Anxin Guo

Probabilistic safety assessment of adjacent floating bodies under extreme waves requires accurate estimation of structural failure probability, which is hampered by strongly nonlinear hydrodynamic coupling and prohibitive simulation costs. This study develops an active-learning framework that couples a high-fidelity computational fluid dynamics numerical wave tank with Gaussian-process surrogate modeling and wave-group parameterization, achieving converged failure-probability estimates with fewer simulations than direct Monte Carlo. Applied to a simplified two-body system under a 100-year return-period sea state, the framework reveals a nonmonotonic dependence of failure probability on the gap-to-breadth ratio G / D , with minimum risk at G / D = 0.5 and increasing risk at wider spacings due to progressive interbody phase decoupling. Sensitivity analyses across multiple failure thresholds confirm the robustness of this trend.

This study aims to elucidate the joint effects of earthquakes and waves on the flow‐field evolution and hydrodynamic coupling mechanisms in the multi‐body system, focusing on how distinct body motion modes influence the diffraction and radiation effects of the target structure. A series of scaled‐model tests was conducted using particle image velocimetry technology. The results indicate that the dynamic energy of the target pontoon is concentrated within the dominant wave‐frequency band, and the amplification of high‐frequency components induced by short‐period waves is the primary cause of variations in wave‐force amplitudes. When seismic excitations are introduced, the structural motions in different DOFs of the preceding and target pontoons exert distinct effects on wave‐induced forces: surge and sway motions mainly generate larger horizontal forces and bending moments, whereas heave motion governs variations in the vertical force, and the high‐frequency seismic excitations will lead to a further increase in these loads, especially in the sway and surge directions. Flow‐field measurements show that seismic excitation mainly redistributes local TKE and Reynolds‐stress patterns, with the clearest changes occurring under surge motion, while the overall flow structure remains similar.

1. As an important component of the rapid transportation network in coastal areas, offshore bridges have been extensively constructed worldwide. However, most of these offshore bridges are under th...

Achieving efficient and non-destructive detection of concrete voids is crucial for ensuring the structural integrity of concrete structures. However, most existing methods focus solely on identifying void existence, lacking effective approaches for the simultaneous recognition of geometric parameters such as void size and depth. To address this limitation, this study proposes a Multi-task Deep Learning (MTDL) model based on the log-Mel spectrograms of percussion signals to achieve the simultaneous identification of the existence, size, and depth of concrete voids. Percussion signals of concrete under varying void conditions were acquired and transformed into log-Mel spectrograms as model inputs. Architecturally, residual block and Convolutional Block Attention Modules are integrated to enhance the extraction and focusing mechanisms of task-relevant features, with their specific contributions validated through ablation analysis. Experimental results demonstrate that the proposed model achieves high recognition accuracy across all three tasks while exhibiting robust anti-noise performance under various signal-to-noise ratio conditions. Furthermore, comparative evaluations with several representative models reveal that the proposed MTDL model maintains high accuracy with a smaller parameter scale and superior computational efficiency. Additionally, extrapolation tests are designed to evaluate the prediction stability of the model, revealing that it can still accurately detect void concrete samples even when the void depth exceeds the training data range, demonstrating favorable extrapolation capability for identifying concrete voids at greater depths.