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Journal ArticleDOI

Probability, Reliability and Statistical Methods in Engineering Design

P.E. James T. P. Yao
- 01 Jan 2001 - 
- Vol. 127, Iss: 1, pp 101-101
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This article is published in Journal of Structural Engineering-asce.The article was published on 2001-01-01. It has received 451 citations till now. The article focuses on the topics: Probabilistic design & Reliability (statistics).

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Citations
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Proceedings ArticleDOI

A data-driven framework for uncertainty quantification of a fluidized bed

TL;DR: A C++ wrapper is developed to integrate an uncertainty quantification toolkit developed at Sandia National Laboratory with MFiX and is integrated with Dakota via the wrapper to obtain low order statistics of the bed height and pressure drop across the bed.

Optimisation robuste des paramètres d'une machine à commande numérique pour l'usinage d'un acier à haute résistance

TL;DR: In this article, a modele reduit de la surface d'etat limite a l'aide de l'Approximation Diffuse is constructed, basee sur un pattern de points evolutif, progressivement enrichi a travers le processus d’optimisation, permet d'optimiser la robustesse du procede dusinage a commande numerique.

Probabilistic Sensitivity Analysis for Launch Vehicles with Varying Payloads and Adapters for Structural Dynamics and Loads

TL;DR: This study addresses how Probabilistic Sensitivity Analysis methods may be used to establish limits on payload mass and cg location and requirements on adaptor stiffnesses while maintaining vehicle loads and frequencies within established bounds.
Proceedings ArticleDOI

Workspace characterization of a robotic system using reliability-based design optimization

TL;DR: The dynamic capability equations (DCE) allow designers to predict the dynamic performance of a robotic system for a particular configuration and reference point on the end-effector (i.e.,point design) in order to obtain designs that can meet dynamic performance requirements.
Proceedings ArticleDOI

Mechanistic Modeling of Strength Distribution of Quasibrittle Structures and Its Implication for Structural Reliability

TL;DR: In this paper, a finite weakest link model of quasibrittle structures, which fail under controlled load at macro-crack initiation from one representative volume element (RVE), is derived from the transition state theory and a hierarchical multi-scale transition model.
References
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Journal ArticleDOI

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

TL;DR: Practical guidelines for verification and validation of NMS models and simulations are established that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies.
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Adaptive Designs of Experiments for Accurate Approximation of a Target Region

TL;DR: An iterative strategy to build designs of experiments is proposed, which is based on an explicit trade-off between reduction of global uncertainty and exploration of the regions of interest, which shows that a substantial reduction of error can be achieved in the crucial regions.
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Adaptive explicit decision functions for probabilistic design and optimization using support vector machines

TL;DR: This article presents a methodology to generate explicit decision functions using support vector machines (SVM) and proposes an adaptive sampling scheme that updates the decision function.
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Validation of reliability computational models using Bayes networks

TL;DR: The methodology includes uncertainty in the experimental measurement, and the posterior and prior distributions of the model output are used to compute a validation metric based on Bayesian hypothesis testing.
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Adaptive probability analysis using an enhanced hybrid mean value method

TL;DR: In this article, an adaptive probability analysis method is proposed to generate the probability distribution of the output performance function by identifying the propagation of input uncertainty to output uncertainty, which is based on an enhanced hybrid mean value (HMV+) analysis in the performance measure approach.