Earthquake Engineering & Structural Dynamics最新文献

The aim of this study was to examine the seismic performance of reinforced concrete shear walls in in-plane and out-of-plane directions under single main shock and mainshock-aftershock sequences. Two shear wall specimens were designed for low cycle load tests to withstand in-plane, in-plane then out-of-plane (IP-OP), out-plane, and out-of-plane then in-plane (OP-IP) forces, respectively. The seismic performance of shear walls under different forces was assessed by analyzing macroscopic failure phenomena and the novel performance parameters of specimens, following which their damage status was evaluated. The specimens were simulated using finite element software, and two frame shear wall structural systems were designed so that non-linear time history analysis could be performed to assess the deformation and damage to the shear walls in the two directions of the plane under a single main impact and different input directions of mainshock-aftershock sequences. The results revealed that following damage in the in-plane direction of the shear wall, seismic capacity decreased significantly if it was subjected to a force in the out-of-plane direction once again, and the degree of damage under the earthquake action of mainshock-aftershock sequences was significantly higher than that under the single main shock action. Therefore, it is necessary to pay special attention to the seismic performance of the out-of-plane direction of the shear walls and examine the seismic performance of the structural system under different input directions of mainshock-aftershock sequences.

Strong earthquake disasters can cause noticeable damage correlations among regional buildings with similar mechanical properties, termed structure-to-structure seismic damage correlation, which has a pronounced impact on the regional seismic risk assessment and therefore needs to be properly quantified. This study first introduces a time-domain analytical and equivalent frequency-domain analysis based on random vibration theory for calculating structure-to-structure seismic damage correlation coefficients. Subsequently, by employing the spatially consistent white noise excitation and the spatially varying white noise excitation with the Luco–Wong coherence function, the structural filtering effect and the ground motion spatial correlation effect are progressively incorporated, and an analytical interstructural damage correlation model incorporating structural dynamic properties and spatial distance is derived. Comparations with Monte Carlo simulations and existing empirical models demonstrate that the proposed analytical model possesses a clear physical basis and high reliability. Finally, a case study was conducted on a district having 29,461 buildings in Shanghai, China to illustrate the influence of interstructural damage correlation on the regional seismic risk. Results show that disregarding the interstructural seismic damage correlation can lead to underestimation of overall loss uncertainty.

Prediction of the soil seismic response is of primary importance for geotechnical earthquake engineering. Conventional physics-based models such as the finite element method (FEM) often face challenges due to inherent model assumptions and uncertainties of model parameters. Furthermore, these physics-based models require significant computational resources, particularly when simulating seismic responses across numerous soil sites. In this study, a multi- input integrative neural network is developed for predicting soil seismic response based on the recorded data from a large number of downhole array sites. Ground motions, seismic event information, and wave velocity structures of the sites are utilized as input data in the proposed neural network, enabling the model to adapt to various site conditions. Comparative assessments against state-of-the-art FEM models demonstrate that the proposed models exhibit superior prediction performance with increased efficiency. Furthermore, the pre-training technique, a transfer learning method, is employed to predict the seismic response at new stations. By fine-tuning the pre-trained model derived from the extensive dataset with limited recorded data from new stations, high-precision seismic response predictions can be realized, illustrating the adaptability and efficacy of the proposed approach in data-scarce conditions.

The traditional tower-style building (TTSB) is an innovative structural form constructed in modern cities imitating the overall appearance of ancient timber pagodas, and it is an extraordinarily cultured high-rise construction. The limited stipulations for high-rise TTSBs in the seismic design code pose challenges in assessing the reliable performance of the unique structure subjected to severe earthquakes. This paper presents the shaking table test and numerical analyses of a 1/15-scale 13-story TTSB specimen with an integral tower height of 5.36 m, which is a steel frame-braced core-tube structure consisting of seven bright floors (built-out stories evident from the outside, BF) and six dim floors (built-in stories not apparent from the outside, DF), with transition columns and stiffening trusses around the exterior perimeter. The experimental results showed that the tested tower-style building had excellent seismic performance and reliable structural integrity. It only experienced minor damage when subjected to extremely high-intensity motions. The interstory drift, dynamic strain, and floor acceleration response of the core-tube region with eccentric steel braces were more significant under severe excitation than those with cross-centered symmetric steel braces. The vertical reaction increased at the upper floor of the tower under vertical acceleration, and differences in the dynamic response of the middle and upper floors were much more apparent after the test. Moreover, 3D numerical simulation models of the tested tower were established and validated against the test responses. Successively, the validated numerical model was used to investigate the influence of the transition column at different floors on the peak interstory drift response and the relevant strain distribution, and the proposal for a proper position of the transition column was recommended at the end.

A novel, practical, and computationally efficient probabilistic methodology for the stochastic generation of suites of fully non-stationary artificial accelerograms is presented. The proposed methodology ensures that the produced ground motion suites match a given target spectral mean and target variability for the whole period range of interest. This is achieved by first producing an ensemble of random target spectra with the given mean and variability and then using them to generate artificial, target spectrum-compatible, acceleration time-histories with spectral representation techniques. Spectral correlation can also be assumed for the generated ground motion spectra. Based on the same backbone, two different formulations are proposed for generating spectrum-compatible acceleration time-histories of the non-stationary kind. The distinction between these two variants lies in the techniques employed for modeling the temporal and spectral modulation, focusing on the site-compatibility of the produced records. The first approach uses past-recorded seismic accelerograms as seed records, and the second proposes and uses a new, probabilistic time-frequency modulating function. The outcome of the proposed methodology is suites containing site-compatible ground motion time-histories whose spectral mean and variability match those obtained from any of the usually employed target spectra used in the earthquake engineering practice. An online tool implementing the proposed methodology is also freely provided.

Synthetic ground motions (GMs) play a fundamental role in both deterministic and probabilistic seismic engineering assessments. This paper shows that the family of filtered and modulated white noise stochastic GM models overlooks a key parameter—the high-pass filter's corner frequency, $�f_c$. In the simulated motions, this causes significant distortions in the long-period range of the linear-response spectra and in the linear-response spectral correlations. To address this, we incorporate $�f_c$ as an explicitly fitted parameter in a site-based stochastic model. We optimize $�f_c$ by individually matching the long-period linear-response spectrum (i.e., $��S�a�\left(�T�\right)�$ for $��T�\ge �1��s�$) of synthetic GMs with that of each recorded GM. We show that by fitting $�f_c$ the resulting stochastically simulated GMs can precisely capture the spectral amplitudes, variability (i.e., variances of $��\log �\left(�S�a�\right(�T�\left)�\right)�$), and the correlation structure (i.e., correlation of