An autoencoder-clustering-based framework of daily wind speed pattern recognition and prediction for mountainous valley bridges

Abstract

Wind fields in mountain valleys exhibit pronounced diurnal variability, primarily influenced by topography and temperature fluctuations. A comprehensive understanding of these characteristics is crucial for optimizing the aerodynamic design of long-span bridge structures. This study develops a machine learning framework for recognizing and predicting daily wind speed variation patterns in mountain valleys. First, long-term field measurements of wind speed and temperature are collected from a bridge site in a mountainous valley, providing a comprehensive time-series dataset. Then, we apply Variational Mode Decomposition (VMD) for data processing and train Autoencoders (AE) to extract representative features of diurnal variations in wind speed, temperature, and their combined patterns. Based on the extracted features, we categorize wind-speed–temperature variation patterns using correlation analysis and clustering methods. Finally, a segment-matching algorithm is used to recognize and predict wind speed trends over extended daily timescales. This study provides a structured approach to modeling daily wind speed variations, offering valuable insights into the dynamic wind environment of mountainous valleys and enhancing predictive capabilities for bridge wind-resistant design and operation.