Abstract Evaluating and optimizing the design of built and yet-to-be-built environments, with respect to human occupancy and behavior is both greatly beneficial and challenging. Crowd simulation can provide the computational means to analyze a design through the movement of virtual occupants (agents). A range of analytic information (metrics) can be computed from the simulated movement of the agents that offer insights on the design. Crowd simulation and the related analysis can be part of interactive or offline design optimization pipelines. Unfortunately, large scale crowd simulations are prohibitively expensive, especially when used within iterative design and optimization loops, where hundreds of simulations often need to be computed at interactive rates. We propose a machine learning framework that aims to solve this problem by learning the relationship between a building design and the evaluation metrics extracted from expensive simulations. We train an offline regression neural network using a synthetic training set that we generate for this purpose. Once the network is trained it can evaluate new designs efficiently, and approximate the corresponding analytic information with high accuracy. The proposed framework can also be used to find an optimized layout. We demonstrate the effectiveness of the framework on a variety of real world case studies.

中文翻译：

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预测人群出口和环境关系以支持建筑设计优化

摘要 评估和优化已建成和未建成环境的设计，考虑到人类的居住和行为，既有益又具有挑战性。人群模拟可以提供计算手段，通过虚拟居住者（代理）的移动来分析设计。可以从代理的模拟运动中计算出一系列分析信息（指标），这些信息可提供有关设计的见解。人群模拟和相关分析可以是交互式或离线设计优化流程的一部分。不幸的是，大规模人群模拟非常昂贵，尤其是在迭代设计和优化循环中使用时，通常需要以交互速率计算数百个模拟。我们提出了一个机器学习框架，旨在通过学习建筑设计与从昂贵的模拟中提取的评估指标之间的关系来解决这个问题。我们使用为此目的生成的合成训练集训练离线回归神经网络。一旦网络经过训练，它就可以有效地评估新设计，并以高精度逼近相应的分析信息。所提出的框架还可用于找到优化的布局。我们在各种现实世界案例研究中证明了该框架的有效性。一旦网络经过训练，它就可以有效地评估新设计，并以高精度逼近相应的分析信息。所提出的框架还可用于找到优化的布局。我们在各种现实世界案例研究中证明了该框架的有效性。一旦网络经过训练，它就可以有效地评估新设计，并以高精度逼近相应的分析信息。所提出的框架还可用于找到优化的布局。我们在各种现实世界案例研究中证明了该框架的有效性。