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Failure assessment diagrams are an integral component of asset integrity management in a variety of industrial sectors. They allow for the assessment of the significance of cracks in structures, between the domains of brittle fracture and plastic collapse. Numerical modelling, or empirically determined assessment lines across this continuum, are the basis of the guidance in current industrial standards. However, as the requirement of such assessments progresses from demonstrating safety to optimising resilience and resource allocation, it will become necessary to compute more informative (probabilistic) estimates, which are compatible with decision analysis. In this paper, Bayesian regression models are proposed as a suitable method of quantifying (aleatory and epistemic) uncertainty in the limit state on a failure assessment diagram. The data considered in this study consists of laboratory tests completed on wide plate fracture specimens. This work is intended to address the inconsistencies in current editions of industrial standards, and limitations (regarding flexibility and application) of existing scientific literature on the topic. Potential applications are discussed, including the use of fracture mechanics in meaningful reliability analysis, quantitative risk management, and optimising experimental design for future material tests. • A probabilistic failure assessment diagram is compared to methods used in industry. • The proposed gaussian process model is flexible, non-linear and probabilistic. • It can therefore approximate the behaviour between brittle and plastic domains. • Applications of reliability analysis and experimental design are demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis–Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix. [Copyright &y& Elsevier]

Several exciting new technologies have been developed in the Biomedical and Life Sciences producing genome and proteome wide multidimensional datasets (see Glossary). However, discovery of the relevant information from this multidimensional data is still a very challenging task. This review examines the main analytic methodologies available to the practitioner. It considers their strengths and weaknesses from a practical standpoint and gives an application example for the classification of human health condition. [Copyright &y& Elsevier]

This paper considers the application of a recently proposed L2 optimal non-parametric reduced set density estimator to novelty detection and binary classification and provides empirical comparisons with other forms of density estimation as well as support vector machines. [Copyright &y& Elsevier]

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution. [Copyright &y& Elsevier]

This paper is concerned with learning dense low-dimensional representations of high-dimensional positive data. The positive data may be continuous, discrete binary or count based. In addition to the low-dimensional data model, a topographic ordering of the representation is desired. The primary motivation for this work is the requirement for a low-dimensional interpretation of sparse vector space models of text documents which may take the form of binary, count based or real multivariate data. The generative topographic mapping (GTM) was developed and introduced as a principled alternative to the self-organising map for, principally, visualising high-dimensional continuous data. The GTM is one method by which a topographically organised low-dimensional data representation may be realised. There are many cases where the observation data is discrete and the application of methods developed specifically for continuous data is inappropriate. Based on the continuous GTM data model a non-linear latent variable model for modelling high-dimensional binary data is presented. The non-negative factorisation of a positive matrix which ensures a topographic ordering of the constituent factors is also presented as a principled yet non-probabilistic alternative to the GTM model. Experimental demonstrations of both methods are provided based on representing binary coded handwritten digits and the topographic organisation and visualisation of a collection of text based documents. [Copyright &y& Elsevier]

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability. • A dynamic Gaussian process regression (GPR) model is proposed for probabilistic prediction of pavement conditions. • A moving window strategy is developed to effectively alleviate the computational cost. • A genetic algorithm is adopted to determine the optimal window size. • The traditional GPR as well as dynamic and static Bayesian linear regression models are employed for comparison. [ABSTRACT FROM AUTHOR]

The increased use of the urban subsurface for competing purposes, such as anthropogenic infrastructures and geothermal energy applications, leads to an urgent need for large-scale sophisticated modelling approaches for coupled mass and heat transfer. However, such models are subject to large uncertainties in model parameters, the physical model itself and in available measured data, which is often rare. Thus, the robustness and reliability of the computer model and its outcomes largely depend on successful parameter estimation and model calibration, which are hampered by the computational burden of large-scale coupled models. To tackle this problem, we develop a novel Bayesian approach for parameter estimation, which allows us to account for different sources of uncertainty, is capable of dealing with sparse field data and makes optimal use of the output data from expensive numerical model runs. This is achieved by combining output data from different models that represent the same physical problem, but at different levels of fidelity, e.g. reflected by different spatial resolution. By applying this new approach to a 1D analytical heat transfer model and a large-scale semi-3D numerical model while using synthetic data, we show that the accuracy and precision of parameter estimation by this multi-fidelity framework by far exceeds the standard single-fidelity results. The consideration of different error terms in the Bayesian framework also allows assessment of the model bias and the discrepancy between the different fidelity levels. These are emulated by Gaussian Process models, which facilitate re-iteration of the parameter estimation without additional model runs. Unlabelled Image • A novel method for parameter estimation under different uncertainties is presented. • It combines outputs from models with different accuracies and computational burden. • We demonstrate the framework on (hydro-)thermal subsurface models with sparse data. • Comparison with a standard estimation method shows significantly improved precision. • Our highly efficient method is particularly suited for large-scale numerical models. [ABSTRACT FROM AUTHOR]