Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

For the first time, graphene nanoplatelets (xGnP) were incorporated at 4 wt% in acrylonitrile–butadiene–styrene (ABS) filaments obtained by a solvent-free process consisting of melt compounding and extrusion. Nanocomposite filaments were then used to feed a fused deposition modelling (FDM) machine to obtain specimens with various build orientations. The elastic modulus and dynamic storage moduli of 3D printed parts along three different build orientations were increased by the presence of xGnP in the ABS matrix. At the same time, a decrease in both stress and strain at break was observed when xGnP is added to ABS. Moreover, a higher thermal stability was induced on 3D printed parts by xGnP, as indicated by a reduction in both coefficient of linear thermal expansion and creep compliance. A comparison between 3D printed and compression moulded parts highlighted the importance of the orientation effects induced by the fused deposition modelling process.

Fused deposition modelling with ABS–graphene nanocomposites

Towards sustainable urban water management: A critical reassessment

Statistical Analysis of Reliability Data

Thermal degradation of acrylonitrile–butadiene–styrene (ABS) blends

A physical probabilistic model to predict failure rates in buried PVC pipelines

In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deteriorati...

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Strong exploration of a cast iron pipe failure model

Asbestos cement (AC) pipes are among the oldest assets in many water supply networks. With increasing failure rates and cost consequences, asset management tools are required to pre-empt unplanned failures and schedule future replacement at the most cost-effective time during service. This paper develops a physical probabilistic failure model for AC pipes under combined internal pressure and external loading. Uncertainty in the degradation process is accounted for using the Weibull extreme value probability distribution. Monte Carlo simulation is then used to estimate the probability of pipe failure as ageing proceeds. In the final stage of the model, a cost/benefit analysis is conducted to determine optimal scheduling of future inspection and replacement activities. The end result is a potentially useful asset management methodology in which both the probable physical lifetime and the economic lifetime of an AC pipe can be estimated.

Paul Davis

Papers

Thermal degradation of acrylonitrile–butadiene–styrene (ABS) blends

A physical probabilistic model to predict failure rates in buried PVC pipelines

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Strong exploration of a cast iron pipe failure model

Failure prediction and optimal scheduling of replacements in asbestos cement water pipes