On 6 February 2023, two major earthquakes struck southeastern T & uuml;rkiye along the East Anatolian Fault, causing widespread structural damage, including the partial collapse of the historic Habibi Neccar Mosque in Antakya. This study presents a simulation-based approach to rapidly assess the seismic vulnerability of this partially damaged historic masonry structure. Due to the complexity and urgent condition of such heritage buildings, a simplified finite element (FE) modeling methodology is employed to evaluate structural behavior and support immediate stabilization decisions. Response spectrum analysis is applied to simulate and interpret stress distribution and deformation patterns in both undamaged and damaged states. The simulation results highlight significant tensile stress concentrations exceeding 0.2 MPa at dome-arch joints and vaults-primary indicators of localized failures. Additionally, the analysis reveals increased out-of-plane deformations and the influence of soil amplification in the remaining walls, both of which further compromise the structural integrity of the building. The findings demonstrate that simplified FE simulations can serve as practical and efficient tools for early seismic assessment of historic structures, contributing to rapid decision making, risk mitigation, and cultural heritage preservation in earthquake-prone areas.

This study investigates the application of machine learning (ML) algorithms for seismic damage classification of bridges supported by helical pile foundations in cohesive soils. While ML techniques have shown strong potential in seismic risk modeling, most prior research has focused on regression tasks or damage classification of overall bridge systems. The unique seismic behavior of foundation elements, particularly helical piles, remains unexplored. In this study, numerical data derived from finite element simulations are used to classify damage states for three key metrics: piers' drift, piles' ductility factor, and piles' settlement ratio. Several ML algorithms, including CatBoost, LightGBM, Random Forest, and traditional classifiers, are evaluated under original, oversampled, and undersampled datasets. Results show that CatBoost and LightGBM outperform other methods in accuracy and robustness, particularly under imbalanced data conditions. Oversampling improves classification for specific targets but introduces overfitting risks in others, while undersampling generally degrades model performance. This work addresses a significant gap in bridge risk assessment by combining advanced ML methods with a specialized foundation type, contributing to improved post-earthquake damage evaluation and infrastructure resilience.

This study investigates the effects of the February 6, 2023, earthquakes in T & uuml;rkiye, measuring 7.8 and 7.6 magnitudes (Mercalli intensities XI and X). It comprehensively assesses their impact, along with the subsequent Hatay earthquake (Mw 6.4), on various structures, including residential RC buildings, commercial, industrial, and strengthened structures, as well as critical lifeline components such as roads, bridges, power, and telecommunication systems, and areas affected by soil failures. Immediate field observations were conducted to assess changes and gather insights. The findings will contribute to the development of recommendations for future seismic damage prevention and mitigation strategies.