Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young's modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multilayer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete. (C) 2008 Elsevier B.V. All rights reserved

Control and surveillance of partially observed stochastic epidemics in a Bayesian framework

This thesis comprises a number of inter-related parts. For most of the thesis we are concerned with developing a new statistical technique that can enable the identi cation of the optimal control by comparing competing control strategies for stochastic epidemic models in real time. In the second part, we develop a novel approach for modelling the spread of Peste des Petits Ruminants (PPR) virus within a given country and the risk of introduction to other countries. The control of highly infectious diseases of agriculture crops, animal and human diseases is considered as one of the key challenges in epidemiological and ecological modelling. Previous methods for analysis of epidemics, in which different controls are compared, do not make full use of the trajectory of the epidemic. Most methods use the information provided by the model parameters which may consider partial information on the epidemic trajectory, so for example the same control strategy may lead to different outcomes when the experiment is repeated. Also, by using partial information it is observed that it might need more simulated realisations when comparing two different controls. We introduce a statistical technique that makes full use of the available information in estimating the effect of competing control strategies on real-time epidemic outbreaks. The key to this approach lies in identifying a suitable mechanism to couple epidemics, which could be unaffected by controls. To that end, we use the Sellke construction as a latent process to link epidemics with different control strategies. The method is initially applied on non-spatial processes including SIR and SIS models assuming that there are no observation data available before moving on to more complex models that explicitly represent the spatial nature of the epidemic spread. In the latter case, the analysis is conditioned on some observed data and inference on the model parameters is performed in Bayesian framework using the Markov Chain Monte Carlo (MCMC) techniques coupled with the data augmentation methods. The methodology is applied on various simulated data sets and to citrus canker data from Florida. Results suggest that the approach leads to highly positively correlated outcomes of different controls, thus reducing the variability between the effect of different control strategies, hence providing a more efficient estimator of their expected differences. Therefore, a reduction of the number of realisations required to compare competing strategies in term of their expected outcomes is obtained. The main purpose of the final part of this thesis is to develop a novel approach to modelling the speed of Pest des Petits Ruminants (PPR) within a given country and to understand the risk of subsequent spread to other countries. We are interested in constructing models that can be fitted using information on the occurrence of outbreaks as the information on the susceptible population is not available, and use these models to estimate the speed of spatial spread of the virus. However, there was little prior modelling on which the models developed here could be built. We start by first establishing a spatio-temporal stochastic formulation for the spread of PPR. This modelling is then used to estimate spatial transmission and speed of spread. To account for uncertainty on the lack of information on the susceptible population, we apply ideas from Bayesian modelling and data augmentation by treating the transmission network as a missing quantity. Lastly, we establish a network model to address questions regarding the risk of spread in the large-scale network of countries and introduce the notion of ` first-passage time' using techniques from graph theory and operational research such as the Bellman-Ford algorithm. The methodology is first applied to PPR data from Tunisia and on simulated data. We also use simulated models to investigate the dynamics of spread through a network of countries

Representation, focus, and movement of covert visual attention

This thesis investigated some of the factors involved in the representation, focus, and movement of covert attention in visual space. Existing research has shown that visual cues produced a facilitation of reaction times (RTs) to visual targets appearing at a cued location for up to 300ms. At longer stimulus-onset-asynchronies (SOAs), this was followed by an increase in RTs relative to the uncued location. This has been termed inhibition of return (lOR). Experiments 1-6 used LED cues which were the same as, or spatially distinct from, a target LED, and were either informative or uninformative about the target location. Results were inconsistent. Where discrimination problems existed, the cue/target probabilities altered the results: from evidence of inhibitory effects, to no significant cue-side effects. Where no discrimination problems existed, facilitatory effects were apparent, and were enhanced by an increased cue/target probability. Experiments 7-10, using exogenous cueing, manipulated attentional focus and the presence of cue-markers. Altering the focus size did not substantiate previous findings of a reciprocal relationship between focus and performance. The removal of cue-markers resulted in increased amounts of inhibition not supporting current single- or dual-component views of lOR. Previous work has shown that informative symbolic visual cues produced costs and benefits of RTs to visual targets. Experiments 11-16, using static endogenous cueing with targets framed in central and peripheral locations, attempted to demonstrate object-based attentional representation. All six experiments showed significant effects due to SOA and cue validity, however, initial results showed no evidence of stimulus-grouping. Only when target position markers were removed, when peripheral targets were used, and when inside/outside location judgements were required instead of target detection, did results indicate some possibility of grouped attentional representation. Finally, several of the experiments also investigated the nature of attentional movement. Results did not support straightforward analogue explanations