{"title":"Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks","authors":"Vinay Chandwani, Vinay Agrawal, Ravindra Nagar","volume":93,"journal":"International Journal of Civil and Environmental Engineering","pagesStart":987,"pagesEnd":995,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9999439","abstract":"
Artificial Neural Networks (ANN) trained using backpropagation
\r\n(BP) algorithm are commonly used for modeling
\r\nmaterial behavior associated with non-linear, complex or unknown
\r\ninteractions among the material constituents. Despite multidisciplinary
\r\napplications of back-propagation neural networks
\r\n(BPNN), the BP algorithm possesses the inherent drawback of
\r\ngetting trapped in local minima and slowly converging to a global
\r\noptimum. The paper present a hybrid artificial neural networks and
\r\ngenetic algorithm approach for modeling slump of ready mix
\r\nconcrete based on its design mix constituents. Genetic algorithms
\r\n(GA) global search is employed for evolving the initial weights and
\r\nbiases for training of neural networks, which are further fine tuned
\r\nusing the BP algorithm. The study showed that, hybrid ANN-GA
\r\nmodel provided consistent predictions in comparison to commonly
\r\nused BPNN model. In comparison to BPNN model, the hybrid ANNGA
\r\nmodel was able to reach the desired performance goal quickly.
\r\nApart from the modeling slump of ready mix concrete, the synaptic
\r\nweights of neural networks were harnessed for analyzing the relative
\r\nimportance of concrete design mix constituents on the slump value.
\r\nThe sand and water constituents of the concrete design mix were
\r\nfound to exhibit maximum importance on the concrete slump value.<\/p>\r\n","references":"[1] P.K. Mehta and P.J.M. Monteiro, Concrete: Structure, Properties and\r\nMaterials. 3rd ed. New York: McGraw Hill, 2006.\r\n[2] Z. Li, Advanced Concrete Technology. 1st ed. New Jersey: John Wiley &\r\nSons, Inc, 2011.\r\n[3] W.P.S. Dias and S.P. Pooliyadda, \"Neural Networks for predicting\r\nproperties of concrete with admixtures,\u201d Construction and Building\r\nMaterials, vol. 15, no. 7, pp. 371-379, 2001.\r\n[4] I.-C. Yeh, \"Exploring concrete slump model using artificial neural\r\nnetworks,\u201d Journal of Computing in Civil Engineering, vol. 20, no. 3,\r\npp. 217-221, 2006.\r\n[5] A. Oztas, M. Pala, E. Ozbay, E. Kanca, N. 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