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A novel experimental data-driven exponential convex model for reliability assessment with uncertain-but-bounded parameters

机译：具有不确定但有界参数的可靠性评估的新型实验数据驱动指数凸模型

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摘要

The convex model is commonly applied to quantify the uncertain-but-bounded parameters. However, the typical interval and ellipsoid models may lead to inaccurate approximation for existing experimental data, which may incur either too risk or conservative for safety assessment. To this end, this study aims to create a novel data-driven exponential convex model to achieve accurate approximation for experiment data, in which the dimension reduction minimum volume method plays the key role. Furthermore, a novel relaxed exponential nominal value method (RENVM) is developed to evaluate the corresponding non-probabilistic reliability index robustly and efficiently, and the sensitivities are also derived based on the straight forward perturbation method to guarantee its efficiency. Through numerical and experimental studies, the accuracy and validity of the proposed data-driven exponential convex model are validated compared to the interval and ellipsoid models, and the robustness and efficiency of the proposed RENVM are also demonstrated for solving both linear and nonlinear problems. (C) 2019 Elsevier Inc. All rights reserved.
机译：凸模型通常用于量化不确定但有界的参数。但是，典型的区间模型和椭球模型可能会导致现有实验数据的近似值不正确，从而可能会导致风险太大或对安全性评估过于保守。为此，本研究旨在创建一种新颖的数据驱动的指数凸模型，以实现对实验数据的精确逼近，其中降维最小体积法起着关键作用。此外，开发了一种新颖的松弛指数标称值方法（RENVM）来鲁棒和有效地评估相应的非概率可靠性指标，并且还基于直接扰动方法推导了灵敏度，以确保其有效性。通过数值和实验研究，验证了所提出的数据驱动指数凸模型与区间模型和椭球模型的准确性和有效性，并且证明了所提出的RENVM解决线性和非线性问题的鲁棒性和效率。 （C）2019 Elsevier Inc.保留所有权利。

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