The Step-Enlargement-Based Time History Analysis Acceleration Technique (the SEB THAAT), introduced in 2008, accelerates seismic response history analysis by enlarging the digitization step of the earthquake records. This paper applies the technique to Incremental Dynamic Analysis and seismic fragility curve generation. First, the SEB THAAT is implemented within the OpenSEES framework, and then using 44 earthquake records and two benchmark structures, comparative analyses are performed with and without the SEB THAAT. The results show runtime reduction by 45-72% with small changes in accuracy (median errors < 4%) for generation of fragility curves. For complementary studies, additional examples are discussed, as well.
This study investigates the seismic performance of four high-slenderness multi-drum columns from the colonnaded street of the ancient Roman Bath in Ankara, T & uuml;rkiye, using finite element analysis (FEA) based on restoration designs. FEA accuracy is validated through shaking table test simulations. Parametric analyses assess the effects of foundation modeling, geometric imperfections, and embedded metallic poles. Nonlinear bond-slip behavior with ductile fracture is used for pole modeling. Site-specific earthquake scenarios and collapse threshold criteria based on rocking and sliding are applied. Results show imperfections critically affect response, while metallic poles enhance seismic resistance and inform heritage preservation strategies.
Recently, data intelligence (DI) models based on machine learning have been extensively applied to the study of reinforced concrete (RC) beam - column joints. However, the datasets used in most DI models typically combine both interior and exterior joints while the joint type is a complex variable that has received limited attention in previous research. In this study, a dataset comprising 310 reinforced concrete beam - column joints subjected to low-cycle loading was compiled, including 171 interior joints and 139 exterior joints. To examine the influence of joint types on the prediction of seismic performance (i.e. failure modes and peak shear stress), three separate datasets were constructed: one containing only interior joints (IJ), one with only exterior joints (EJ), and one combining both types (IEJ). In addition, to balance computational efficiency and prediction accuracy, the best-first search strategy was integrated with conventional machine learning algorithms to develop an improved predictive model. The proposed model identifies the most suitable feature combinations for each algorithm in predicting joint performance and was validated against conventional machine learning algorithms with and without feature selection, as well as typical joint theory models. Corresponding DI models for IJ, EJ, and IEJ datasets were established to predict failure modes and peak shear stress of beam - column joints. Finally, feature importance indices were calculated using the selected feature sets and interpretability techniques, and the influence of joint types on prediction performance was systematically analyzed.
To investigate the dynamic response and failure characteristics of the portal section of a loess tunnel under seismic action under different rainfall intensities, two working conditions - heavy rain (80 mm/24 h) and extreme rainstorm (170 mm/24 h) - were set up for shaking table model tests. Acceleration sensors and strain gauges were used to collect dynamic response data of the tunnel and slope, revealing the deformation patterns and failure mechanisms of the tunnel lining and soil under the coupled effect of rainfall and earthquake. The results show that rainfall intensity significantly affects the dynamic response of the tunnel structure. Under extreme rainstorm conditions, axial cracks appear earlier in the tunnel, and the soil is more prone to liquefaction. The vault and invert are the most sensitive areas for acceleration response, especially under Z-direction loading, showing significant nonlinear amplification effects. The left springing is the strain concentration zone, exhibiting obvious asymmetric pressure characteristics. As seismic intensity increases, the strain response of the lining transitions from linear to nonlinear, with 1.0 g being the threshold for structural degradation. Rainfall exacerbates seismic damage in the portal section of loess tunnels, and under extreme rainstorm conditions, the structure is more likely to enter the plastic stage and fail. It is recommended to strengthen key parts such as the vault, invert, and springing in the seismic design of loess tunnels, and to consider the impact of coupled horizontal and vertical ground motions.
We addressed uncertainties in source-scaling relationships caused by database heterogeneities in kinematic fault-rupture models using a linear mixed-effects regression model. A total of 23 source-scaling relationships for various source scaling parameters are developed with respect to moment magnitude using a combined database of 465 rupture models from SRCMOD and USGS events. Detailed residual analyses and comparison studies are performed to evaluate the best fit for each parameter. The proposed relationships adhere to the regression principles and compare well with other scaling relationships. We observed that incorporating random effects improves the model's prediction capacity, particularly for groups with small data population.
