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    ABSTRACT: This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1 h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement.
    Environmental Modelling and Software 10/2014; 60:265–276. DOI:10.1016/j.envsoft.2014.06.016
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    ABSTRACT: The Nkout deposit is part of an emerging iron ore province in West and Central Africa. The deposit is an oxide facies iron formation comprising fresh magnetite banded iron formation (BIF) at depth, which weathers and oxidises towards the surface forming caps of high grade hematite/martite–goethite ores. The mineral species, compositions, mineral associations, and liberation have been studied using automated mineralogy (QEMSCAN®) combined with whole rock geochemistry, mineral chemistry and mineralogical techniques. Drill cores (saprolitic, lateritic, BIF), grab and outcrop samples were studied and divided into 4 main groups based on whole rock Fe content and a weathering index. The groups are; enriched material (EM), weathered magnetite itabirite (WMI), transitional magnetite itabirite (TMI) and magnetite itabirite (MI). The main iron minerals are the iron oxides (magnetite, hematite, and goethite) and chamosite. The iron oxides are closely associated in the high grade cap and liberation of them individually is poor. Liberation increases when they are grouped together as iron oxides. Chamosite significantly lowers the liberation of the iron oxides. Automated mineralogy by QEMSCAN® (or other similar techniques) can distinguish between Fe oxides if set up and calibrated carefully using the backscattered electron signal. Electron beam techniques have the advantage over other quantitative mineralogy techniques of being able to determine mineral chemical variants of ore and gangue minerals, although reflected light optical microscopy remains the most sensitive method of distinguishing closely related iron oxide minerals. Both optical and electron beam automated mineralogical methods have distinct advantages over quantitative XRD in that they can determine mineral associations, liberation, amorphous phases and trace phases.
    Ore Geology Reviews 10/2014; 62:25–39. DOI:10.1016/j.oregeorev.2014.02.015
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    ABSTRACT: This paper describes the development and the evaluation of a robust sliding mode observer fault detection scheme applied to an aircraft benchmark problem as part of the ADDSAFE project. The ADDSAFE benchmark problem which is considered in this paper is the yaw rate sensor fault scenario. A robust sliding mode sensor fault reconstruction scheme based on an LPV model is presented, where the fault reconstruction signal is obtained from the so-called equivalent output error injection signal associated with the observer. The development process includes implementing the design using AIRBUS׳s the so-called SAO library which allows the automatic generation of flight certifiable code which can be implemented on the actual flight control computer. The proposed scheme has been subjected to various tests and evaluations on the Functional Engineering Simulator conducted by the industrial partners associated with the ADDSAFE project. These were designed to cover a wide range of the flight envelope, specific challenging manoeuvres and realistic fault types. The detection and isolation logic together with a statistical assessment of the FDD schemes are also presented. Simulation results from various levels of FDD developments (from tuning, testing and industrial evaluation) show consistently good results and fast detection times.
    Control Engineering Practice 10/2014; 31. DOI:10.1016/j.conengprac.2014.05.003
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    ABSTRACT: Materials with a negative Poisson’s ratio are referred to as auxetic. One recently invented example of this is the helical auxetic yarn (HAY). This has been proved to successfully exhibit auxetic behaviour both as a yarn and when incorporated into fabric. The HAY is based on a double-helix geometry where a relatively stiffer ‘wrap’ is helically wound around a compliant core fibre. This paper studies the effect of the interaction between the core and the wrap fibre on the auxetic behaviour of the HAY, including the effect of their relative moduli. Assessment of the Poisson’s ratio of the HAYs has revealed that an elevated difference in component moduli causes the wrap fibre embedding itself into the core fibre, thus decreasing the auxetic effect. Careful determination of an optimum core–wrap moduli ratio where the ratio is high enough to yield an auxetic effect and low enough to prevent the core-indentation effect can lead to the fabrication of a yarn with largest negative Poisson’s ratio.
    Composites Science and Technology 10/2014; 102:87–93. DOI:10.1016/j.compscitech.2014.07.023
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    ABSTRACT: This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures – a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.
    Computers & Structures 05/2014; 136:64–77. DOI:10.1016/j.compstruc.2014.01.026
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    ABSTRACT: The vision of large-scale commercial arrays of floating marine energy converters (MECs) necessitates the robust, yet cost-effective engineering of devices. Given the continuous environmental loading, fatigue has been identified as one of the key engineering challenges. In particular the mooring system which warrants the station-keeping of such devices is subject to highly cyclic, non-linear load conditions, mainly induced by the incident waves.To ensure the integrity of the mooring system the lifecycle fatigue spectrum must be predicted in order to compare the expected fatigue damage against the design limits. The fatigue design of components is commonly assessed through numerical modelling of representative load cases. However, for new applications such as floating marine energy converters numerical models are often scantily validated.This paper describes an approach where load measurements from large-scale field trials at the South West Mooring Testing Facility (SWMTF) are used to calculate and predict the fatigue damage. The described procedure employs a Rainflow cycle analysis in conjunction with the Pålmgren–Miner rule to estimate the accumulated damage for the deployment periods and individual sea states.This approach allows an accurate fatigue assessment and prediction of mooring lines at a design stage, where field trial load measurements and wave climate information of potential installation sites are available. The mooring design can thus be optimised regarding its fatigue life and costly safety factors can be reduced. The proposed method also assists in monitoring and assessing the fatigue life during deployment periods.
