6,310 research outputs found

    Coupled THM analysis of long-term anisotropic convergence in the full-scale micro tunnel excavated in the Callovo-Oxfordian argillite

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    The main purpose of this paper is to analyse the convergence measurements of the ALC1604 in situ heating test carried out in the Callovo-Oxfordian claystone formation (COx) in the Meuse/Haute-Marne underground research laboratory (MHM URL). The concept of the test consists of horizontal micro-tunnel, equipped with a steel casing. The micro-tunnel is excavated in the direction of the horizontal principal major stress (sH). In situ observations showed anisotropic convergence with the maximum and minimum values in the horizontal and vertical directions, respectively. Coupled THM numerical analyses have been carried out to provide a structured framework for interpretation, and to enhance understanding of THM behaviour of Callovo-Oxfordian claystone. However, a special mechanical constitutive law is adopted for the description of the time-dependent anisotropic behaviour of the COx. The simulation of the test using this enhanced model provides a satisfactory reproduction of the THM long-term anisotropic convergence results. It also provides a better understanding of the observed test response.Postprint (published version

    Thermo-hydro-mechanical simulation of a full-scale steel-lined micro-tunnel excavated in the callovooxfordian claystone

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    The paper presents an interpretation of the full-scale ALC1604 in situ heating test carried out in Callovo-Oxfordian claystone (COx) in the Meuse/Haute-Marne underground research laboratory (MHM URL). The MHM URL is a site-specific facility planned to study radioactive waste disposal in the COx. The thermo-hydro-mechanical (THM) behaviour of the host rock is significant for the design of the underground radioactive waste disposal facility and for its long-term safety. When subjected to thermal loading, the Callovo-Oxfordian claystone of low permeability (~10-20-10-21 m2) exhibits a strong pore pressure response that significantly affects the hydraulic and mechanical behaviour of the material. The observations gathered in the in situ test have provided an opportunity to examine the integrated thermo-hydromechanical (THM) response of this sedimentary clay. Coupled THM numerical analyses have been carried out to provide a structured framework for interpretation, and to enhance understanding of THM behaviour of COx. Numerical analyses have been based on a coupled theoretical formulation that incorporates a constitutive law specially developed for this type of material. The law includes a number of features that are relevant for a satisfactory description of the hydromechanical behaviour. By performing the numerical analysis, it has been possible to incorporate anisotropy of material parameters and of in situ stresses. The performance and analysis of the in situ tests have significantly enhanced the understanding of a complex THM problem and have proved the capability of the numerical formulation to provide adequate predictive capacity

    Precision and Sensitivity in Detailed-Balance Reaction Networks

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    We study two specific measures of quality of chemical reaction networks, Precision and Sensitivity. The two measures arise in the study of sensory adaptation, in which the reaction network is viewed as an input-output system. Given a step change in input, Sensitivity is a measure of the magnitude of the response, while Precision is a measure of the degree to which the system returns to its original output for large time. High values of both are necessary for high-quality adaptation. We focus on reaction networks without dissipation, which we interpret as detailed-balance, mass-action networks. We give various upper and lower bounds on the optimal values of Sensitivity and Precision, characterized in terms of the stoichiometry, by using a combination of ideas from matroid theory and differential-equation theory. Among other results, we show that this class of non-dissipative systems contains networks with arbitrarily high values of both Sensitivity and Precision. This good performance does come at a cost, however, since certain ratios of concentrations need to be large, the network has to be extensive, or the network should show strongly different time scales

    Capacitated Transport Vehicle Routing for Joint Distribution in Supply Chain Networks

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    This paper addresses the problem of capacitated transport vehicle routing for joint distribution of different suppliers. Although all suppliers distribute similar products to cities within the same region, each supplier has an extensive distribution network. In this paper, we develop solution procedures that guide the problem-solving process and quickly lead to a good routing solution. A case study is introduced with three real-world case problems for illustrating the applicability and suitability of the proposed approach. The results show that solution procedure approach for is more than reasonable for quickly constructing good transport vehicle routes

    Bioactive molecule prediction using extreme gradient boosting

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    Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and NaĂŻve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets
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