Probability, Reliability and Statistical Methods in Engineering Design

By James T. P. Yao, P.E.. 2001

795 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/deb2876b808cae1595ec78c4bdff2db37b1c364a

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In Statistics and Probability

Citations in the corpus

System reliability-based design optimization using the MPP-based dimension reduction method (2010, positive): “As an analytic method, which is the MPP-based method, the first-order reliability method (FORM; Haldar and Mahadevan 2000; Madsen et al. 1986; Hasofer and Lind 1974; Ditlevsen and Madsen 1996) has been very popular because of its simplicity of calculation.”
Measures of agreement between computation and experiment: Validation metrics (2004, background): “Then, the Taylor series for uncorrelated input random variables can be expanded about the mean of each of the input variables, l v i , and written as [55] where E ð Y m Þ is the expected value, i.e., the mean, of the SRQ and Var( v i ) is the variance of the input variables.”
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Reference Manual (2001, methodology): “The Mean Value method (MV, also known as MVFOSM in [Haldar and Mahadevan, 2000]) is the simplest, least-expensive method in that it estimates the response means, response standard deviations, and all CDF/CCDF forward/inverse mappings from a single evaluation of response functions and gradients at th”
Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty (2016, methodology): “For more details regarding the implementation of FORM, the reader is referred to textbooks by Ditlevsen (1979); Haldar and Mahadevan (2000), etc.”
Stochastic finite element analysis of the free vibration of laminated composite plates (2007, neutral): “A detailed account of both methods can be found in [17] and will be summarized here.”
Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities (2017, background): “A natural way ofmodeling the aleatory uncertainty is to use random variables for time-independent quantities and stochastic processes for time-varying quantities based on the classical frequency statistical theories [91].”
Unified nano-mechanics based probabilistic theory of quasibrittle and brittle structures: I. Strength, static crack growth, lifetime and scaling (2011, background): “…the extreme value distribution of Gaussian variables, which is the Gumbel distribution (Fisher and Tippett, 1928; Ang and Tang, 1984; Gumbel, 1958; Haldar and Mahadevan, 2000; Bazant, 2000; van der Hofstad and Redig, 2006), representing the so-called intermediate asymptotic regime in the sense of…”
Efficient reliability method for implicit limit state surface with correlated non‐Gaussian variables (2015, methodology): “In fact, the implementation of polynomial RSM-based reliability analysis is quite involved when the LSS is highly non-linear and when the number of basic random variables is very large [15]; in such circumstances, advanced RSM’s such as the stochastic RSM, support vector machine RSM and neural netwo”
Identification of marginal and joint CDFs using Bayesian method for RBDO (2009, methodology): “Dn is a critical value obtained from a standard mathematical table presented by Haldar and Mahadevan (2000). The maximum difference between FX(xi) and Gn(xi) of the ith sample xi for i = 1, .”
Activation energy based extreme value statistics and size effect in brittle and quasibrittle fracture (2007, background): “Based on integrating the product of load and strength distributions (Freudenthal et al., 1966; Haldar and Mahadevan, 2000), joint probability computations show that the probability cut-off up to which the pdf tail must be known need not be as small as 10 À6 –10 À7 , but must still be only about one…”
Reliability-based design optimization with confidence level under input model uncertainty due to limited test data (2011, background): “In that case, the obtained βt -contour shape will be very different from a true βt -contour, which will lead to an inaccurate optimum design.”
Uncertainty Quantification in the Mechanical Response of Crystal Plasticity Simulations (2019, methodology): “The first-order, second-moment (FOSM) method is a computationally efficient tool to estimate the variability, accurate up to the first-order, in the output of a mathematical model and hence chosen for the present study.(24) However, the FOSM method requires prior knowledge of the variability (mean, ”