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Corrosion is one of the common damage mechanisms in many engineering structures such as marine structures, petroleum pipelines, aerospace and nuclear reactor. However, the service performance of metal materials and structures is gradually degenerating with the increase of service life due to the rapid growth of corrosion damages. Thus, the coupled effects for corrosion damage in reliability analysis should be considered urgently. Then, the purpose of this paper is to develop the corrosion damage physical model and the corresponding reliability analysis methods, which consider the coupled effect of corrosion damage.

A failure physical model, considering the coupled effect of pitting growth, crack and crack propagation, is presented in this paper. Sequentially, the corrosion reliability with respect to pitting physical damage can be investigated. The presented pitting damage physical model is formulated as time-variant performance limit state functions, which include the crack transition, crack growth and fracture failure mechanics. The first-passage failure criterion is used to construct the corrosion reliability framework, involving in the pitting damage model with the increase of service life.

Results demonstrate that the multiplicative dimensional reduction (MDR) method behaves much better than FORM no matter in accuracy or efficiency. The proposed corrosion reliability method is applicable for dealing with the damage failure model of the structural pitting corrosion.

The MDR method is used to calculate the corrosion reliability index of a given structure with fewer function calls. Finally, an aeronautical metal material is used to demonstrate the efficiency and precision of the proposed corrosion reliability method when the failure physical model considering the coupled effects of mechanical stresses and corrosion environment is adopted.

To develop a new computational tool for predicting failure probability of structural/mechanical systems subject to random loads, material properties, and geometry.

High dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for capturing the high‐dimensional relationships between sets of input and output model variables. It is a very efficient formulation of the system response, if higher order variable correlations are weak and if the response function is dominantly of additive nature, allowing the physical model to be captured by the first few lower order terms. But, if multiplicative nature of the response function is dominant then all right hand side components of HDMR must be used to be able to obtain the best result. However, if HDMR requires all components, which means 2^N number of components, to get a desired accuracy, making the method very expensive in practice, then factorized HDMR (FHDMR) can be used. The component functions of FHDMR are determined by using the component functions of HDMR. This paper presents the formulation of FHDMR approximation of a multivariate limit state/performance function, which is dominantly of multiplicative nature. Given that conventional methods for reliability analysis are very computationally demanding, when applied in conjunction with complex finite element models. This study aims to assess how accurately and efficiently HDMR/FHDMR based approximation techniques can capture complex model output uncertainty. As a part of this effort, the efficacy of HDMR, which is recently applied to reliability analysis, is also demonstrated. Response surface is constructed using moving least squares interpolation formula by including constant, first‐order and second‐order terms of HDMR and FHDMR. Once the response surface form is defined, the failure probability can be obtained by statistical simulation.

Results of five numerical examples involving structural/solid‐mechanics/geo‐technical engineering problems indicate that the failure probability obtained using FHDMR approximation for the limit state/performance function of dominantly multiplicative in nature, provides significant accuracy when compared with the conventional Monte Carlo method, while requiring fewer original model simulations.

This is the first time where application of FHDMR concepts is explored in the field of reliability and system safety. Present computational approach is valuable to the practical modeling and design community, where user often suffers from the curse of dimensionality.

Research in marketing indicates that consumers may be sensitive to the final digits of prices. For example, despite being substantively equivalent, a price such as $199 may create more favorable price perceptions than $200. However, existing research has primarily focused on the effects of price endings in the context of uni‐dimensional prices – prices consisting of a single number. Advertised prices in the marketplace are often multi‐dimensional, consisting of numerous price dimensions. In such pricing contexts, price endings may influence consumers’ ability to conduct the arithmetic required to compute the total advertised price. Examines the effect of various price ending strategies on consumers’ computational efforts. The findings indicate that the more commonly exercised price ending strategies tend to result in prices that are the most difficult for consumers to evaluate.

This paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems.

The approach used in this paper is based on artificial neural network architectures that incorporate linear and nonlinear principal component analysis, combined with generalized dimensional analysis.

Neural network principal component analysis coupled with generalized dimensional analysis reduces input variable space by about 90 percent in the modeling of oil reservoirs. Once trained, the computation time is negligible and orders of magnitude faster than any traditional discretisation schemes such as fine‐mesh finite difference.

