Surrogate-based fragility analysis and probabilistic optimisation of cable-stayed bridges subject to seismic loads. (1st April 2022) - Franchini, Andrea; Sebastian, Wendel; D'Ayala, Dina
Highlights: Parameterised fragility functions used for PEER design of cable-stayed bridges. Sensitivity analysis reveals conflicting demands and crucial variables interaction. Four optimisation strategies are proposed for damage probability and repair cost. The model of a real cable-stayed bridge is updated and optimised. A 40% reduction in construction and repair cost is achieved. Abstract: The lack of computationally efficient, probabilistic performance-based design strategies for cable-stayed bridges hinders their optimal design in seismic regions. Thus, this paper proposes the implementation of parameterised fragility functions (PFFs) for the surrogate-based sensitivity analysis and performance-based optimisation of these structures. In particular, PFFs are exploited to define computationally efficient decision variables and four optimisation strategies that aim to optimise the probability of seismic damage and the direct losses related to the seismic repair cost. To illustrate this overall strategy, a 542 m three-span cable-stayed bridge is considered. The ability to predict dynamic behaviour is improved by using the measured dynamic response of the reference cable-stayed bridge to inform the numerical model's architecture. Then, key design variables of tower and cable cross-sections are selected for optimisation. The sensitivity analysis shows conflicting demands placed upon design variables by different decision variables (e.g. damage probability and the sum of construction and repair costs, CRC) and the crucial importance of accounting for design variables interaction when making choices on design updating. Therefore, numerical optimisation emerges as the most efficient tool to deal with these issues. With respect to the reference structure, the proposed single-objective optimisation strategies reduced the system-level damage probability by 3.5 times, and yielded an 88% reduction of repair cost, and a 40% decrease of CRC. A Pareto-front of optimal design variables was also calculated to simultaneously optimise system damage probability and CRC.