The onsite early warning techniques that issue earthquake alerts based on the seismic
response of single stations have proven to be quite successful in detecting damage. The …

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid
metaheuristics optimisation techniques to predict the axial load bearing capacity of fully …

This study examines the feasibility of using a machine learning approach for rapid damage
assessment of reinforced concrete (RC) buildings after the earthquake. Since the real-world …

Due to its flexible and clean nature, distributed Photovoltaic (PV) power is essential for
solving energy and operational coordination challenges in integrating solar energy with …

As a natural disaster, earthquakes pose a significant threat to human life, infrastructure, and
societal stability. To mitigate these risks, earthquake forecasting has the potential to provide …

Earthquakes are extremely destructive natural disasters, causing ground shaking, tsunamis,
fissures, and landslides that result in loss of life. Early prediction of earthquake magnitude …

Time series data associated with seismic activities pose significant challenges in disaster
preparedness. These challenges underscore the need for reliable and timely damage …

Abstract Large Language Models (LLMs) have been shown to perform well for many
downstream tasks. Transfer learning can enable LLMs to acquire skills that were not …

Large Language Models (LLMs) have been shown to perform well for many downstream
tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pre …

With the continuous development of Internet of Things (IoT) technology, edge processing
platforms for power system characteristics have become an important technology. At …