Pavement Strategy Optimization of Cable-Stayed Bridges against the Negative Reaction Risks of Auxiliary Piers

Abstract

Because the adjustment of the stay cable tension and girder counterweight is limited at the operation stage it is a difficult problem to control the negative reaction risk of the auxiliary pier (NRRAP) caused by multisource construction uncertainties and traffic growth. This paper proposes a pavement strategy optimization to control the NRRAP by adjusting the pavement thickness. The pavement strategy optimization is formulated as a reliability-constrained, multiobjective optimization problem, which is resolved by the nondominated sorting genetic algorithm (NSGA-II). A sensitivity analysis and a reliability analysis based on the generalized regression neural network (GRNN) surrogate model were performed to illustrate the significance of the uncertainty level in auxiliary pier negative reactions. The Pareto front examines the balance of construction cost, driving comfort and specified reliability threshold. The efficiency and accuracy of the proposed method are validated in a real cable-stayed bridge, and the results exhibit its advantages in controlling the NRRAP.