Finite element model updating of steel-concrete composite bridge: A study case of the Ruri bridge in Vietnam

Abstract

The study presents the finite element (FE) model update of the existing simple-spans steelconcrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.

Keywords

• vibration analysis
• accelerometer
• fe model update
• concrete bridge
• particle swarm optimisation
• genetic algorithm