This study discusses a machine learning (ML)‐based framework for estimating the drift limit states of concrete‐filled steel tube (CFT) columns. The calibration process used a comprehensive database assembled from numerous experiments reported the in literature. An ML regression model was trained based on the calibration data to predict numerical model parameters. Subsequently, 10,000 CFT column design parameters were randomly sampled, and a parametric study was conducted to investigate the influence of the design parameters on the drift limit states using an ML model and the simulation results obtained from the developed numerical model. The results showed that the drift limit states associated with slight, moderate, and extensive damage were mainly affected by the parameters related to the strength and stiffness of the CFT columns (depth‐to‐thickness ratio, steel tube yield strength‐to‐concrete compressive strength ratio, and steel tube stiffness ratio). The drift limit state associated with complete damage was primarily influenced by the axial load ratio. Additionally, it was found that CFT columns are more likely to experience moderate damage owing to the local buckling of the steel tube rather than from concrete crushing. Finally, fragility curves for the CFT frame systems were developed using the nonlinear time‐history analysis results in conjunction with the developed ML‐based drift limit state prediction model.
This paper evaluates the influence of soil–structure interaction (SSI) on the seismic performance of a 25‐story reinforced concrete core wall building designed under performance‐based seismic design (PBSD) guidelines. A high‐fidelity three‐dimensional nonlinear finite element model was developed in OpenSees to capture nonlinear soil and superstructure behavior and explicit interaction among the superstructure, foundation, and surrounding soil. Nonlinear response‐history analyses were conducted for both the SSI model and a conventional fixed‐base superstructure model. Engineering demand parameters, including interstory drifts, floor accelerations, coupling beam rotations, wall strains, and core wall shear and moment demands, were compared to assess SSI effects. Results show that the two models produce broadly consistent predictions of seismic demands above ground level. The SSI model predicted up to 50% smaller shear force in basement walls due to interaction between the basement walls and surrounding soil. SSI primarily reduced short‐period floor acceleration spectra (<0.5 s) at the floor center of mass and localized demands at the ground level, where shear and moment responses in the reinforced concrete (RC) core differed by up to 10%. Moment demands imposed on the foundation were also reduced in the SSI model by up to approximately 10% and 20% beneath the RC core and basement walls, respectively. This relatively limited SSI influence is mainly attributed to the site's stiff conditions, where excavation of about 20 m of softer soils left a dense coarse‐grained layer ( V s ≈ 480 m/s) over bedrock at 30 m depth, resulting in a short site period and limited soil–foundation compliance. These findings indicate that fixed‐base modeling remains adequate for PBSD on shallow, relatively stiff soil sites, though SSI may warrant explicit consideration for low‐ to mid‐rise buildings, deeper or softer soil profiles, or design checks at or below ground level.
The 2025 Mw 7.7 Mandalay, Myanmar earthquake caused structural and nonstructural damage to high‐rise buildings in Bangkok, over 1000 km from the epicenter, while low‐rise structures remained unaffected, a selective impact not expected from such a distant source. Because of the limited availability of building instrumentation data in Bangkok, this study analyzed publicly available videos as an alternative data source to estimate building response through water spillage analysis and pixel displacement tracking. To our knowledge, this is the first characterization of the far‐field, basin‐resonance response of a large set of high‐rise buildings from public video. The natural periods of 28 buildings, estimated at 2.8–6.9 s, closely match the 3–7 s long‐period ground motions amplified by Bangkok's deep basin. Despite the low amplitude of recorded ground accelerations, resonant conditions, combined with extended surface wave duration and low building damping, allowed displacement amplitudes to accumulate over multiple cycles, explaining the selective response of high‐rise buildings. A video‐based strong motion period‐height relationship ( T = 0.0261H) is derived, yielding periods about 30% longer than the Thai seismic code formula, which is based on ambient conditions, and the observed drift ratios increase with velocity response spectral amplitude at each building's period. The complete collapse of a 33‐story building under construction is also analyzed, with an estimated period of 4.3 s before the collapse and a drift ratio exceeding 2%, indicating that factors beyond resonance contributed to the failure. These findings demonstrate that video‐based methods can characterize building response where seismic instrumentation is limited, with implications for the seismic design of high‐rise buildings in basin cities exposed to distant large earthquakes.
Understanding behavioral responses during and immediately after an earthquake is critical to reducing injuries and developing preparedness strategies. This study identifies response patterns under seismic threat using qualitative evidence from the 2020 Elazığ and İzmir earthquakes in Türkiye. Data were collected from 63 participants through semistructured individual interviews and family‐based focus group discussions. Responses were examined in three temporally distinct phases: (1) onset of the tremor, (2) intensification, and (3) immediate post‐tremor period. During the tremor, reported behaviors clustered around four dominant patterns: movement toward exits or open spaces under acute fear, freezing or waiting, improvised protective action, and moving toward vulnerable individuals. These responses were shaped by real‐time threat cues, including perceived shaking intensity, environmental noise, falling objects, perceived building danger, and the visible reactions of others. As the tremor intensified, increased perceived risk was associated with evacuation attempts, continued immobility, or delayed action under uncertainty. In several instances, participants remained in place because they perceived movement through stairs, exits, or enclosed spaces as dangerous. Initial postearthquake responses were characterized by movement toward perceived safe areas, attempts to contact relatives, and spontaneous helping behaviors toward nearby individuals. The findings suggest that earthquake response is shaped by dynamic threat interpretation, fear transmission through social cues, family responsibility, and socially mediated coordination. Interpreted through the Protective Action Decision Model and Emerging Norm Theory, the results highlight how behavioral responses evolve through the interaction between environmental signals, social cues, and emergent coordination. The study provides empirically based insights for improving preparedness messages, promoting contextually sensitive protective actions, strengthening household coordination strategies, and enhancing immediate postearthquake communication planning.
We present an update to the 2023 U.S. 50‐state National Seismic Hazard Model, based on questions and comments received from a range of users, as well as additional internal and building code reviews since its initial release. For the conterminous United States, the update includes (1) revised weighting of ground motion models in the Intermountain West, (2) revised weighting of seismicity models across the stable crust of the central and eastern United States, (3) corrections to fault rupture input files and hazard codes, and (4) adjustments to stable crust nonlinear site amplification terms at soft soil sites. The model also updates the implementation of moment magnitude 7–8 subduction interface earthquake ruptures for Alaska and includes an alternate, full‐catalog rate model for Hawaii. The revised model is available through the U.S. Geological Survey online tools and is accompanied by a data release of high‐resolution hazard datasets for eight site conditions (from soft soil to hard rock with a time‐averaged shear‐wave velocity, V S30 , ranging from 150 to 1500 m/s) in terms of peak horizontal ground acceleration, peak ground velocity, and 5% damped pseudo‐spectral accelerations at 21 spectral periods. It has been approved as the basis for updated design provisions and is being considered for inclusion in other hazard and risk assessment products.
