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Mining of existing data for cement-solidified wastes using neural networks

Stegemann, JA; Buenfeld, NR; (2004) Mining of existing data for cement-solidified wastes using neural networks. J ENVIRON ENG-ASCE , 130 (5) 508 - 515. 10.1061/(ASCE)0733-9372(2004)130:5(508).

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Abstract

This paper summarizes the results of an investigation into the use of neural networks to analyze data collected from the literature regarding the interaction of wastes and hydraulic binders in, and final properties of, cement-solidified wastes. Neural network models were constructed for prediction of the effects of contaminants on setting time, unconfined compressive strength, and leachate pH. It was found that construction of successful models was possible, with prediction errors approaching experimental error, and that modeling was useful for generalizing about the relative effects of the input variables on the outputs using the results from the different studies. The work has shown that the potential for practical implementation of models of this type in prediction of key properties related to long-term behavior, and/or formulation design in waste treatment facilities clearly exists, but more detailed definition of the data space by experimentation, with more complete harmonization of methods and reporting of experimental results, will be necessary to develop reliable commercial models.

Type: Article
Title: Mining of existing data for cement-solidified wastes using neural networks
DOI: 10.1061/(ASCE)0733-9372(2004)130:5(508)
Keywords: cements, stabilization, solidification, metals, predictions, pH, data analysis, solid wastes, models, STRENGTH PREDICTION, CONCRETE STRENGTH, BEHAVIOR, PASTE
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: http://discovery.ucl.ac.uk/id/eprint/75916
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