An efficient formulation for limit state function gradient calculation

Guan, X.L.; Melchers, Robert E.

doi:10.1016/0045-7949(94)90380-8

Cited by 8 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very common alternative is to use finite differences, but frequently some troubles arise when this method is used. Some works implementing the limit state functions derivatives at the finite element formulation level have been presented and good results were obtained (Guan and Melchers, 1994;Frangopol et al, 1996), although using a very complex procedure. On the other hand, techniques employing simulation procedures, such as Direct Monte Carlo Method and Monte Carlo with Importance Sampling, have a prohibitive computational cost in large structural systems even if the structural evaluation is accelerated by techniques such as Neumann Series Expansion, described in Papadopoulos and Papadrakakis (1998).…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of concrete structures with neural networks and response surfaces

Gomes

Awruch

2005

Engineering Computations

View full text Add to dashboard Cite

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -To research the feasibility in using artificial neural networks (ANN) and response surfaces (RS) techniques for reliability analysis of concrete structures. Design/methodology/approach -The evaluation of the failure probability and safety levels of structural systems is of extreme importance in structural design, mainly when the variables are eminently random. It is necessary to quantify and compare the importance of each one of these variables in the structural safety. RS and the ANN techniques have emerged attempting to solve complex and more elaborated problems. In this work, these two techniques are presented, and comparisons are carried out using the well-known first-order reliability method (FORM), with non-linear limit state functions. The reliability analysis of reinforced concrete structure problems is specially considered taking into account the spatial variability of the material properties using random fields and the inherent non-linearity. Findings -It was observed that direct Monte Carlo simulation technique has a low performance in complex problems. FORM, RS and neural networks techniques are suitable alternatives, despite the loss of accuracy due to approximations characterizing these methods. Research limitations/implications -The examples tested are limited to moderated large non-linear reinforced concrete finite element models. Conclusions are drawn based on the examples.Practical implications -Some remarks are outlined regarding the fact that RS and ANN techniques have presented equivalent precision levels. It is observed that in problems where the computational cost of structural evaluations (computing failure probability and safety levels) is high, these two techniques could improve the performance of the structural reliability analysis through simulation techniques. Originality/value -This paper is important in the field of reliability analysis of concrete structures specially when neural networks or RS techniques are used.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability analysis of concrete structures with neural networks and response surfaces

Gomes

Awruch

2005

Engineering Computations

View full text Add to dashboard Cite

show abstract

“…Probabilistic finite element (PFE) analysis [37,38] has been used in structure engineering to estimate the effects of input parameter uncertainty. Several analytical methods for sensitivity calculation in PFE were reviewed and compared by Guan and Melchers [39]. However, those methods usually are either not efficient or difficult to implement in probabilistic design.…”

Section: Introductionmentioning

confidence: 99%

Application of the Sequential Optimization and Reliability Assessment Method to Structural Design Problems

Liu

Chen

Sheng

et al. 2003

Volume 2: 29th Design Automation Conference, Parts a and B

View full text Add to dashboard Cite

The use of probabilistic optimization in structural design applications is hindered by the huge computational cost associated with evaluating probabilistic characteristics, where the computationally expensive finite element method (FEM) is often used for simulating design performance. In this paper, a Sequential Optimization and Reliability Assessment (SORA) method with analytical derivatives is applied to improve the efficiency of probabilistic structural optimization. With the SORA method, a single loop strategy that decouples the optimization and the reliability assessment is used to significantly reduce the computational demand of probabilistic optimization. Analytical sensitivities of displacement and stress functionals derived from finite element formulations are incorporated into the probability analysis without recurring excessive cost. The benefits of our proposed methods are demonstrated through two truss design problems by comparing the results with using conventional approaches. Results show that the SORA method with analytical derivatives is the most efficient with satisfactory accuracy.

show abstract

“…A plan strain analysis is carried out. By assuming that c and f homogenous lognormal random field, @D p @X can be evaluated using the methods proposed by Guan and Melchers (1994) and Ning and Jiashou (2002).…”

mentioning

confidence: 99%

A probabilistic non-linear finite element analysis for slope stability problem

Farah

Ltifi

Hassis

2015

The IES Journal Part A: Civil & Structural Engineering

View full text Add to dashboard Cite

This paper deals with the slope stability problem by a finite element reliability analysis considering the spatial variation of soil strength parameters. In this work, the spatial variation is described using random field theory. To simplify the implementation of the proposed procedure for slope reliability analysis, elastic perfectly plastic constitutive model describing the soil behaviour using the Mohr coulomb yield criterion is adopted. The application of the proposed approach for the slope reliability analysis is performed using a performance function expressed in terms of stress fields mobilised along a circular failure surface. For this purpose, a computation of the stress gradient based on the incremental theory of plasticity is used. The results of the proposed procedure are checked by a probabilistic method based on the combination of the Bishop's model and the Monte Carlo simulation. The numerical examples have elucidated the efficiency and the validity of the present procedure to slope reliability analysis. Finally, the proposed procedure is applied for locating critical probabilistic circular sliding surface considering the spatial variation of the shear strength parameters.

show abstract

An efficient formulation for limit state function gradient calculation

Cited by 8 publications

References 10 publications

Reliability analysis of concrete structures with neural networks and response surfaces

Reliability analysis of concrete structures with neural networks and response surfaces

Application of the Sequential Optimization and Reliability Assessment Method to Structural Design Problems

A probabilistic non-linear finite element analysis for slope stability problem

Contact Info

Product

Resources

About