Finding the minimum number of input independent variables needed to characterize a system helps in extracting general rules about its behavior, and allows for quick setting of design guidelines, and particularly when evaluating changes in the physical properties of systems.

The methodology can be used to simulate dynamical systems characterized by differential equations, in an interactive CAD and optimization providing faster on‐line solutions and speeding up design guidelines.

The objective of this work is to develop an element technology to recover the plane stress response without any plane stress specific modifications in the large strain regime. Therefore, the essential feature of the proposed element formulation is an interface to arbitrary three‐dimensional constitutive laws. The easily implemented and computational cheap four‐noded element is characterized by coarse mesh accuracy and the satisfaction of the plane stress constraint in a weak sense. A number of example problems involving arbitrary small and large strain constitutive models demonstrate the excellent performance of the concept pursued in this work.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

Methods and techniques of aggregating preferences or priorities in the analytic hierarchy process (AHP) usually ignore variation or dispersion among experts and are vulnerable to extreme values (generated by particular viewpoints or experts trying to distort the final ranking). The purpose of this paper is to propose a modelling approach and a graphical representation to characterize inconsistency and disagreement in the group decision making in the AHP.

The authors apply a regression approach for estimating the decision weights of the AHP using linear mixed models (LMM). They also test the linear mixed model and the multi‐dimensional scaling graphical display using a case of strategic performance management in education.

In addition to determining the weight vectors, this model also allows the authors to decompose the variation or uncertainty in experts' judgment. Well‐known statistical theories can estimate and rigorously test disagreement among experts, the residual uncertainty due to rounding errors in AHP scale, and the inconsistency within individual experts' judgments. Other than characterizing different sources of uncertainty, this model allows the authors to rigorously test other factors that might significantly affect weight assessments.

This study provides a model to better characterize different sources of uncertainty. This approach can improve decision quality by allowing analysts to view the aggregated judgments in a proper context and pinpoint the uncertain component that significantly affects decisions.

The paper starts with a review of constitutive equations for rubber‐like materials, formulated in the invariants of the right Cauchy—Green deformation tensor. A general framework for the derivation of the stress tensor and the tangent moduli for invariant‐based models, for both the reference and the current configuration, is presented. The free energy of incompressible rubber‐like materials is extended to a compressible formulation by adding the volumetric part of the free energy. In order to overcome numerical problems encountered with displacement‐based finite element formulations for nearly incompressible materials, three‐dimensional finite elements, based on a penalty‐type formulation, are proposed. They are characterized by applying reduced integration to the volumetric parts of the tangent stiffness matrix and the pressure‐related parts of the internal force vector only. Moreover, hybrid finite elements are proposed. They are based on a three‐field variational principle, characterized by treating the displacements, the dilatation and the hydrostatic pressure as independent variables. Subsequently, this formulation is reduced to a generalized displacement formulation. In the numerical study these formulations are evaluated. The results obtained are compared with numerical results available in the literature. In addition, the proposed formulations are applied to 3D finite element analysis of an automobile tyre. The computed results are compared with experimental data.

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Considering the vector Helmholtz equation in three dimensions, this paper aims to present a novel approach for coupling the finite element method and a boundary integral formulation. It is demonstrated that the method is well-suited for many realistic three-dimensional problems in high-frequency engineering.

The formulation is based on partial solutions fulfilling the global boundary conditions and the iterative interaction between them. In comparison to other coupling formulation, this approach avoids the typical singularity in the integral kernels. The approach applies ideas from domain decomposition techniques and is implemented for a parallel calculation.

Using confirming elements for the trace space and default techniques to realize the infinite domain, no additional loss in accuracy is introduced compared to a monolithic finite element method approach. Furthermore, the degree of coupling between the finite element method and the integral formulation is reduced. The accuracy and convergence rate are demonstrated on a three-dimensional antenna model.

This approach introduces additional degrees of freedom compared to the classical coupling approach. The benefit is a noticeable reduction in the number of iterations when the arising linear equation systems are solved separately.

This paper focuses on multiple heterogeneous objects surrounded by a homogeneous medium. Hence, the method is suited for a wide range of applications.

The novelty of the paper is the proposed formulation for the coupling of both methods.