Prediction of unconfined compressive strength of cement paste with pure metal compound additions.
CEMENT CONCRETE RES
903 - 913.
Neural network analysis was used to construct models of unconfined compressive strength (UCS) as a function of mix composition using existing data from literature studies of pure compound additions to Portland cement paste. The models were able to represent the known nonlinear dependency of UCS on age and water content, and generalised from the literature data to find relationships between UCS and contaminant concentrations, resulting in the following ranking of the UCS values predicted for addition of the contaminants, on an equimolar basis: at 7 days, Clapproximate toCr(III)>NO3- approximate toCd>control>Zngreater than or equal toNi>Pb>Cu>>Ba; at 28 days, Cl>Cr(III)>NO3- approximate tocontrolgreater than or equal toZngreater than or equal toCd>Ni>Ph>Cu>>Ba. Application of the best neural network to other data suggested that Cs is a retarder and Cr(VI) has no effect. No trends could be discerned for Hg, K, Mn, Na and SO42-. The root-mean-square error for the best neural network seems to be an estimate of the interlaboratory error for UCS. (C) 2002 Published by Elsevier Science Ltd.
|Title:||Prediction of unconfined compressive strength of cement paste with pure metal compound additions|
|Keywords:||acceleration, retardation, compressive strength, heavy metals, toxic metal, waste management, LEACHABILITY, LEAD|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering
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