Title

Predictive Analysis by Incorporating Uncertainty through a Family of Models Calibrated with Structural Health-Monitoring Data

Authors

Authors

N. Catbas; H. B. Gokce;D. M. Frangopol

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Eng. Mech.-ASCE

Keywords

FEM model; Structural health monitoring; Bridge; Uncertainty; Reliability; Artificial neural network (ANN); Calibration; Updating; Family of models; AMBIENT VIBRATION; BRIDGE; BEHAVIOR; Engineering, Mechanical

Abstract

Complex analysis and design of structures, especially landmark structures such as long-span bridges, have been conducted by many engineers and researchers. Currently, it is possible to collect more and precise monitoring data as well as to develop complex three-dimensional (3D) FEM models. These models, which can be calibrated using structural health-monitoring (SHM) data, can be used for the estimation of component and system reliability of bridges. However, the uncertainties related to the data, analysis, and nonstationary nature of the structural behavior need to be better incorporated by using a set of models that are continuously updated with monitoring data. This set of models constitutes a family as a result of the approach by which the models are obtained and the relationships among them. The objective of this paper is to explore the impact of uncertainty in predicting the system reliability obtained by a one-time, initially calibrated FEM model as well as by a family of FEM models continuously calibrated with monitoring data. To explore the uncertainty effects, a laboratory structure that has a combined system configuration with main and secondary elements is monitored. The monitoring data are employed for the FEM model calibration by using artificial neural networks (ANNs) to obtain parent (calibrated) FEM models from which a set of offspring FEM models is generated to incorporate the uncertainties. It is shown that the use of parent-offspring FEM models becomes important especially when critical parameters that have an impact on the model responses cannot be precisely defined. Finally, it is shown in a comparative fashion that the prediction of reliability using a family of FEM models and a single model can be quite different because the family of models provides a more realistic estimate of the structural responses and probability of failure. DOI: 10.1061/(ASCE)EM.1943-7889.0000342. (C) 2013 American Society of Civil Engineers.

Journal Title

Journal of Engineering Mechanics-Asce

Volume

139

Issue/Number

6

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

712

Last Page

723

WOS Identifier

WOS:000318569600005

ISSN

0733-9399

Share

COinS