Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …

Improving the urban wind climate yields substantial advantages, encompassing enhanced
public health, increased pedestrian safety, improved building energy efficiency, and effective …

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is
essential in improving the safety and availability of energy storage systems. However, the …

Wind-structures interaction has been extensively examined in the last few decades using
field measurements, full scale measurements and wind tunnel testing. These experimental …

The performance of a wind turbine is impacted by various environmental factors and their
interactions, such as wind speed, wave load, and temperature fluctuation. In order to …

Although the problems of wind pressure prediction on roofs have been studied extensively,
the prediction accuracy is still unsatisfactory owing to the limited capacity of shallow learning …

This study presents a data-driven methodology for predicting the pressure coefficient
statistics on the windward wall, roof, and leeward wall of low-rise buildings situated …

This study conducted extensive wind tunnel tests to evaluate the impact of terrain complexity
on wind pressure across low-rise buildings. A series of wind tunnel tests was performed …

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators
(WTGs), and the existing methods are insufficient to achieve accurate and rapid fault …

Nowadays, various machine learning (ML) approaches are widely used in different research
areas. However, the need for a large training dataset has restricted the attractiveness of ML …