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Data-driven Arbitrary Polynomial Chaos Expansion on Uncertainty Quantification for Real-time Hybrid Simulation Under Stochastic Ground Motions
Experimental Techniques ( IF 1.6 ) Pub Date : 2020-06-03 , DOI: 10.1007/s40799-020-00381-w
M. Chen , T. Guo , C. Chen , W. Xu

Uncertainties in real-time hybrid simulation include structural parameters and ground motion. Uncertain parameters often do not follow common distribution types. Data-driven arbitrary polynomial chaos constructs optimal orthogonal polynomial basis based on the sample data without distribution assumption. In this study, the data-driven polynomial chaos is compared with other generalized polynomial chaos from the aspects of the rate of error convergence when applied for uncertainty quantification of real-time hybrid simulation. Moreover, uncertainties of ground motion are considered in the RTHS problem to represent the scenarios with more complex input variables. Different statistical indicators are utilized to evaluate the accuracy of the alternative model in comparison with the Monte Carlo simulation results. Compared with generalized polynomial chaos, the data-driven arbitrary polynomial chaos presents potential for uncertainty quantification of real-time hybrid simulation with approximate or better accuracy. Actuator delay in RTHS could change the sensitivity of model output to the random variables.

中文翻译:

随机地面运动下实时混合仿真不确定性量化的数据驱动任意多项式混沌扩展

实时混合仿真中的不确定性包括结构参数和地面运动。不确定的参数通常不遵循常见的分布类型。数据驱动的任意多项式混沌基于样本数据构造最优正交多项式基,无分布假设。本研究将数据驱动的多项式混沌应用于实时混合仿真的不确定性量化时,从误差收敛率方面与其他广义多项式混沌进行了比较。此外,在 RTHS 问题中考虑了地面运动的不确定性,以表示具有更复杂输入变量的场景。与蒙特卡罗模拟结果相比,使用不同的统计指标来评估替代模型的准确性。与广义多项式混沌相比,数据驱动的任意多项式混沌为实时混合仿真的不确定性量化提供了近似或更高精度的潜力。RTHS 中的执行器延迟可能会改变模型输出对随机变量的敏感性。
更新日期:2020-06-03
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