TY - GEN
T1 - Applicable schemes for the Vehicle-Bridge Interaction System Identification method
AU - Yamamoto, K.
AU - Shin, R.
N1 - Publisher Copyright:
© 2023 The Author(s).
PY - 2023
Y1 - 2023
N2 - The VBISI (Vehicle-Bridge Interaction System Identification) method is a driveby monitoring method that estimates the vehicle’s and bridge’s parameters and road unevenness simultaneously only from vehicle vibration and position data. The estimation process randomly assumes the mechanical parameters of vehicle and bridge, and solves two processes, namely the IEP (Input Estimation Problem) of the vehicle and the DRS (Dynamic Response Simulation) of the bridge to estimate the road unevenness. It is realized by updating the mechanical parameters by minimizing the residual of the estimated road unevenness. This paper introduces and compares the potential schemes: PSO (Particle Swarm Optimization), NM (Nelder-Mead) method and MCMC (Monte Carlo Markov Chain). The differences between these schemes are also examined numerically in this study. PSO is very efficient but needs large calculation resources, NM is very efficient and shows high accuracy, MCMC is very costly but gives the reliable.
AB - The VBISI (Vehicle-Bridge Interaction System Identification) method is a driveby monitoring method that estimates the vehicle’s and bridge’s parameters and road unevenness simultaneously only from vehicle vibration and position data. The estimation process randomly assumes the mechanical parameters of vehicle and bridge, and solves two processes, namely the IEP (Input Estimation Problem) of the vehicle and the DRS (Dynamic Response Simulation) of the bridge to estimate the road unevenness. It is realized by updating the mechanical parameters by minimizing the residual of the estimated road unevenness. This paper introduces and compares the potential schemes: PSO (Particle Swarm Optimization), NM (Nelder-Mead) method and MCMC (Monte Carlo Markov Chain). The differences between these schemes are also examined numerically in this study. PSO is very efficient but needs large calculation resources, NM is very efficient and shows high accuracy, MCMC is very costly but gives the reliable.
UR - http://www.scopus.com/inward/record.url?scp=85186714217&partnerID=8YFLogxK
U2 - 10.1201/9781003323020-86
DO - 10.1201/9781003323020-86
M3 - Conference contribution
AN - SCOPUS:85186714217
SN - 9781003323020
T3 - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
SP - 709
EP - 716
BT - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
A2 - Biondini, Fabio
A2 - Frangopol, Dan M.
PB - CRC Press/Balkema
T2 - 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
Y2 - 2 July 2023 through 6 July 2023
ER -