A five-story moment frame structural model with a base isolation system was tested on a shaking table. The isolation system comprised both linear natural rubber bearing (LNR) and nonlinear viscous dampers (NLVDs). Seven ground motions were employed: including three far-fault (FF) and four near-fault (NF) earthquake ground motions. The performance of the isolation system was evaluated by measuring the displacement and base shear of the isolation bearings. Furthermore, the axial force and displacement of the NLVDs were measured. The evolution of the fundamental dynamic frequency of the frame during the test was also determined. During strong earthquakes, NF ground motions caused larger story drifts and floor accelerations of the superstructure than FF ground motions. The displacement and base shear of the isolation base was very large when the isolated structure was subjected to Kobe_TAK000 and ChiChi_TCU102/278 pulse-like NF ground motions. Furthermore, the LNR s experienced tension and uplift when the PGA of input earthquake ground motions was larger than 0.80 g. Although the NLVDs performed very well in combination with the LNRs, the severe responses of the isolation bearings were caused by NF ground motion with a pulse period T_p neighboring the fundamental period of the isolated structure.

Frequent aftershocks often follow a strong mainshock. They can significantly increase cumulative damage to a structure. A model of a five-story reinforced concrete frame structure was designed and a nonlinear mathematical model of the structure was developed to investigate the damage states resulting from different mainshock-aftershock sequences. Mainshock-aftershock sequences consisting of one of three recorded mainshocks combined with one of five recorded aftershocks were created for input to the mathematical model. Inelastic energy dissipation and the Park-Ang damage index were used as measures of cumulative damage to the structure. The results demonstrate that consideration of only the single mainshock ground motion in seismic building design can result in the design and construction of unsafe buildings. Total cumulative damage to a structure is caused by the combination of damage states resulting from the mainshock and the aftershock(s).

Accurate estimation of structural displacement is crucial for assessing the damage state of structures. However, displacement sensors are rarely deployed on practical structures. Moreover, the traditional integration method based on acceleration data entails sophisticated processing steps, such as multiple filtering and baseline correction procedures. Therefore, an end‐to‐end displacement prediction method based on the long short‐term memory (LSTM) model integrated with an attention mechanism is presented herein to mitigate the limitations. The proposed model can predict structural displacement using noisy acceleration data acquired from accelerometer sensors mounted on structures. The overlapping window technique was employed to augment the dataset and enhance training efficiency. Furthermore, an attention mechanism was incorporated to improve prediction accuracy. When compared with the traditional integration method and the LSTM model without the attention mechanism, the proposed Attention‐LSTM model exhibited superior performance on the testing dataset. Additionally, the effects of key parameters were systematically investigated, such as the normalization method, cropping window size, number of LSTM layers, and number of units per layer. It is demonstrated that the proposed Attention‐LSTM model possesses robust generalization capability and antinoise performance, which contributes to the reliable estimation of structural displacement in engineering structures.

A Ms 6.8 earthquake struck Luding, China, in September 2022, causing significant structural damages to buildings. Notably, reinforced concrete (RC) frame structures exhibited the failure mode of strong beam–weak column (SBWC), which poses a severe threat to human safety and economic stability. This study investigates the disadvantageous failure mechanisms in RC frame structures, drawing on observations from the author's recent field investigations. Refined finite element models (FEMs) of RC frames were developed to systematically simulate these failure mechanisms. The models enabled an in-depth analysis of structural characteristics, with particular attention to column-to-beam flexural strength ratios (CBFSRs). These ratios were calculated to identify thresholds that can prevent destructive SBWC failure modes and promote the desired strong column–weak beam (SCWB) behavior. The FEM analysis results were validated against real-world earthquake damage phenomena, showing strong consistency in damage patterns. The study also highlights the critical role of external factors in exacerbating structural damage. For example, slope site effects significantly amplified seismic impacts on structures. Furthermore, the influence of non-structural elements such as Que Ti and infill walls was found to increase shear force demands on RC frame columns, further compromising their performance under seismic loads. Based on these findings, the study proposes an optimal range for CBFSRs to achieve SCWB behavior, contributing to safer structural designs. Practical recommendations and considerations are outlined to guide future earthquake-resistant construction practices and mitigate disaster risks effectively.