Jinsheng Wang

Triple-box girders have recently emerged as an attractive cross-sectional form for long-span bridges, with their aerodynamic performance and vortex-induced vibration (VIV) strongly affected by slot gap spacing. However, for completed (as-built) triple-box configurations, a systematic understanding of how gap ratio governs aerodynamic performance, VIV, and the associated flow mechanisms remains limited. In this study, wind tunnel tests were performed to measure surface pressures and vibrations for five gap ratios, i.e., GW/H = 0.81, 1.62, 2.43, 3.24, and 4.05. Complementary computational fluid dynamics simulations were conducted to examine the vorticity evolution and clarify the underlying excitation mechanisms. The results indicate that gap ratio influences the mean pressure coefficient, force coefficients, fluctuating pressure coefficient, mean square amplitude spectra of fluctuating pressures, Strouhal number (St), VIV, and flow characteristics to different extents. In particular, the effects of gap ratio are more evident for the midstream and downstream girders than for the upstream girder. Further examination of St values derived from different dominant frequencies reveals a clear transition in the frequency domain response at GW/H = 2.43, whereas the maximum VIV amplitude is observed at GW/H = 3.24. Flow field visualization reveals that vortex shedding from the upstream girder interacting with slot gap recirculation amplifies unsteadiness around the midstream and downstream girders, while inter-girder interference weakens as the gap ratio increases. These findings improve understanding of how flow evolution governs aerodynamic interference and VIV in completed triple-box girders.

Structural reliability analysis poses a considerable challenge in engineering practice, leading to the development of various state-of-the-art methods. Active learning methods, known for their superior performance, have been extensively investigated for assessing failure probabilities in structural reliability analysis. This paper aims to develop an efficient and accurate Kriging-based active learning method for structural reliability analysis, incorporating a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, a novel learning function allocation scheme is introduced to address the challenge of no single learning function demonstrating superior performance across various engineering contexts. Six active learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, are used to form a portfolio of functions in the allocation scheme. The use of the U learning function initiates the active learning process with a truncated candidate sample pool, alleviating computational burden while maintaining accuracy. Additionally, a hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. The performance of the proposed method is evaluated through four numerical examples and one engineering case, demonstrating its accuracy and efficiency.

To facilitate the establishment of the probabilistic model for quantifying the vulnerability of coastal bridges to natural hazards and support the associated risk assessment and mitigation activities, it is imperative to develop an accurate and efficient method for wave forces prediction. With the fast development of computer science, surrogate modeling techniques have been commonly used as an effective alternative to computational fluid dynamics for the establishment of a predictive model in coastal engineering. In this paper, a hybrid surrogate model is proposed for the efficient and accurate prediction of the solitary wave forces acting on coastal bridge decks. The underlying idea of the proposed method is to enhance the prediction capability of the constructed model by introducing an additional surrogate to correct the errors made by the main predictor. Specifically, the regression-type polynomial chaos expansion (PCE) is employed as the main predictor to capture the global feature of the computational model, whereas the interpolation-type Kriging is adopted to learn the local variations of the prediction error from the PCE. An engineering case is employed to validate the effectiveness of the hybrid model, and it is observed that the prediction performance (in terms of residual mean square error and correlation coefficient) of the hybrid model is superior to the optimal PCE and artificial neural network (ANN) for both horizontal and vertical wave forces, albeit the maximum PCE degrees used in the hybrid model are lower than the optimal degrees identified in the pure PCE model. Moreover, the proposed hybrid model also enables the extraction of explicit predictive equations for the parameters of interest. It is expected that the hybrid model could be extended to more complex wave conditions and structural shapes to facilitate the life-cycle structural design and analysis of coastal bridges.