in the early 20th century. The proposed design relies on skylight for lighting, and each classroom has an outdoor platform. An optimization algorithm based on Spatial Daylight Autonomy (sDA), Uniformity of Daylighting (UOD), Annual Sunlight Exposure (ASE), Outdoor Platform Area (OPA), Gable Wall Length (GWL), and Space Utilization (SU) is used to obtain the optimal concrete form of the building. To speed up the simulation process, a set of Artificial Neural Network (ANN) based rapid prediction network models for complex forms is proposed. This group prediction method improves the simulation speed by 357 times and grossly speed up the optimization process based on six indexes in the early design stage, resulting in four terraced teaching buildings that meet the above criteria. Overall, the proposed design provides a novel architectural form that ensures overall visual comfort while promoting students' learning and physical health. Post-pandemic, we advocate terraced classrooms inspired by historical educational architecture, emphasizing space and daylighting. ANN-based model developed, achieving 357x faster optimization, adaptable to complex patterns. The optimized terraced classrooms demonstrate a substantial enhancement in daylighting performance.
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影响因子：
2.5
作者：
Aries, M. B. C.;Aarts, M. P. J.;van Hoof, J.
通讯作者：
van Hoof, J.
影响因子：
2.8
作者：
Arbab, Maryam;Rahbar, Morteza;Arbab, Mojgan
通讯作者：
Arbab, Mojgan
影响因子：
7.4
作者：
Lakhdari, Khaoula;Sriti, Leila;Painter, Birgit
通讯作者：
Painter, Birgit
DOI：
10.1038/sj.jea.7500165
发表时间：
2001-05-01
期刊：
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY
影响因子：
--
作者：
Klepeis, NE;Nelson, WC;Engelmann, WH
通讯作者：
Engelmann, WH
影响因子：
1.4
作者：
Guyon, Dennis
通讯作者：
Guyon, Dennis