论文标题
深度学习在生成所需的设计选项中的应用：使用合成训练数据集的实验
Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset
论文作者
论文摘要
大多数设计方法都包含一个前进框架，要求建筑物的主要规格以生成输出或评估其性能。但是，建筑师敦促不确定适当的设计参数，但仍敦促特定目标。深度学习（DL）算法提供了一个智能的工作流程，在该算法中，系统可以从顺序培训实验中学习。这项研究将使用DL算法的方法应用于生成所需的设计选项。在这项研究中，研究了一个对象识别问题，以最初根据训练数据集预测未见样本图像的标签，该培训数据集由不同类型的合成2D形状组成；后来，应用了生成的DL算法进行训练并为给定标签生成新的形状。在下一步中，对算法进行了训练，可以根据空间日光自治（SDA）指标生成窗口/墙壁图案，以实现所需的光/阴影性能。实验在预测看不见的样本形状和生成新的设计选项方面显示出令人鼓舞的结果。
Most design methods contain a forward framework, asking for primary specifications of a building to generate an output or assess its performance. However, architects urge for specific objectives though uncertain of the proper design parameters. Deep Learning (DL) algorithms provide an intelligent workflow in which the system can learn from sequential training experiments. This study applies a method using DL algorithms towards generating demanded design options. In this study, an object recognition problem is investigated to initially predict the label of unseen sample images based on training dataset consisting of different types of synthetic 2D shapes; later, a generative DL algorithm is applied to be trained and generate new shapes for given labels. In the next step, the algorithm is trained to generate a window/wall pattern for desired light/shadow performance based on the spatial daylight autonomy (sDA) metrics. The experiments show promising results both in predicting unseen sample shapes and generating new design options.
