High-efficient evaluations of building performance are often required for comparisons of different design alternatives in architectural sustainable design processes. General Computational Fluid Dynamics (CFD) simulations are usually complicated and time-consuming for wind environment investigation and evaluation. A hybrid framework for rapid evaluation of pedestrian-level wind environment will be proposed in the present work. This framework will then be formulated by integrating parametric design, CFD simulation, image processing, and machine learning, and it could immediately predict the Low-Velocity Areas (LVAs) around rectangular-form buildings. A large amount of data of 300 building cases generated by parametric design, CFD simulation, and image processing to train a Machine Learning Model (MLM) could be applied for the prediction of LVAs. In the case investigations, MLM was tested in the prediction of the other new 24 building cases with random geometric parameters. The comparison of MLM and CFD results showed that their solutions were close to each other. Efficiency and accuracy of the hybrid framework were further demonstrated through quantitative analysis of statistical discrepancies of MLM and CFD results. Hybrid framework was an original attempt to integrate multiple emerging computational tools, and it could provide high-efficient quantitative analysis of wind environment and give practical design optimization information in the early stage.

在建筑可持续设计过程中，通常需要对建筑性能进行高效评估，以比较不同的设计方案。通用计算流体动力学 (CFD) 模拟通常对于风环境调查和评估来说既复杂又耗时。在目前的工作中将提出一种用于快速评估行人级风环境的混合框架。然后，该框架将通过集成参数化设计、CFD 模拟、图像处理和机器学习来制定，它可以立即预测矩形建筑物周围的低速区域 (LVA)。通过参数化设计、CFD 模拟和图像处理来训练机器学习模型 (MLM) 生成的 300 个建筑案例的大量数据可用于预测 LVA。在案例调查中，MLM 在其他 24 个具有随机几何参数的新建筑案例的预测中进行了测试。MLM 和 CFD 结果的比较表明它们的解彼此接近。通过对 MLM 和 CFD 结果的统计差异进行定量分析，进一步证明了混合框架的效率和准确性。混合框架是对多种新兴计算工具进行集成的独创性尝试，它可以提供高效的风环境定量分析，并在早期提供实用的设计优化信息。通过对 MLM 和 CFD 结果的统计差异进行定量分析，进一步证明了混合框架的效率和准确性。混合框架是对多种新兴计算工具进行集成的独创性尝试，它可以提供高效的风环境定量分析，并在早期提供实用的设计优化信息。通过对 MLM 和 CFD 结果的统计差异进行定量分析，进一步证明了混合框架的效率和准确性。混合框架是对多种新兴计算工具进行集成的独创性尝试，它可以提供高效的风环境定量分析，并在早期提供实用的设计优化信息。