Uncertainty and Sensitivity Analysis of Damage Identification Results Obtained Using Finite Element Model Updating

Abstract

A full-scale seven-story reinforced concrete shear wall building structure was tested on the UCSD-NEES shake table in the period October 2005 -January 2006. The shake table tests were designed so as to damage the building progressively through several historical seismic motions reproduced on the shake table. A sensitivity-based finite element (FE) model updating method was used to identify damage in the building. The estimation uncertainty in the damage identification results was observed to be significant, which motivated the authors to perform, through numerical simulation, an uncertainty analysis on a set of damage identification results. This study investigates systematically the performance of FE model updating for damage identification. The damaged structure is simulated numerically through a change in stiffness in selected regions of a FE model of the shear wall test structure. The uncertainty of the identified damage (location and extent) due to variability of five input factors is quantified through analysis-of-variance (ANOVA) and meta-modeling. These five input factors are: (1-3) level of uncertainty in the (identified) modal parameters of each of the first three longitudinal modes, (4) spatial density of measurements (number of sensors), and (5) mesh size in the FE model used in the FE model 1 To whom correspondence should be addressed. E-mail: jpconte@ucsd.edu 2 updating procedure (modeling error). A full factorial design of experiments is considered for these five input factors. In addition to ANOVA and meta-modeling, this study investigates the one-at-a-time sensitivity analysis of the identified damage to the level of uncertainty in the identified modal parameters of the first three longitudinal modes. The results of this investigation demonstrate that the level of confidence in the damage identification results obtained through FE model updating, is a function of not only the level of uncertainty in the identified modal parameters, but also choices made in the design of experiments (e.g., spatial density of measurements) and modeling errors (e.g., mesh size). Therefore, the experiments can be designed so that the more influential input factors (to the total uncertainty/variability of the damage identification results) are set at optimum levels so as to yield more accurate damage identification results.

Hierarchical Bayesian Model Updating for Probabilistic Damage Identification

“…This reduction will not be observed when the uncertainties of the updating structural parameters are estimated by a frequentist approach; the estimated uncertainties converge with increasing the number of data. In frequentist approach, the distributions of updating parameters are estimated from the deterministically identified parameters corresponding to different test data [15,36,37]. This inconsistency is due to the estimation of conceptually different uncertainties in the two approaches.…”

Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

“…The robustness of model and measurement uncertainties is one of the important issues in model updatingbased damage detection or system identification studies (Mottershead and Friswell, 1993;Beck and Katafygiotis, 1998;Moaveni et al, 2009). To investigate the robustness of the proposed damage detection methodology in terms of measurement uncertainties, the first eight modes of the actual damaged structure are used in the damage identification.…”

A novel finite element model updating method based on substructure and response surface model

“…By calculating the relationship between the parameters and structural properties, the sensitivity of the parameters can be obtained [5]. Combining the sensitivity-based finite element model updating method with uncertainty analysis, the finite element model of a seven-story reinforced concrete wall building structure is updated by Moaveni et al [6]; the finite element model of a certain concrete-filled steel tubular arch bridge is updated by Jaishi et al [7] based on sensitivity analysis; the first-order Taylor-series expansion of the eigenvalues are applied to improve the sensitivity-based updating approach by Zhang et al [8], and the proposed method is verified by updating a 1/150 scaled suspension bridge; The modal flexibility are adopted as the objective function, then the sensitivity-based method is employed by Jaishi et al [9] to update the finite element model of a reinforced concrete beam; combining the sensitivity analysis with the two-levels neural network, Lu et al [10] improve the SA-based finite element model http updating method, and a numerical case study is engaged to verify the proposed algorithm.…”