Modelling the mechanical strength development of treated fine sediments: a statistical approach

Abstract : Sediments valorization (recycling) has revealed limitations due to different restrains and practical difficulties. When it comes to different recovery methods, the possibility of valuing diverse types of sediments still needs to be defined. Using a statistical approach, the present study aims to quantitatively estimate the mechanical resistance of stabilized sediments. A database that included 22 fine sediments is selected and assembled from the literature. These sediments were treated with distinct types and quantities of additives (fillers and/or binders). The present study includes two parts. On one hand, using multivariate linear regression tool of XLstat software, an analytical model that highlights the effects of various parameters influencing the mechanical resistance of treated sediments after 28 days is obtained. This model showed that organic matter content and plasticity index are the most significant factors of sediments characteristics, while cement is the best mechanical strength booster. On the other hand, the evolution of treated sediments mechanical resistance over time is modelled by an exponential relationship using a least square regression method. Both models showed acceptable accuracies compared to a panel of selected experimental values.
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Article dans une revue
Environmental Technology, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2018, pp.1-20. 〈10.1080/09593330.2018.1432697〉
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https://hal-normandie-univ.archives-ouvertes.fr/hal-01983092
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Soumis le : mardi 12 mars 2019 - 11:52:15
Dernière modification le : jeudi 14 mars 2019 - 13:49:29

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Ishak Moghrabi, Harifidy Ranaivomanana, Fateh Bendahmane, Ouali Amiri, Daniel Levacher. Modelling the mechanical strength development of treated fine sediments: a statistical approach. Environmental Technology, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2018, pp.1-20. 〈10.1080/09593330.2018.1432697〉. 〈hal-01983092〉

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