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2021 | OriginalPaper | Buchkapitel

Prediction of Compressive Strength and Electrical Resistivity of Mortar Mixes Containing Industrial Waste Products

verfasst von : Maninder Singh, Babita Saini, H. D. Chalak

Erschienen in: Smart Technologies for Sustainable Development

Verlag: Springer Singapore

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Abstract

In the present paper, non-linear soft computing technique (neural network) has been used to predict the compressive strength and electrical resistivity of cement mortar at 7 and 28 days. Thirteen mixes of cement mortar consisting of silica fume and alccofine as subrogation of cement were selected. The training and testing data used in ANN predictive model were based on experimental results in the laboratory. Cement, silica fume, alccofine, sand and water were used as input parameters. The predicted results obtained from ANN using multilayer feedforward neural network were compared with the experimental results. Results showed that ANN technique is effective for the prediction of strength in compression and electrical resistivity of various cement mortar mixes and correlation coefficients were also high. The values of correlation coefficient (R) and R² were higher at 28 days than 7 days results for both compressive strength and electrical resistivity.

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Titel: Prediction of Compressive Strength and Electrical Resistivity of Mortar Mixes Containing Industrial Waste Products
verfasst von: Maninder Singh
Babita Saini
H. D. Chalak
Verlag: Springer Singapore
Buch: Smart Technologies for Sustainable Development
Print ISBN: 978-981-15-5000-3

Electronic ISBN: 978-981-15-5001-0

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-15-5001-0_16

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