Automated finite element model updating of a scale bridge model using measured static and modal test data. (1st November 2015)

• Record Type: Journal Article
• Authors: Sanayei, Masoud; Khaloo, Ali; Gul, Mustafa; Necati Catbas, F.
• Is Part Of: Engineering structures. Volume 102(2015:Nov. 01)
• Page Start: 66
• Page End: 79
• Publication Date: 2015-11-01
• Subjects: Finite element model calibration -- Nondestructive test data -- Parameter estimation -- Monte Carlo analysis -- Multiresponse parameter estimation -- Simultaneous stiffness and mass parameter estimation -- Structural health monitoring; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2015.07.029
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Multiresponse parameter estimation was used for simultaneous stiffness and mass estimations. Monte Carlo analysis was performed to identify error sensitive parameters. NDT data quality analysis was implemented to reduce measurement errors. A set of most observable and error tolerant parameters were estimated. Two separate commercial software packages were used for real-time FE model updating. Abstract: Structural Health Monitoring (SHM) using nondestructive test data has become promising for finite element (FE) model updating, model verification, structural evaluation and damage assessment. This research presents a multiresponse structural parameter estimation method for the automated FE model updating using data obtained from a set of nondestructive tests conducted on a laboratory bridge model. Both stiffness and mass parameters are updated at the element level, simultaneously. Having measurement and modeling errors is an inevitable part of data acquisition systems and finite element models. The presence of these errors can affect the accuracy of the estimated parameters. Therefore, an error sensitivity analysis using Monte Carlo simulation was used to study the input–output error behavior of each parameter based on the load cases and measurement locations of the nondestructive tests. Given the measured experimental responses, the goal was to select the unknown parameters of the FE model with high observability that leads to creating a well-conditioned system with the least sensitivity to measurement errors. A data quality study was performed to assess the accuracy and reliability of the measured data. Based on this study, a subset of the most reliable measured data was selected for the FE model updating. The selected subset of higher quality measurements and the observable unknown parameters were used for FE model updating. Three static and dynamic error functions were used for structural parameter estimation using the selected measured static strains, displacements, and slopes as well as dynamic natural frequencies and associated mode shapes. The measured data sets were used separately and also together for multiresponse FE model updating to match the predicted analytical response with the measured data. The FE model was successfully calibrated using multiresponse data. Two separate commercially available software packages were used with real-time data communications utilizing Application Program Interface (API) scripts. This approach was efficient in utilizing these software packages for automated and systematic FE model updating. The usefulness of the proposed method for automated finite element model updating at the element level is shown by being able to lead to simultaneous estimation of the stiffness and mass parameters using experimental data.