Browsing by Author "Li, Yong"

Now showing 1 - 7 of 7
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    (33, 33) Birrell, Matías; Li, Yong; Astroza, Rodrigo; 33
    A key aspect of performance assessment of structures is the quantification and propagation of uncertainties, from the estimation of hazards to possible losses. In particular, probabilistic structural analysis deals with aleatory and epistemic sources of uncertainty in nonlinear modeling. Materials and components in structural models are represented by uncertain parameters, which can be accounted for via probabilistic constitutive models. The variability at a local level is then propagated to the system level when the structural model is sampled, sometimes inducing great uncertainty in structural demands. However, probabilistic modeling of real structures via finite element (FE) models has been a challenge due to high computational costs. One avenue to reduce this cost and make probabilistic modeling viable in practice is to develop cost-effective surrogate models. In this paper, a Gaussian Process (GP) approach is proposed to study the composition of parameter-induced uncertainty in mechanics-based nonlinear FE structural model responses. The methodology is validated by evaluating common regression error metrics between the original FE models and their GP surrogates. Case studies of two structures are presented, including a five-story reinforced concrete (RC) building and a five-span RC highway bridge. Finally, the low computational cost of the surrogate models is leveraged to perform simulation-based global sensitivity analysis using Sobol indices to quantify parameter-induced uncertainty in structural responses.
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    (33, 33) Liu, Zhenning; Abtahi, Shaghayegh; Astroza, Rodrigo; Li, Yong; 33
    Nonlinear finite-element model updating (FEMU) is a promising approach for post-event damage assessment of civil structures. This paper conducted FEMU for a full-scale reinforced concrete bridge column tested under a sequence of earthquakes and examined the evolution of seismic damage across different earthquake ground motions. It was found that using experimental data, FEMU can be applied to identify unknown key model parameters (e.g. bond-slip and core concrete parameters) and damage of the columns as represented by the change of the key parameters. In addition, the updated models demonstrated their ability to better predict the system response for future earthquakes.
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    (33, 33) Chen, Yuxiang; Castiglione, Juan; Astroza, Rodrigo; Li, Yong; 33
    Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.
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    (33, 33) Li, Yong; Castiglione, Juan; Astroza, Rodrigo; Chen, Yuxiang; 33
    To enable optimal building energy management in response to the ever-changing building and boundary conditions, it is critical to have numerical models that can provide accurate online prediction based on economically measurable inputs and feedback. The present study explores the capabilities of using the unscented Kalman filter (UKF) in combination with resistance-capacitance (RC) models for online estimation of the thermal dynamics of single detached houses. A joint state-parameter UKF estimation approach is applied to estimate unknown state and model parameters by using fictitious process equations to augment the state vector to include model parameters. The performance of this approach is evaluated by comparing the estimated state values to the monitored data. In addition, the prediction capability of the updated model is also investigated. The estimation procedure, mathematical operations, and result analysis are presented in detail. The remarkable model performance achieved shows that the UKF can efficiently improve RC models’ predictability and enable timely online model updating and response prediction.
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    (33, 33) Liu, Zhenning; Li, Yong; Astroza, Rodrigo; 33
    Stochastic filtering strategies, such as the unscented Kalman filter (UKF), are widely used for system or damage identification problems in various fields. However, in civil engineering, it is rarely used to learn about unknown modeling aspects using experimental data, despite of many studies using simulated data. This paper uses the UKF to estimate parameters of a nonlinear finite element (FE) model of a reinforced concrete (RC) bridge column where bond-slip effect played a significant role. The bridge column was tested on a shake table at the University of California, San Diego, and was subjected to a suite of seismic input motions of varying intensities. Although perfect bonding is a common engineering assumption, the experimental observations indicated that the bond-slip effect should be taken into account due to high contribution to fixed-end rotation to column drifts. In this regard, a computationally efficient nonlinear FE model is developed in OpenSees using fiber-based beam-column elements with fixed-end rotation considered. The FE model is updated by minimizing the discrepancy between FE-predicted and measured response, leading to optimum estimate of the unknown model parameters (e.g., those for core concrete and bond-slip). The UKF is employed for nonlinear FE model updating after careful selection of critical model parameters based on a sensitivity study. Data from different tests with seismic excitations of various intensity levels are also considered. In sum, this study successfully applies the UKF to estimate unknown modeling aspects using experimental data and affirms that the bond slip effect in the tested bridge pier column has a significant impact in its dynamic response.
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    (33, 33) Pinto, Francisco; Torres, César; Birrell, Matias; Li, Yong; Fayaz, Jawad; Astroza, Rodrigo; 33
    This study proposes a probabilistic, uncertainty-informed framework for calibrating advanced soil constitutive models (SCMs), particularly, advanced critical state-based models, to accurately capture uncertainty in soil behavior in geotechnical applications. The proposed framework incorporates Polynomial Chaos Expansion (PCE) metamodels to optimize sensitivity analysis (SA) and enable Bayesian updating of SCM parameters, ensuring precise calibration that addresses inherent and epistemic uncertainties. Additionally, Random Forest (RF) analysis is employed to validate initial statistical assumptions and parameter trends during SA and enhance the robustness in the calibration process. Monotonic drained triaxial tests are used within this framework to calibrate the SANISAND model, an advanced critical state-based SCM for sand, with a focus on Nevada Sand soil due to its significance in geotechnical engineering. The framework estimates parameters’ joint probability density functions (PDFs) from experimental data, providing probabilistic insights into model responses under varying confining pressures and relative densities. By reducing computational demands and integrating uncertainty quantification, this approach offers an efficient and accurate calibration process, improving SCM predictive capability and reliability for use in finite element (FE) analyses. This study demonstrates the framework's application and validation to Nevada Sand and proposes PDFs with correlation coefficients for the SANISAND model, accelerating its integration in posterior stochastic geotechnical system-level modeling.
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    Calibration of a large nonlinear finite element model of a highway bridge with many uncertain parameters
    (Springer New York LLC) Astroza, Rodrigo; Barrientos, Nicolás; Li, Yong; Saavedra Flores, Erick