Browsing by Author "Conte, Joel P."

Now showing 1 - 7 of 7
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    (Springer New York LLC, 33) Ramancha, Mukesh Kumar; Madarshahian, Ramin; Astroza, Rodrigo; Conte, Joel P.; 33
    Bayesian nonlinear finite element (FE) model updating using input and output measurements have emerged as a powerful technique for structural health monitoring (SHM), and damage diagnosis and prognosis of complex civil engineering systems. The Bayesian approach to model updating is attractive because it provides a rigorous framework to account for and quantify modeling and parameter uncertainty. This paper employs the unscented Kalman filter (UKF), an advanced nonlinear Bayesian filtering method, to update, using noisy input and output measurement data, a nonlinear FE model governed by a multiaxial material constitutive law. Compared to uniaxial material constitutive models, multiaxial models are typically characterized by a larger number of material parameters, thus requiring parameter estimation to be performed in a higher dimensional space. In this work, the UKF is applied to a plane strain FE model of Pine Flat dam (a concrete gravity dam on King’s River near Fresno, California) to update the time-invariant material parameters of the cap plasticity model, a three-dimensional non-smooth multi-surface plasticity concrete model, used to represent plain concrete behavior. This study considers seismic input excitation and utilizes numerically simulated measurement response data. Estimates of the multi-axial material model parameters (for the single material model used in this study) are non-unique. All sets of parameter estimates yield very similar and accurate seismic response predictions of both measured and unmeasured response quantities.
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    (Springer New York LLC, 33) Astroza, Rodrigo; Alessandri, Andres; Conte, Joel P.; 33
    A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.
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    (33, 33) Sun, Lin; Conte, Joel P.; Todd, Michael D.; Astroza, Rodrigo; Bock, Yehuda; Offield, Glen; Vernon, Frank; 33
    This paper focuses on system identification (SID) of the UC San Diego Geisel Library building using ambient vibration (AV) data, assuming that the building’s behavior can be fully described by linear models in terms of material, geometry, damping, etc. Three state-space-based, output-only time domain SID methods are applied and fully automated to identify the library’s modal properties using AV data from both a 15-day and a 486-day monitoring period. The modes identified from the AV data are higher-order coupled torsional-flexural modes. The identified modal properties are influenced by the atmospheric conditions, and the amplitude of the building’s ambient vibration. The time-varying identified modal properties show a cyclical 1-day pattern due to human activity, earth tremors, and short-term changes in atmospheric conditions such as wind speed and temperature. One of the output-only SID methods was used to estimate modal properties from ambient vibration data recorded continuously over a 486-day period, including three low-intensity earthquakes. No permanent changes in the identified modal properties were observed due to the three low-intensity earthquakes that occurred during that period. Renovations of the Geisel Library involving only non-structural components (e.g., non-load-bearing partition walls, changes in space allocations, and inertial/live loads) caused some discontinuities in the identification of the modes of interest in this study. The influence of the data window length on system identification results, in terms of identification success rate and estimation uncertainty, was investigated. The identified state-space models are also used to assess the relative contribution of the ambient base excitation to the building’s total ambient vibrational response. This research offered a unique opportunity to study linear SID of a large and complex real-world structure under ambient excitations, and the effects of changing environmental conditions on the identified modal properties. It provided insight into some of the causes of the observed temporal variation in the identified modal properties. The SID results presented in this study also provide a baseline for future structural health monitoring studies of the Geisel Library building.
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    (33, 33) Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.; Restrepo, José I.; Hutchinson, Tara C.; 33
    Damage detection (DD) is one of the primary goals of health monitoring of civil structures. Vibration-based techniques aim to identify the modal properties (or vibration characteristics) of structures and are one of the most popular approaches for structural damage detection. The use of vibration characteristics (natural frequencies, damping ratios, and mode shapes) for DD purposes is based on the premise that these characteristics change when the structure suffers damage, since the modal properties depend on the physical properties (mass, stiffness, and damping) of the structure. The use of output-only measurements (e.g., ambient vibration or AV) is the most popular approach for damage detection of civil structures. AV data recorded before and after the structure has potentially suffered damage can be used for DD. However, application of vibration-based DD using AV requires an accurate and reliable estimation of the modal properties and their variability (or uncertainty) in order to genuinely determine the existence of damage. This study presents a comprehensive statistical analysis of the identified modal properties of a full-scale five-story reinforced concrete building using AV data. The building specimen was tested on the NHERI@UCSD shake table in base-isolated and fixed-base configurations. On April 6, 2012, about two weeks before the start of the seismic tests in the base-isolated configuration, a dense array of twenty accelerometers was deployed on the structure and AV data were recorded continuously until May 18, 2012, three days after completion of the seismic tests in the fixed-base configuration. In its fixed-base configuration, the building was subjected to a sequence of six earthquake motions that progressively damaged the structure. Two popular methods of operational modal analysis are used to automatically identify the modal properties of the fixed-base building at different damage states using the recorded AV data. A statistical analysis of the identified modal parameters is performed to investigate the statistical variability and accuracy of the system identification results. The variability of the identified modal parameters due to environmental conditions is also investigated.
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    Bayesian Nonlinear Finite Element Model Updating of a Full-Scale Bridge-Column Using Sequential Monte Carlo
    (Springer) Ramancha, Mukesh K.; Astroza, Rodrigo; Conte, Joel P.; Restrepo, Jose I.; Todd, Michael D.
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    Bayesian updating and identifiability assessment of nonlinear finite element models
    Ramancha, Mukesh K.; Astroza, Rodrigo; Madarshahian, Ramin; Conte, Joel P.
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    Seismic response analysis and modal identification of a full-scale five-story base-isolated building tested on the NEES@UCSD shake table
    Astroza, Rodrigo; Conte, Joel P.; Restrepo, José I.; Ebrahimian, Hamed; Hutchinson, Tara