Reliability based design optimization of bridges considering bridge-vehicle interaction by Kriging surrogate model

This paper presents a reliability-based design optimization method for bridge structures, considering the uncertainties of the material parameters and the effect of bridge-vehicle interaction. The uncertainties of system parameters, such as Young's modulus and mass density, are considered, which are simulated as Gaussian and/or lognormal random fields. The formulas used to represent the random fields of material properties and system matrices are derived. The Gaussian random inputs are approximated with Karhunen-Loeve (KL) expansion, while the lognormal random inputs are approximated with a combination of KL expansion and Polynomial Chaos (PC) expansion. Reliability-based design optimization analysis is conducted to determine the minimum required crosssection area of bridge structures under probability constraints, in which the failure probability is estimated from the Kriging surrogate model and Monte Carlo Simulation (MCS) method. Numerical studies on a simply supported beam under a moving force and a three-dimensional box-section bridge under a moving vehicle are conducted to investigate the efficiency and accuracy of the proposed approach. The reliability analysis results are compared with those obtained from MCS method, validating the accuracy of the proposed approach. With the proposed method, the minimum value of cross-section area of the bridge under the preset probability constraint can be obtained.

Automated summary

This paper presents a reliability-based design optimization method for bridge structures, which considers uncertainties of material parameters and bridge-vehicle interaction. The proposed approach is validated through numerical studies on simply supported beam and box-section bridge, demonstrating its efficiency and accuracy in determining the minimum required cross-section area under probability constraints.