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Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions. [ABSTRACT FROM AUTHOR]

The flexibility of the foundation system significantly affects the seismic and operational performance of integral abutment bridges (IAB). The so-called pile isolation system can lead to higher flexibility in pile foundations. It consists in backfilling the pile hole with high-damping materials up to a certain depth from the surface level. However, the impact of this solution in increasing the lateral flexibility and reducing the seismic demand strongly depends on the scale factor and pile diameter. Most investigations on this topic are based on experimental tests on scaled pile specimens. This paper explores the pile isolation system's effectiveness by conducting a multivariate sensitivity analysis of the seismic demand of an IAB structural archetype. The IAB archetype is modelled as a Winkler beam with a piece-wise definition of the subgrade stiffness and equivalent viscous damping, simulating the responses of the soil and high-damping particles. The simulated data are then used to calibrate a probabilistic formulation of the seismic demand reduction due to the pre-hole. The formulation, calibrated following a Bayesian approach, is used to derive estimates of the q-factor associated with the damping pre-hole for possible use in engineering practice. The analyses demonstrate that pile isolation with high-damping material can be effective but possesses a limited dissipating capacity, with a seismic reduction factor of approximately 1 and 2. [ABSTRACT FROM AUTHOR]

The dynamic response of post-tensioned rocking walls in a mass timber building can be reduced to a single-degree-of-freedom (SDOF) model. In this model, the rocking wall panel is simplified as a rigid block, while the base rotation represents the degree of freedom of the entire structure. The paper presents an analytical approach to develop and calibrate this nonlinear model using shake table tests of a full-scale two-story building with CLT rocking walls. The experimental data are used to estimate the parameters of the governing equation using least-squares optimization. The correlation between the obtained parameters and the cumulative dissipated energy led to a nonlinear model with degradation behavior captured. After that, the calibrated model was used to assess the fragility functions of the structure under repetitive seismic events. [ABSTRACT FROM AUTHOR]