Journal Article


Arturo González
Eugene J. O'Brien
Jason Dowling



cross entropy bridge weigh in motion bridge wim optimisation weigh in motion vehicle bridge interaction vbi finite element model

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem (2012)

Abstract Moving Force Identification (MFI) theory can be used to create an algorithm for a Bridge Weigh-in-Motion (WIM) system that can produce complete force histories of the loads that have traversed a bridge structure. MFI is based on general inverse theory, however, and calibration of such a system requires a complete Finite Element (FE) model of the bridge to be available for implementation in the field. This is something that is often infeasible in practice as FE models created using theoretical values for material properties bear a poor relation to reality. The Cross Entropy optimisation method has been adapted here to address this calibration problem. The general system FE global mass and stiffness matrices of the bridge FE model are found by best fit optimisation to match field measurements. In this fashion a fully automated calibration procedure is developed for an MFI algorithm. This system is tested theoretically using three different FE plate models, coupled with a 3-dimensional vehicle model, allowing for Vehicle–Bridge Interaction (VBI).
Collections Ireland -> University College Dublin -> Earth Institute Research Collection
Ireland -> University College Dublin -> Institutes and Centres
Ireland -> University College Dublin -> UCD Earth Institute
Ireland -> University College Dublin -> Civil Engineering Research Collection
Ireland -> University College Dublin -> College of Engineering & Architecture
Ireland -> University College Dublin -> School of Civil Engineering

Full list of authors on original publication

Arturo González, Eugene J. O'Brien, Jason Dowling

Experts in our system

Arturo González
University College Dublin
Total Publications: 106
Eugene J. O'Brien
University College Dublin
Total Publications: 192