Automated Finite Element Model Updating of the UCF Grid Benchmark Using Multiresponse Parameter Estimation

Structural Health Monitoring (SHM) using nondestructive test (NDT) data has become very 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 FE model updating using data obtained from a nondestructive test on a laboratory bridge model. 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 parameter estimates. 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. This method is applicable to full-scale structures and can be used for bridge model validation and bridge management.

[1]  Jean-Claude Golinval,et al.  Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures , 2001 .

[2]  Mustafa Gul,et al.  Damage assessment using flexibility and flexibility-based curvature for structural health monitoring , 2008 .

[3]  Daniel L. Balageas.  Introduction to Structural Health Monitoring , 2010 .

[4]  Masoud Sanayei,et al.  Damage assessment of structures using static test data , 1991 .

[5]  Masoud Sanayei,et al.  Parameter Estimation of Structures from Static Strain Measurements. I: Formulation , 1996 .

[6]  Chan Ghee Koh,et al.  A hybrid computational strategy for identification of structural parameters , 2003 .

[7]  Masoud Sanayei,et al.  Multiresponse Parameter Estimation for Finite-Element Model Updating Using Nondestructive Test Data , 2007 .

[8]  Masoud Sanayei,et al.  Significance of Modeling Error in Structural Parameter Estimation , 2001 .

[9]  Gregory D. Buckner,et al.  Structural health monitoring and damage detection using an intelligent parameter varying (IPV) technique , 2004 .

[10]  Jason Lee Burkett.  Benchmark Studies For Structural Health Monitoring Using Analytical And Experimental Models , 2005 .

[11]  Randall J. Allemang,et al.  THE MODAL ASSURANCE CRITERION–TWENTY YEARS OF USE AND ABUSE , 2003 .

[12]  Michael J. Saletnik,et al.  Parameter Estimation of Structures from Static Strain Measurements. II: Error Sensitivity Analysis , 1996 .

[13]  Arthur J. Helmicki,et al.  Structural Identification for Condition Assessment: Experimental Arts , 1997 .

[14]  Masoud Sanayei,et al.  STRUCTURAL MODEL UPDATING USING EXPERIMENTAL STATIC MEASUREMENTS , 1997 .

[15]  Yeong-Bin Yang,et al.  Dynamic Testing and System Identification of a Multi-Span Highway Bridge , 1999 .

[16]  Christopher DiCarlo.  Applications of statistics to minimize and quantify measurement error in finite element model updating , 2008 .

[17]  Jesse D. Sipple.  Frequency response function based finite element model updating , 2013 .

[18]  Ian F. C. Smith,et al.  Model Identification of Bridges Using Measurement Data , 2005 .

[19]  A. Emin Aktan,et al.  Structural identification of constructed systems : approaches, methods, and technologies for effective practice of St-Id , 2013 .

[20]  R. J. Barthorpe,et al.  On model- and data-based approaches to structural health monitoring , 2010 .