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Byeng D. Youn - One of the best experts on this subject based on the ideXlab platform.

  • Sequential optimization and uncertainty propagation method for efficient optimization-based Model Calibration
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Hyejeong Son, Byeng D. Youn
    Abstract:

    The goal of Model Calibration is to improve the predictive capability of a computational Model by estimating the unknown input variables of the Model. Optimization-based Model Calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based Model Calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a Calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate Model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based Model Calibration. Two engineering applications, Model Calibration of a shallow strip footing Model and an automotive steering wheel-column Model, are presented to demonstrate the effectiveness of the proposed method.

  • Review of statistical Model Calibration and validation—from the perspective of uncertainty structures
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Wongon Kim, Hyunseok Oh, Byeng D. Youn, Nam H. Kim
    Abstract:

    Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical Model Calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE Models. The objective of this paper is to review the state-of-the-art and trends in statistical Model Calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories—the forward problem, the inverse problem, and the validation problem—in the context of techniques and applications for statistical Model Calibration and validation.

  • Special issue: a comprehensive study on enhanced optimization-based Model Calibration using gradient information
    Structural and Multidisciplinary Optimization, 2018
    Co-Authors: Guilian Yi, Byeng D. Youn
    Abstract:

    Model Calibration is the process of estimating unknown inputs in a Model to improve the agreement between Model predictions and experimental observations. Optimization-based Model Calibration is a probabilistic approach for estimating unknown inputs by using optimization techniques. Gradient-based optimization algorithms are popular for optimization-based Model Calibration because of their computational efficiency. Gradient-based algorithms, however, also have drawbacks that include the local optimum issue, the numerical noise issue, lack of gradient information, and related concerns. In optimization-based Model Calibration, a Calibration metric that quantifies the similarity or difference between two probability distributions (the predicted and the observed system responses) is defined as an objective function. Current methods of optimization-based Model Calibration use existing Calibration metrics, such as the likelihood function and the probability residual. Occasionally, these methods show inaccurate calibrated results. Therefore, first, this comprehensive study investigates the root causes of the inaccurate calibrated results that arise from using existing Calibration metrics. Second, an enhanced method is proposed to achieve robust optimization-based Model Calibration by providing analytical gradient information. This study provides a general guideline for improved optimization-based Model Calibration.

Fatma Birinci - One of the best experts on this subject based on the ideXlab platform.

  • finite element Model Calibration effects on the earthquake response of masonry arch bridges
    Finite Elements in Analysis and Design, 2011
    Co-Authors: Baris Sevim, Alemdar Bayraktar, Ahmet Can Altunisik, Sezer Atamturktur, Fatma Birinci
    Abstract:

    The focus of this paper is to illustrate the importance of Model Calibration and in situ vibration testing by comparing the finite element Model predictions of the earthquake response of the two historical arch bridges, Osmanli and Senyuva, before and after Model Calibration. The three-dimensional finite element Models of these two arch bridges, built in the ANSYS finite element program, are used to predict bridge dynamic characteristics, such as natural frequencies and mode shapes. Following the analytical study, ambient vibration tests were conducted to experimentally obtain dynamic characteristics of these two bridges. During ambient vibration tests, accelerometers were placed at several points on the bridge to collect the vibration response due to natural and operational excitation sources. Enhanced frequency domain decomposition and stochastic subspace identification techniques were used to extract the experimental natural frequencies, mode shapes and damping ratios. Finite element Models of the two arch bridges were adjusted such that the Model predictions reproduce the ambient vibration test results with increased fidelity. The behavior of the masonry arch bridges under earthquake excitation recorded during the Erzincan Earthquake in 1992 is simulated by both the initial and adjusted finite element Models. The findings of this study emphasize the importance of Model Calibration and ambient vibration testing.

Baris Sevim - One of the best experts on this subject based on the ideXlab platform.

  • finite element Model Calibration effects on the earthquake response of masonry arch bridges
    Finite Elements in Analysis and Design, 2011
    Co-Authors: Baris Sevim, Alemdar Bayraktar, Ahmet Can Altunisik, Sezer Atamturktur, Fatma Birinci
    Abstract:

    The focus of this paper is to illustrate the importance of Model Calibration and in situ vibration testing by comparing the finite element Model predictions of the earthquake response of the two historical arch bridges, Osmanli and Senyuva, before and after Model Calibration. The three-dimensional finite element Models of these two arch bridges, built in the ANSYS finite element program, are used to predict bridge dynamic characteristics, such as natural frequencies and mode shapes. Following the analytical study, ambient vibration tests were conducted to experimentally obtain dynamic characteristics of these two bridges. During ambient vibration tests, accelerometers were placed at several points on the bridge to collect the vibration response due to natural and operational excitation sources. Enhanced frequency domain decomposition and stochastic subspace identification techniques were used to extract the experimental natural frequencies, mode shapes and damping ratios. Finite element Models of the two arch bridges were adjusted such that the Model predictions reproduce the ambient vibration test results with increased fidelity. The behavior of the masonry arch bridges under earthquake excitation recorded during the Erzincan Earthquake in 1992 is simulated by both the initial and adjusted finite element Models. The findings of this study emphasize the importance of Model Calibration and ambient vibration testing.

Guesuk Lee - One of the best experts on this subject based on the ideXlab platform.

  • Sequential optimization and uncertainty propagation method for efficient optimization-based Model Calibration
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Hyejeong Son, Byeng D. Youn
    Abstract:

    The goal of Model Calibration is to improve the predictive capability of a computational Model by estimating the unknown input variables of the Model. Optimization-based Model Calibration (OBMC) is a probabilistic way to estimate the unknown input variables through the use of optimization techniques. Performing optimization in a probabilistic sense requires a high computational cost to obtain statistics about the outputs at every iteration of the optimization. To improve optimization efficiency, this paper proposes a sequential optimization-based Model Calibration approach that makes use of first an efficient, and then a highly accurate probabilistic assessment method, in sequence. At the earlier stage of the sequential optimizations, approximate integration methods are used to accelerate the probabilistic assessment process. As a Calibration metric, the moment matching metric is devised to use the obtained statistics of the outputs. When the optimization reaches near-convergence, a more accurate method, such as a sampling method with an accurate surrogate Model, is substituted for the probabilistic assessment. Thus, this paper provides an efficient and accurate procedure for optimization-based Model Calibration. Two engineering applications, Model Calibration of a shallow strip footing Model and an automotive steering wheel-column Model, are presented to demonstrate the effectiveness of the proposed method.

  • Review of statistical Model Calibration and validation—from the perspective of uncertainty structures
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Guesuk Lee, Wongon Kim, Hyunseok Oh, Byeng D. Youn, Nam H. Kim
    Abstract:

    Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical Model Calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE Models. The objective of this paper is to review the state-of-the-art and trends in statistical Model Calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories—the forward problem, the inverse problem, and the validation problem—in the context of techniques and applications for statistical Model Calibration and validation.

Yang Dacheng - One of the best experts on this subject based on the ideXlab platform.