158 research outputs found

    Prediction of subsidence due to underground mining by artificial neural networks

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    Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network was used. The training and testing of neural network is based on the available data. Input variables represent extraction parameters and coordinates of the points of interest, while the output variable represents surface subsidence data. After the neural network has been successfully trained, its performance is tested on a separate testing set. Finally, the surface subsidence trough above the projected excavation is predicted by the trained neural network. The applicability of ANN for the prediction of surface subsidence was verified in different subsidence models and proved on actual excavated levels and in levelled data on surface profile points in the Velenje Coal Mine. (C) 2003 Elsevier Science Ltd. All rights reserved

    Combining the radial basis function Eulerian and Lagrangian schemes with geostatistics for modeling of radionuclide migration through the geosphere

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    To assess the long-term safety of a radioactive waste disposal system, mathematical models are used to describe groundwater flow, chemistry, and potential radionuclide migration through geological formations. A number of processes need to be considered, when predicting the movement of radionuclides through the geosphere. The most important input data are obtained from field measurements, which are not available for all regions of interest. For example, the hydraulic conductivity as an input parameter varies from place to place. In such cases, geostatistical science offers a variety of spatial estimation procedures. Methods for solving the solute transport equation can also be classified as Eulerian, Lagrangian and mixed. The numerical solution of partial differential equations (PDE) has usually been obtained by finite-difference methods (FDM), finite-element methods (FEM), or finite-volume methods (FVM). Kansa introduced the concept of solving partial differential equations using radial basis functions (RBF) for hyperbolic, parabolic, and elliptic PDEs. The aim of this study was to present a relatively new approach to the modeling of radionuclide migration through the geosphere using radial basis function methods in Eulerian and Lagrangian coordinates. In this study, we determine the average and standard deviation of radionuclide concentration with regard to variable hydraulic conductivity, which was modelled by a geostatistical approach. Radionuclide concentrations will also be calculated in heterogeneous and partly heterogeneous 2D porous media. (C) 2004 Elsevier Ltd. All rights reserved

    Statistical testing of directions observations independence

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    Independence of observations is often assumed when adjusting geodetic network. Unlike the\ud distance observations, no dependence of environmental conditions is known for horizontal\ud direction observations. In order to determine the dependence of horizontal direction observations,\ud we established test geodetic network of a station and four observation points. Measurements of\ud the highest possible accuracy were carried out using Leica TS30 total station along with precise\ud prisms GPH1P. Two series of hundred sets of angles were measured, with the first one in bad\ud observation conditions. Using different methods, i.e. variance–covariance matrices, x2 test and analyses of time series, the independence of measured directions, reduced directions and horizontal angles were tested. The results show that the independence of horizontal direction\ud observations is not obvious and certainly not in poor conditions. In this case, it would be appropriate for geodetic network adjustments to use variance–covariance matrix calculated from measurements instead of diagonal variance–covariance matrix

    The use of the mesh free methods (radial basis functions) in the modeling of radionuclide migration and moving boundary value problems

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    Recently, the mesh free methods (radial basis functions-RBFs) have emerged as a novel computing method in the scientific and engineering computing community. The numerical solution of partial differential equations (PDEs) has been usually obtained by finite difference methods (FDM), finite element methods (FEM) and boundary elements methods (BEM). These conventional numerical methods still have some drawbacks. For example, the construction of the mesh in two or more dimensions is a nontrivial problem. Solving PDEs using radial basis function (RBF) collocations is an attractive alternative to these traditional methods because no tedious mesh generation is required. We compare the mesh free method, which uses radial basis functions, with the traditional finite difference scheme and analytical solutions. We will present some examples of using RBFs in geostatistical analysis of radionuclide migration modeling. The advection-dispersion equation will be used in the Eulerian and Lagrangian forms. Stefan's or moving boundary value problems will also be presented. The position of the moving boundary will be simulated by the moving data centers method and level set method

    The use of artificial neural networks in adiabatic curves modeling

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    Adiabatic hydration curves are the most suitable data for temperature calculations in concrete hardening structures. However, it is very difficult to predict the adiabatic hydration curve of an arbitrary concrete mixture. The idea of modeling adiabatic temperature rise during concrete hydration with the use of artificial neural networks was introduced in order to describe the adiabatic hydration of an arbitrary concrete mixture, depending on factors which influence the hydration process of cement in concrete. The influence of these factors was determined by our own experiments. A comparison between experimentally determined adiabatic curves and adiabatic curves, evaluated by proposed numerical model shows that artificial neural networks can be used to predict adiabatic hydration curves effectively. This model can be easily incorporated in the computer programs for prediction of the thermal fields in young concrete structures, implemented in the finite element or finite difference codes. New adiabatic hydration curves with some other initial parameters of the concrete mixture can be easily included in this model in order to expand the range of suitability of artificial neural networks to predict the adiabatic hydration curves. (C) 2008 Elsevier B.V. All rights reserved

