Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

Abstract

This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.

Straddle-Type Monorail State Assessment Based on Adaptive Whale Optimization Algorithm

“…Track beams are typically made of reinforced concrete materials, and the assessment methods for them have mainly been borrowed from highway bridges. In recent years, a series of bridge condition assessment methods have been established such as reliability theory [4][5] , hierarchical analysis [6] , machine learning [7] , finite element model (FEM) modification [8][9][10] , and so on. The finite element model modification method has garnered significant interest due to its clear physical meaning and strong mechanical analysis capabilities.…”