Owing to its significance in ensuring structural safety and occupant comfort, wind pressure on buildings has attracted the attention of numerous scholars. However, the characteristics of wind pressures are usually complex. This study employs an unsupervised machine-learning algorithm, clustering algorithms, to study wind pressures on buildings. Wind pressures on a single building and two adjacent buildings with different gaps are measured in a wind tunnel, with clustering algorithms applied to cluster different wind pressure patterns. The results show that for the single-building model, the pressure patterns are symmetrical on the side surfaces of the building; for the two-building model with a small gap, a channeling effect can be identified; for the two-building model with a large gap, the pressure patterns shared symmetry with that of the single-building model. Clustering algorithms can recognize unidentified patterns of wind pressures on buildings. This study demonstrates that clustering algorithms are a powerful tool for recognizing patterns hidden in complex pressure fields and flow fields. Therefore, this study proposes a promising machine-learning technique that can perfectly complement traditional building methods using wind engineering.

由于其在确保结构安全和居住舒适性方面的重要意义，建筑物上的风压引起了众多学者的关注。但是，风压的特性通常很复杂。本研究采用无监督的机器学习算法，聚类算法来研究建筑物上的风压。在风洞中测量单个建筑物和两个具有不同间隙的相邻建筑物上的风压，并应用聚类算法对不同的风压模式进行聚类。结果表明，对于单一建筑物模型，建筑物侧面的压力模式是对称的。对于间隙较小的两建模型，可以识别出通道效应。对于差距很大的两建模型，压力模式与单一建筑物模型共享对称性。聚类算法可以识别建筑物上未知的风压模式。这项研究表明，聚类算法是识别隐藏在复杂压力场和流场中的模式的强大工具。因此，本研究提出了一种有前途的机器学习技术，可以很好地补充使用风能工程的传统建筑方法。