Damage of the cladding structures usually occurs from the wind-sensitive part, which can cause the damaged conditions to obviously vary from different areas especially on a large roof surface. It is necessary to design optimization due to the difference of wind loads by defining more accurate wind pressure coefficient (WPC) zones according to the wind vulnerability analysis. The existing wind pressure coefficient zoning methods (WPCZM) have successfully been used to characterize the simple roof shapes. But the solutions for the complex and irregular roof shapes generally rely on the empirical judgment which is defective to the wind loading analysis. In this study, a classification concept for WPC values on the roof surface is presented based on the unsupervised learning algorithm, which is not limited by the roof geometry and can realize the multitype WPC zoning more accurately. As a typical unsupervised learning algorithm, an improved K-means clustering is proposed to develop a new WPCZM to verify the above concept. And a method to determine the optimal K-value is presented by using the K-means clustering test and clustering validity indices to overcome the difficulty of obtaining the cluster number in the traditional methods. As an example, the most unfavorable pressure and suction WPC zones are studied on a flat roof structure with single wind direction and full wind direction based on the data obtained from the wind tunnel test. As another example, the mean pressure coefficient zones are studied on a saddle roof structure under 0- and 45-degree wind direction based on the data obtained by the wind tunnel test. And the proposed WPCZM is illustrated and verified.
展开▼
