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Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However, uninterrupted utilization of plant and equipment is requisite to reaping the maximum benefit of mechanization; one particular problem being plant breakdown duration and its impact upon process productivity. Predicting the duration of plant downtime would enable plant managers to develop suitable contingency plans to reduce the impact of downtime. This paper presents a stochastic mathematical modelling methodology (more specifically, probability density function of random numbers) which predicts the probable magnitude of ‘the next’ breakdown, in terms of duration for tracked hydraulic excavators. A random sample of 33 machines was obtained from opencast mining contractors, containing 1070 observations of machine breakdown duration. Utilization of the random numbers technique will engender improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which aims to: extend the model's application to cover other industrial settings and plant items; to predict the time at which breakdown will occur (vis‐à‐vis the duration of breakdown); and apply the random numbers modelling to individual machine compartments.

This paper aims to provide a comprehensive review of uncertainty quantification methods supported by evidence-based comparison studies. Uncertainties are widely encountered in engineering practice, arising from such diverse sources as heterogeneity of materials, variability in measurement, lack of data and ambiguity in knowledge. Academia and industries have long been researching for uncertainty quantification (UQ) methods to quantitatively account for the effects of various input uncertainties on the system response. Despite the rich literature of relevant research, UQ is not an easy subject for novice researchers/practitioners, where many different methods and techniques coexist with inconsistent input/output requirements and analysis schemes.

This confusing status significantly hampers the research progress and practical application of UQ methods in engineering. In the context of engineering analysis, the research efforts of UQ are most focused in two largely separate research fields: structural reliability analysis (SRA) and stochastic finite element method (SFEM). This paper provides a state-of-the-art review of SRA and SFEM, covering both technology and application aspects. Moreover, unlike standard survey papers that focus primarily on description and explanation, a thorough and rigorous comparative study is performed to test all UQ methods reviewed in the paper on a common set of reprehensive examples.

Over 20 uncertainty quantification methods in the fields of structural reliability analysis and stochastic finite element methods are reviewed and rigorously tested on carefully designed numerical examples. They include FORM/SORM, importance sampling, subset simulation, response surface method, surrogate methods, polynomial chaos expansion, perturbation method, stochastic collocation method, etc. The review and comparison tests comment and conclude not only on accuracy and efficiency of each method but also their applicability in different types of uncertainty propagation problems.

The research fields of structural reliability analysis and stochastic finite element methods have largely been developed separately, although both tackle uncertainty quantification in engineering problems. For the first time, all major uncertainty quantification methods in both fields are reviewed and rigorously tested on a common set of examples. Critical opinions and concluding remarks are drawn from the rigorous comparative study, providing objective evidence-based information for further research and practical applications.

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.

The purpose of this paper is to probe the potentials of computational intelligence (CI) and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s (PHEVs) fuel cost and optimize its fuel consumption rate. Besides, the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.

The hybrid intelligent tool is implemented in two different levels. The hyper-level algorithm is a Pareto-based memetic algorithm, known as the chaos-enhanced Lamarckian immune algorithm (CLIA), with three different objective functions. As a hyper-level supervisor, CLIA tries to design a fast and accurate identifier which, at the same time, can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.

Based on the conducted numerical simulations, a set of interesting points are inferred. First, it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features. It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.

To the best knowledge of the authors, this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.

To provide an explicit representation for wide‐sense stationary stochastic fields which can be used in stochastic finite element modelling to describe random material properties.

This method represents wide‐sense stationary stochastic fields in terms of multiple Fourier series and a vector of mutually uncorrelated random variables, which are obtained by minimizing the mean‐squared error of a characteristic equation and solving a standard algebraic eigenvalue problem. The result can be treated as a semi‐analytic solution of the Karhunen‐Loève expansion.

According to the Karhunen‐Loève theorem, a second‐order stochastic field can be decomposed into a random part and a deterministic part. Owing to the harmonic essence of wide‐sense stationary stochastic fields, the decomposition can be effectively obtained with the assistance of multiple Fourier series.

The proposed explicit representation of wide‐sense stationary stochastic fields is accurate, efficient and independent of the real shape of the random structure in consideration. Therefore, it can be readily applied in a variety of stochastic finite element formulations to describe random material properties.

