Search

Summary: The robust design of a tuned mass damper (TMD) with hybrid aleatory and epistemic uncertainties is proposed in this study. In this method, the aleatory uncertainty involved in the external excitation is represented with the white noise in stochastic theory. The epistemic uncertainties derived from fragmentary statistical data and incomplete preknowledge of structural model and site condition are fully captured with the discrete multi‐intervals in evidence theory. In order to overcome the computational bottleneck related to the uncertainty propagation of epistemic uncertainties, a parallel‐efficient global optimization (parallel‐EGO) method is proposed to approximate the bounds of structural response for joint focal elements. Then, a robustness objective function, with the aim to minimize the worst system response of the primary structure, is presented to search the optimal parameters of TMD. Finally, case studies for a single‐degree‐of‐freedom (SDOF) system and a multi‐degree‐freedom (MDOF) system validate that the designed TMD not only significantly reduces the worst seismic responses but also improves the robustness of the primary structure. [ABSTRACT FROM AUTHOR]

Abstract Evidence theory, with its powerful features for uncertainty analysis, provides an alternative to probability theory for representing epistemic uncertainty, which is an uncertainty in a system caused by the impreciseness of data or knowledge that can be conveniently addressed. However, this theory is time-consuming for most applications because of its discrete property. This article describes an adaptive sub-interval perturbation-based computational strategy for representing epistemic uncertainty in structural dynamic analysis with evidence theory. The possibility of adopting evidence theory as a general tool for uncertainty quantification in structural transient response under stochastic excitation is investigated using an algorithm that can alleviate computational difficulties. Simulation results indicate that the effectiveness of the presented strategy can be used to propagate uncertainty representations based on evidence theory in structural dynamics. [ABSTRACT FROM AUTHOR]

Uncertainties originate from physical variability, data uncertainty, and modeling errors in the fatigue crack growth prediction analysis. This study presents an evidential uncertainty quantification (UQ) approach for determining uncertainties involved in revealing the material constants of the metal fatigue crack growth model with imprecise uncertainty information (i.e., epistemic uncertainty). The parameters in fatigue crack growth model are obtained by fitting the available sparse experimental data, and then the uncertainties in these parameters are considered. To alleviate the computational difficulties in the UQ analysis based on evidence theory, an interval optimization method based on differential evolution is used in finding the propagated belief structure. The overall procedure is demonstrated using the results of several replicated experiments on aluminum alloy CCT specimens. [ABSTRACT FROM AUTHOR]

The small first passage probability of nonlinear dynamic structures under nonstationary seismic excitation implies a prohibitive computational demand because of the epistemic uncertainties rooted in structure system. In this work, a hybrid Kriging-high dimensional model representation (Kriging-HDMR) methodology for drastically simplifying this task is proposed. The epistemic uncertainties with well-defined bounds but no concrete distribution forms are addressed by interval model. Therefore, the first passage probability of the generic response process that the epistemic uncertainties involved, is represented by the conditional univariate extreme value distribution (EVD) of structural parameters with interval form. Then, a Kriging assisted HDMR together with third moment saddle point approximation (TMSA) is proposed to alleviate the computational burden and provides an accurate depiction of possible model outcome. The differential evolution (DE) interval optimization strategy is performed to accelerate the post process of searching the lower bound (LB) and upper bound (UB) of first passage probability with interval form. Finally, a nine-story shear frame with Bouc–Wen model is presented to demonstrate the accuracy and efficiency of proposed method. [ABSTRACT FROM AUTHOR]