    Reliability analysis of a glulam beam

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    The present case study is an example of the use of reliability analysis to asses the failure probability of a tapered glulam beam. This beam is part of a true structure built for a super market in the town of Kokemaki in Finland. The reliability analysis is carried out using the snow load statistics available from the site and on material strength information available from previous experiments. The Eurocode 5 and the Finnish building code are used as the deterministic methods to which the probabilistic method is compared to. The calculations show that the effect of the strength variation is not significant, when the coefficient of variation of the strength is around 15% as usually assumed for glulam. The probability of failure resulting from a deterministic design based on Eurocode 5 is low compared to the target values and lower sections are possible if applying a probabilistic design method. In fire design, if a 60 min resistance is required, this is not the case according to Eurocode 5 design procedures, a higher section would be required. However, a probabilistic based fire analysis results in bounds for the yearly probability of failure which are comparable to the target value and to the values obtained from the normal probabilistic based design. (C) 2006 Elsevier Ltd. All rights reserved

    Modelling of radionuclide migration through the geosphere with radial basis function method and geostatistics

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    The modelling of radionuclide transport through the geosphere is necessary in the safety assessment of repositories for radioactive waste. A number of key geosphere processes need to be considered when predicting the movement of radionuclides through the geosphere. The most important input data are obtained from field measurements, which are not available for all regions of interest. For example, the hydraulic conductivity, as input parameter, varies from place to place. In such cases geostatistical science offers a variety of spatial estimation procedures. To assess the a long term safety of a radioactive waste disposal system, mathematical models are used to describe the complicated groundwater flow, chemistry and potential radionuclide migration through geological formations. The numerical solution of partial differential equations (PDEs) has usually been obtained by finite difference methods (FDM), finite element methods (FEM), or finite volume methods (FVM). Kansa introduced the concept of solving PDEs using radial basis functions (RBFs) for hyperbolic, parabolic and elliptic PDEs. The aim of this study was to present a relatively new approach to the modelling of radionuclide migration through the geosphere using radial basis functions methods and to determine the average and sample variance of radionuclide concentration with regard to spatial variability of hydraulic conductivity modelled by a geostatistical approach. We will also explore residual errors and their influence on optimal shape parameters

    The world OA iniciative for scientific communication in civil engineering and institutional repository as its answer – the case study of Slovenia. Poster presentation at 79th IFLA Congress.

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    Scientific communication in technics consists primarily of articles in peer-reviewed scholarly journals. In our research of 2026 articles, published in JCR journals in the field of civil engineering in 2007, we found out that 21% of them are published as open accessed articles, most of them as articles archived in institutional repositories. They reached 29% of all citations in the analysed period. In accordance with these findings, in 2011 the repository at the University of Ljubljana, Faculty of Civil and Geodetic Engineering was built in open source tool ePrints. 1400 theses as well as 200 research articles have been archived in it till January 2013. The library was one of the most important stakeholders in its building as it is still today. Each of the repository unit is linked into Slovenian bibliographical information system COBISS and its cataloguing part COBIB as well as the part of high valuated research works of Slovenian researchers SICRIS. The statistics (150.000 visits from all over the world in one year) is a motivation for us to develop it further. In the near future the connection to the Slovenian Digital Library (dLib.si) shall be established, which will ensure the preservation of metadata

    Does the Open Access Business Model Have a Significant Impact on the Citation of Publications? Case Study in the Field of Civil Engineering

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    One of the possible benefits of open access (OA) might be the better visibility of articles,which is usually measured by the number of citations of the article. In order to\ud realistically estimate the effect of OA on citation, it is not enough to compare OA and non-OA ISI journals. Thus, as Harnad and Brody (2004) suggested, the numbers of citations\ud of OA and non-OA articles from the same journals were compared. Therefore, we have chosen to analyze the publications in three international journals in the field\ud of civil engineering. All of them have an ISI impact factor in the Civil engineering subject category in the ISI/Web of science database (WOS). The articles were classified\ud into two groups − the OA publications and the non-OA publications. We analyzed all the articles published in the same year and the number of their citations until the end of February 2012, seeking to find out if these two groups differ from each other

    Non-linear fire-resistance analysis of reinforced concrete beams

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    The non-linear structural analysis of reinforced concrete beams in fire consists of three separate steps: (i) The estimation of the rise of surrounding air temperature due to fire; (ii) the determination of the distribution of the temperature within the beam during fire; (iii) the evaluation of the mechanical response due to simultaneous time-dependent thermal and mechanical loads. Steps (ii) and (iii) are dealt with in the present paper. We present a two-step computational procedure where a 2D transient thermal analysis over the cross-sections of beams are made first, followed by mechanical analysis of the structure. Fundamental to the accuracy of the mechanical analysis is a new planar beam finite element. The effects of plasticity in concrete, and plasticity and viscous creep in steel are taken into consideration. The properties of concrete and steel along with the values of their thermal and mechanical parameters are taken according to the European standard ENV 1992-1-2 (1995). The comparison of our numerical and full-scale experimental results shows that the proposed mechanical and 2D thermal computational procedure is capable to describe the actual response of reinforced concrete beam structures to fire
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