    Renewable Energy 03/2014; 63:133–144. DOI:10.1016/j.renene.2013.08.050
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    ABSTRACT: As mass production has migrated to developing countries, European and US companies are forced to rapidly switch towards low volume production of more innovative, customised and sustainable products with high added value. To compete in this turbulent environment, manufacturers have sought new fabrication techniques to provide the necessary tools to support the need for increased flexibility and enable economic low volume production. One such emerging technique is Additive Manufacturing (AM). AM is a method of manufacture which involves the joining of materials, usually layer-upon-layer, to create objects from 3D model data. The benefits of this methodology include new design freedom, removal of tooling requirements, and economic low volumes. AM consists of various technologies to process versatile materials, and for many years its dominant application has been the manufacture of prototypes, or Rapid Prototyping. However, the recent growth in applications for direct part manufacture, or Rapid Manufacturing, has resulted in much research effort focusing on development of new processes and materials. This study focuses on the implementation process of AM and is motivated by the lack of socio-technical studies in this area. It addresses the need for existing and potential future AM project managers to have an implementation framework to guide their efforts in adopting this new and potentially disruptive technology class to produce high value products and generate new business opportunities. Based on a review of prior works and through qualitative case study analysis, we construct and test a normative structural model of implementation factors related to AM technology, supply chain, organisation, operations and strategy.
    International Journal of Production Economics 03/2014; 149:194–201. DOI:10.1016/j.ijpe.2013.07.008
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    ABSTRACT: In tonnage terms the commercial production of engineering composites is dominated by glass reinforced systems, this is particularly the case in the automotive industry. Natural fibres have long been regarded as a viable lightweight replacement for glass, however the various shortcomings of natural/cellulosic fibres have so far, inhibited exploitation, where resistance to fast fracture during impact is a major failing. Composite mesostructure describes mid-scale structures in composites, such as fibre alignment patterns, bundling effects, and fibre end synchronisation. The mesostructure can dramatically affect final properties in some random short fibre systems where flow is involved, such as sheet moulding compounds (SMC), and can be the determining factor in, for example, the success of one fibre system over another. This study seeks to manipulate the fibre mesostructure in moulding compounds reinforced with natural/cellulosic fibres, where it is shown that by arranging mechanically inferior fibres in bundles, composite impact energy absorption can be substantially improved, where the reasons behind the toughening mechanism at work, is discussed and optimum bundle dimensions for several fibre systems are identified. Fibre bundling seems to be a highly interesting method for toughening composites made from mechanically inferior natural/cellulosic fibres, however no work in the area has been reported until now.
    Composites Science and Technology 03/2014; 93:97–105. DOI:10.1016/j.compscitech.2014.01.003
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    ABSTRACT: There is growing awareness and public concern about environmental impacts of waste management and disposal. Environmental policy instruments have been strengthened and associated governmental programmes have increased in recent years, resulting in high level strategies for waste management. Risk assessment is now an essential tool in the prioritisation of environmental and human health protection. However, regulators need to compare the full range of risks on a sound and consistent basis. Comparing risks from such diverse sources poses a significant challenge, and traditional hazard and risk assessments are no longer sufficient. Consideration now needs to be given to a much wider range of factors if risk assessment is to be used as an aid to more integrated decision-making process. For this purpose, baseline study - the foundation of risk assessment - can play a crucial role. To date limited research has been conducted on the need, parameters, requirements, and constituents of baseline study particularly in the context of how, why, and what information is to be collated in order to render risk assessments more appropriately integrated and complete. To establish the 'state-of-the-art' of baseline study, this paper comprehensively reviews the literature regarding environmental risk assessment in general terms, and then proceeds to review work that is specifically related to landfills and landfill leachate, thereby identifying knowledge gaps and shortfall areas. This review concludes that a holistic baseline study procedure for waste disposal sites, which risk assessors could use for carrying out risk analyses specifically for landfill leachate, does not as yet exist.
    Environment international 11/2013; 63C:149-162. DOI:10.1016/j.envint.2013.09.015
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    ABSTRACT: This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology.
    Advanced Engineering Informatics 10/2013; 27(4):486–495. DOI:10.1016/j.aei.2013.03.002
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