This paper discloses the connection between the spectral representation theory of wide‐sense stationary stochastic fields and the Karhunen‐Loève theorem of general second‐order stochastic fields, and obtains a Fourier‐Karhunen‐Loève representation for the former stochastic fields.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

A stochastic discrete Fredholm system in the form xn(ω) = hn(ω) + ∞Σj=1 Cn,j(ω)ƒj(xj(ω)), n = 1,2,…, is studied, where ω ∈ Ω, the supporting set of a complete probability measure space (Ω,A,P). A random solution of the discrete system is defined to be a discrete parameter second order stochastic process xn(ω) that satisfies the equation almost surely. The stochastic geometric stability of xn(ω) is defined, and conditions are given under which the random solution has this property. A finite system which approximates the infinite system is given, and it is shown that under certain conditions the unique random solution of the approximating system converges to the unique random solution of the infinite system.

The purpose of this paper is to implement the hardware structure for radial basis function (RBF) neural network based on stochastic logic computation.

The hardware implementation of artificial neural networks (ANNs) has a complicated structure and is normally space consuming due to huge size of digital multiplication, addition/subtraction, non‐linear activation function, etc. Also the unavailability of ANN hardware at an attractive price limits its use for real time applications. In stochastic logic theory, the real numbers are converted to random streams of bits instead of a binary number. The performance of the proposed structure is analyzed using very high speed integrated circuit hardware description language.

Stochastic theory‐based arithmetic and logic approach provides a way to carry out complex computation with very simple hardware and very flexible design of the system. The Gaussian RBF for hidden layer neuron is employed using stochastic counter that reduces the hardware resources significantly. The number of hidden layer neurons in RBF neural network structure is adaptively varied to make it an intelligent system.

The paper outlines the stochastic neural computation on digital hardware for implementing radial basis neural network. The structure has considered the optimized usage of hardware resources.

The purpose of this paper is to expose computational methods as applied to engineering systems and evolutionary processes with randomness in external actions and inherent parameters.

In total, two approaches are distinguished that rely on solvers from deterministic algorithms. Probabilistic analysis is referred to as the approximation of the response by a Taylor series expansion about the mean input. Alternatively, stochastic simulation implies random sampling of the input and statistical evaluation of the output.

Beyond the characterization of random response, methods of reliability assessment are discussed. Concepts of design improvement are presented. Optimization for robustness diminishes the sensitivity of the system to fluctuating parameters.

Deterministic algorithms available for the primary problem are utilized for stochastic analysis by statistical Monte Carlo sampling. The computational effort for the repeated solution of the primary problem depends on the variability of the system and is usually high. Alternatively, the analytic Taylor series expansion requires extension of the primary solver to the computation of derivatives of the response with respect to the random input. The method is restricted to the computation of output mean values and variances/covariances, with the effort determined by the amount of the random input. The results of the two methods are comparable within the domain of applicability.

The present account addresses the main issues related to the presence of randomness in engineering systems and processes. They comprise the analysis of stochastic systems, reliability, design improvement, optimization and robustness against randomness of the data. The analytical Taylor approach is contrasted to the statistical Monte Carlo sampling throughout. In both cases, algorithms known from the primary, deterministic problem are the starting point of stochastic treatment. The reader benefits from the comprehensive presentation of the matter in a concise manner.

The purpose of this study is to develop a stochastic finite element method (FEM) to solve the calculation precision deficiency caused by spatial variability of dam compaction quality.

The Choleski decomposition method was applied to generate constraint random field of porosity. Large-scale laboratory triaxial tests were conducted to determine the quantitative relationship between the dam compaction quality and Duncan–Chang constitutive model parameters. Based on this developed relationship, the constraint random fields of the mechanical parameters were generated. The stochastic FEM could be conducted.

When the fully random field was simulated without the restriction effect of experimental data on test pits, the spatial variabilities of both displacement and stress results were all overestimated; however, when the stochastic FEM was performed disregarding the correlation between mechanical parameters, the variabilities of vertical displacement and stress results were underestimated and variation pattern for horizontal displacement also changed. In addition, the method could produce results that are closer to the actual situation.

Although only concrete-faced rockfill dam was tested in the numerical examples, the proposed method is applicable for arbitrary types of rockfill dams.

The value of this study is that the proposed method allowed for the spatial variability of constitutive model parameters and that the applicability was confirmed by the actual project.