Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data
https://orcid.org/0000-0002-3691-2752
https://orcid.org/0000-0001-9037-0176
Bridge weigh-in-motion (B-WIM) systems are employed for monitoring traffic weights, providing useful information for management decisions. Many applications were proposed based on the information collected, such as calculation of influence lines and damage detection. In this work, an additional application is addressed, to perform model updating of structural parameters from information collected during the calibration of B-WIM systems. The goal of model updating techniques is to adjust the model parameters in order to achieve better agreement between predicted and experimental responses. Therefore, the resulting updated model is able to provide valuable information for decision makers. For many civil engineering applications, the updated parameters may have an inherent variability during the execution of the experimental procedure, since some external effects, such as environmental conditions, may change considerably along the process. To account for this inherent variability properly, a hierarchical Bayesian strategy is adopted. Results for both numerically simulated signals and a real engineering calibration procedure indicate that the proposed hierarchical Bayesian model updating approach is able to perform suitable estimates.
Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data
https://orcid.org/0000-0002-3691-2752
https://orcid.org/0000-0001-9037-0176
Bridge weigh-in-motion (B-WIM) systems are employed for monitoring traffic weights, providing useful information for management decisions. Many applications were proposed based on the information collected, such as calculation of influence lines and damage detection. In this work, an additional application is addressed, to perform model updating of structural parameters from information collected during the calibration of B-WIM systems. The goal of model updating techniques is to adjust the model parameters in order to achieve better agreement between predicted and experimental responses. Therefore, the resulting updated model is able to provide valuable information for decision makers. For many civil engineering applications, the updated parameters may have an inherent variability during the execution of the experimental procedure, since some external effects, such as environmental conditions, may change considerably along the process. To account for this inherent variability properly, a hierarchical Bayesian strategy is adopted. Results for both numerically simulated signals and a real engineering calibration procedure indicate that the proposed hierarchical Bayesian model updating approach is able to perform suitable estimates.
Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data
https://orcid.org/0000-0002-3691-2752
https://orcid.org/0000-0001-9037-0176
Bridge weigh-in-motion (B-WIM) systems are employed for monitoring traffic weights, providing useful information for management decisions. Many applications were proposed based on the information collected, such as calculation of influence lines and damage detection. In this work, an additional application is addressed, to perform model updating of structural parameters from information collected during the calibration of B-WIM systems. The goal of model updating techniques is to adjust the model parameters in order to achieve better agreement between predicted and experimental responses. Therefore, the resulting updated model is able to provide valuable information for decision makers. For many civil engineering applications, the updated parameters may have an inherent variability during the execution of the experimental procedure, since some external effects, such as environmental conditions, may change considerably along the process. To account for this inherent variability properly, a hierarchical Bayesian strategy is adopted. Results for both numerically simulated signals and a real engineering calibration procedure indicate that the proposed hierarchical Bayesian model updating approach is able to perform suitable estimates.
Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data
J. Bridge Eng.
Gonçalves, Matheus Silva (Autor:in) / Holdorf Lopez, Rafael (Autor:in) / Valente, Amir Mattar (Autor:in)
01.05.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Prediction Error Variances in Bayesian Model Updating Employing Data Sensitivity
| Online Contents | 2016
Prediction Error Variances in Bayesian Model Updating Employing Data Sensitivity
| Online Contents | 2016
| BASE | 2015