Quantification of model uncertainty and variability in Newmark displacement analysis
Newmark displacement model has been extensively used to evaluate earthquake-induced displacement in earth systems. In this paper, model uncertainty and variability associated with the Newmark displacement analysis are systematically studied. Fourteen Newmark displacement models using scalar or vecto...
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sg-ntu-dr.10356-1424882020-06-23T01:28:12Z Quantification of model uncertainty and variability in Newmark displacement analysis Du, Wenqi Huang, Duruo Wang, Gang Institute of Catastrophe Risk Management (ICRM) Science::Geology Newmark Displacement Model Uncertainty Newmark displacement model has been extensively used to evaluate earthquake-induced displacement in earth systems. In this paper, model uncertainty and variability associated with the Newmark displacement analysis are systematically studied. Fourteen Newmark displacement models using scalar or vector intensity measures (IMs) as predictors are compared in this study. In general, model uncertainty for the vector-IM models is found smaller than that of the scalar-IM models, and remains consistent over different earthquake magnitude, distance and site conditions. Yet, the model uncertainty of these Newmark displacement models is still much larger than that of the ground-motion prediction equations (GMPEs) for IMs, indicating further development of the models is much needed. Considering the variabilities contributed from both GMPEs and Newmark displacement models, the total variability of the predicted Newmark displacements is rather consistent among the scalar- and vector-IM displacement models, due to extra sources of variability introduced by incorporating additional IMs. Finally, a logic tree scheme is implemented in the fully probabilistic Newmark displacement analysis to account for the model uncertainty and variability. Sensitivity analysis shows that specific weights would not significantly influence the displacement hazard curves as the results may be dominated by outlier models. Instead, selecting appropriate GMPEs and Newmark displacement models is more important in using the logic-tree framework. 2020-06-23T01:28:12Z 2020-06-23T01:28:12Z 2018 Journal Article Du, W., Huang, D., & Wang, G. (2018). Quantification of model uncertainty and variability in Newmark displacement analysis. Soil Dynamics and Earthquake Engineering, 109, 286-298. doi:10.1016/j.soildyn.2018.02.037 0267-7261 https://hdl.handle.net/10356/142488 10.1016/j.soildyn.2018.02.037 2-s2.0-85044585187 109 286 298 en Soil Dynamics and Earthquake Engineering © 2018 Elsevier Ltd. All rights reserved. |
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Science::Geology Newmark Displacement Model Uncertainty Du, Wenqi Huang, Duruo Wang, Gang Quantification of model uncertainty and variability in Newmark displacement analysis |
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Newmark displacement model has been extensively used to evaluate earthquake-induced displacement in earth systems. In this paper, model uncertainty and variability associated with the Newmark displacement analysis are systematically studied. Fourteen Newmark displacement models using scalar or vector intensity measures (IMs) as predictors are compared in this study. In general, model uncertainty for the vector-IM models is found smaller than that of the scalar-IM models, and remains consistent over different earthquake magnitude, distance and site conditions. Yet, the model uncertainty of these Newmark displacement models is still much larger than that of the ground-motion prediction equations (GMPEs) for IMs, indicating further development of the models is much needed. Considering the variabilities contributed from both GMPEs and Newmark displacement models, the total variability of the predicted Newmark displacements is rather consistent among the scalar- and vector-IM displacement models, due to extra sources of variability introduced by incorporating additional IMs. Finally, a logic tree scheme is implemented in the fully probabilistic Newmark displacement analysis to account for the model uncertainty and variability. Sensitivity analysis shows that specific weights would not significantly influence the displacement hazard curves as the results may be dominated by outlier models. Instead, selecting appropriate GMPEs and Newmark displacement models is more important in using the logic-tree framework. |
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Institute of Catastrophe Risk Management (ICRM) |
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Institute of Catastrophe Risk Management (ICRM) Du, Wenqi Huang, Duruo Wang, Gang |
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Article |
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Du, Wenqi Huang, Duruo Wang, Gang |
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Du, Wenqi |
title |
Quantification of model uncertainty and variability in Newmark displacement analysis |
title_short |
Quantification of model uncertainty and variability in Newmark displacement analysis |
title_full |
Quantification of model uncertainty and variability in Newmark displacement analysis |
title_fullStr |
Quantification of model uncertainty and variability in Newmark displacement analysis |
title_full_unstemmed |
Quantification of model uncertainty and variability in Newmark displacement analysis |
title_sort |
quantification of model uncertainty and variability in newmark displacement analysis |
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2020 |
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https://hdl.handle.net/10356/142488 |
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1681059807485755392 |