Yongxin Wu

Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals. The current traditional denoising methods together with deep learning-based data-driven approaches encounter difficulties when they need to remove noise from seismic signals while keeping their fundamental structural elements, especially under conditions of low signal-to-noise ratios. In this study, we propose a novel denoising framework that integrates a physics-guided neural network with adaptive time–frequency decomposition, referred to as TF-PhysNet. The system breaks down broadband seismic data into separate frequency bands. Scientists can use these to study specific noise patterns that appear at various frequency points. The system uses a shared convolutional neural network-long short-term memory architecture to remove noise from each sub-band, which helps it learn both short-term waveform patterns and extended temporal relationships. The system uses physics-guided restrictions to eliminate false signal variations, which appear during the signal recovery process. The experimental findings from synthetic and real seismic data sets show that TF-PhysNet delivers better results than standard denoising techniques and deep learning-based methods for signal-to-noise ratio improvement and correlation coefficient enhancement.

A nonlinear tuned liquid column damper with inerter and negative stiffness (NS‐TLCDI) is proposed for structural vibration control with frequency tunability. The close‐form transfer functions of different responses are presented by introducing an equivalent linearization method. The Routh–Hurwitz stability criterion is applied to confirm the stability of system with negative stiffness. Subsequently, analytical solutions for the H ₂ optimized frequency ratio and damping ratio of NS‐TLCDI under a white noise excitation are derived based on different engineering requirements that prioritize either absolute acceleration or relative displacement, the validity of which is verified by comparison with corresponding numerical solutions. Furthermore, the vibration suppression of NS‐TLCDI in the frequency domain is assessed compared with a tuned liquid column damper inerter (TLCDI). The results indicate that NS‐TLCDI offers a significant advantage over TLCDI in reducing the mean square values of the dynamic responses of the main structure, which becomes more pronounced as the absolute value of negative stiffness increases within the stability requirements. Moreover, the robustness of the system equipped with NS‐TLCDI has also been considerably enhanced under this trend. Finally, a detailed time‐history response analysis is conducted to evaluate the vibration control performance of NS‐TLCDI under 44 real earthquake excitations. Both in terms of peak values and overall, the time‐history responses of the structure equipped with optimal NS‐TLCDI are consistently lower than that of optimal TLCDI. The energy dissipation of systems is further examined, indicating that the dashpot of NS‐TLCDI remains the dominant dissipation component.

Soil strength exhibits natural spatial variability, which obscures the stochastic link between failure mechanisms and bearing capacity. This study establishes an analytical framework that couples multiple kinematically admissible mechanisms with random field theory to quantify the undrained bearing capacity of footings on soil with spatially variable strength. The analytical solutions show good agreement with numerical simulations in evaluating the undrained bearing capacity. Subsequently, the influence of spatial variability in undrained shear strength on the statistics of bearing capacity was investigated, providing insight into the distribution of critical failure mechanisms. Furthermore, this study introduces a failure-frequency map representing the spatial distribution of failure possibility, which can serve as a diagnostic tool to guide ground improvement from a random field perspective. Overall, the analytical model offers direct insights into critical failure mechanisms and their associated influence on bearing capacity under random field conditions, while still retaining the fidelity of probabilistic analyses involving extensive Monte Carlo simulations.

The Newmark sliding‐block displacement ( D ) has been widely used to evaluate the seismic performance of slopes in both site‐specific and regional scales. Current predictive models for D of slopes are generally developed without consideration of the specific slope orientation. This study presents the directionality analysis of preferential polarization for D and a model to predict the D considering the specific slope orientation in strike‐slip earthquakes. Based on a database of 817 ground motion records with strike‐slip faulting, it is shown that the orientation of maximum D tends to occur close to the transverse orientation (perpendicular to the orientation at a given site pointing to the epicenter). There is a significantly larger probability of exceeding the median displacement ( D ₅₀ ) over all orientations in the transverse orientation. The orientational D is strongly correlated with the angular difference with respect to the transverse orientation. The orientation‐dependent model is developed to estimate D at specific orientations by combining with an existing D ₅₀ predictive relationship. The model enables the transformation of D ₅₀ to D in any specific orientation, and hence allowing for the site‐specific and regional landslide hazard assessment with consideration of specific slope orientation in strike‐slip earthquakes.

Skirted foundations are widely used in offshore and subsea engineering. Previous studies have shown that soil undrained shear strength variability has a notable impact on probabilistic analyses of skirted foundation bearing capacity. This study proposes an efficient machine-learning method to predict the uniaxial bearing capacity factors of skirted foundations under pure horizontal and moment loads, without relying on traditional time-consuming random finite element methods. A two-dimensional convolutional neural network is adopted to capture the potential correlation between soil random fields and bearing capacity factors. The proposed CNN-based model exhibits satisfactory prediction performance with regard to coefficients of variation and scale of fluctuations in two directions. Specifically, coefficient of determination (R2) values exceed 0.97, while root mean square error (RMSE) values remain below 0.13 for the surrogate model. In addition, more than 96% of the predictions are associated with a relative error of 5% or less, providing evidence of the proposed 2D-CNN model’s satisfactory prediction performance.

This study investigated the seismic response of rectangular tunnels with various embedment depths considering the spatial variability of soil shear modulus. The spectral representation method was adopted to simulate the anisotropic random field of soil. The excess pore water pressure, the liquefied zone, the ground displacement and the uplift displacement of the tunnel were obtained through the random finite difference method to analyze the seismic response. It was observed that the soil excess pore water pressure ratio under the tunnel gradually decreased and the liquefaction degree reduced with depth increase. The peak value of the liquefied zone range increased with the increase in embedment depth. The mean response of stochastic analysis was smaller than the deterministic calculation results when the tunnel embedment depth was less than 10 m. The maximum tunnel floating displacement obtained from random analyses had the probability of 67.3%, exceeding the value calculated by deterministic analyses when H = 12 m.