Data-based wind disaster climate identification algorithm and extreme wind speed prediction

An e xtreme wind speed estimation method that consider s wind hazard climate type s is critical for design wind load calculation for building structure s affected by mixed climate s . However, it is very difficult to obtain wind hazard climate type s from meteorologi cal data records, because they restrict the application of extreme wind speed estimation in mixed climates . This paper first proposes a wind hazard type identification algorithm based on a numerical pattern recognition method that utilizes feature extraction and generalization . Next, it compares six commonly used machine learning models using K - fold cross - validation. Finally, it takes meteorological data from three locations near the southeast coast of China as example s to examine t he algor ithm's performance . Based on classification results, the extreme wind speed s calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discus sed . Extreme wind speed; Mixed climates; Data - driven method; Pattern Recognition; Machine Learning; 1. Introduction Wind effects are key factors in structural design, and extreme wind speeds are the starting point . F or flexible structures such as long - span bridges, long - span roofs and high - rise buildings, wind loads are normally the predominant loads. I n order to meet both the ultimate safety and performance requirements of wind - resistant structural design, it s necessary to accurately estimate the extreme wind speed s for different recurrence period s . For significant buildings and infrastructures, it is necessary to estimat e the extreme wind speed through probabilistic methods from local wind speed record s .