This paper proposes a comprehensive analysis framework, combining three-dimensional (3D) numerical modelling and metamodeling, to investigate the probabilistic performance of retrofit actions on coastal bridges subjected to extreme wave forces. Specifically, a 3D Computational Fluid Dynamics (CFD) model is developed to calculate extreme wave load on the bridge superstructure. The established 3D model is validated by laboratory experiments. The characteristics of wave forces are parametrically investigated, and an Artificial Neural Network (ANN) metamodel is utilised to quantify the loading effects with multiple surge and wave parameters. Such a numerical-based ANN metamodel could predict wave forces under variable scenarios accurately, and significantly reduce the high computational cost of the 3D numerical model. Based on the numerical and metamodeling results, the bridge fragility curve is derived by considering uncertainties associated with structural demand, capacity, and hurricane hazard. Long-term failure risk is assessed under different climate change scenarios. Furthermore, different retrofit methods to improve structural performance and reduce failure risk are examined according to the proposed framework, including inserting air venting holes, enhancing connection strengths, and elevating bridge structures. The proposed framework could facilitate the optimal and robust design and maintenance of coastal infrastructures under hurricane effects in a long-term time interval.
- 3D CFD model
- Artificial Neural Network
- Climate change
- Coastal bridge
- Probabilistic fragility model
ASJC Scopus subject areas
- Civil and Structural Engineering