The anticipated method aims to develop a robust machine learning-based approach for
aftershock classification using seismic sensing signals. Accurate classification of after …

Machine Learning (ML) has developed rapidly in recent years, achieving exciting
advancements in applications such as data mining, computer vision, natural language …

The prediction of the seismic risk and vulnerability of bridge clusters can contribute positively
to the development of large-scale regional earthquake resilience and loss models. Using …

This study aims to determine the optimal number and combination of input parameters from
machine learning (ML) techniques, encompassing both earthquake and bridge parameters …

Residual drift of structural systems after seismic events can make buildings uninhabitable
and may cause their collapse in aftershocks, as seen in the 2023 Turkey-Syria earthquake …

This study addressed the issue of distortion in evaluating the seismic damage state of
railway bridge piers using the original Park-Ang damage index. By constructing a …

Restoring residential buildings following earthquake damage requires a significant level of
resources. Being able to predict these resource requirements in advance and accurately …

A fast and accurate earthquake damage assessment is essential for timely post-earthquake
evaluation and rescue. However, existing methods that combine structural and seismic …

Predicting bearing capacity of reinforced concrete (RC) columns under specific failure
modes plays a dominant role in the seismic safety of building structures. Traditional …

A novel non-destructive non-contact methodology based on surface crack image processing
is developed in this paper for residual strength and stiffness estimation of seismically …