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Concrete strength prediction using artificial neural network and genetic programming

By Preeti Kulkarni and Shreenivas N. Londhe

Abstract

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations

Topics: , artificial neural network; genetic programming; concrete; compressive strength
Publisher: 'Tulpar Academic Publishing'
Year: 2018
DOI identifier: 10.20528/cjcrl.2018.03.002
OAI identifier: oai:challengejournal.com:article/347

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