Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks

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Concrete Slump Using Hybrid Artificial Neural Networks</title> <meta name="description" content="Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks"> <meta name="keywords" content="Artificial neural networks, Genetic algorithms, Back-propagation algorithm, Ready Mix Concrete, Slump value."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks"> <meta name="citation_author" content="Vinay Chandwani"> <meta name="citation_author" content="Vinay Agrawal"> <meta name="citation_author" content="Ravindra Nagar"> <meta name="citation_publication_date" content="2014/09/04"> <meta name="citation_journal_title" content="International Journal of Civil and Environmental Engineering"> <meta name="citation_volume" content="8"> <meta name="citation_issue" content="9"> 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href="https://publications.waset.org/search?q=Vinay%20Chandwani">Vinay Chandwani</a>, <a href="https://publications.waset.org/search?q=Vinay%20Agrawal"> Vinay Agrawal</a>, <a href="https://publications.waset.org/search?q=Ravindra%20Nagar"> Ravindra Nagar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.</p> <iframe src="https://publications.waset.org/9999439.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20networks" title="Artificial neural networks">Artificial neural networks</a>, <a href="https://publications.waset.org/search?q=Genetic%20algorithms" title=" Genetic algorithms"> Genetic algorithms</a>, <a href="https://publications.waset.org/search?q=Back-propagation%20algorithm" title=" Back-propagation algorithm"> Back-propagation algorithm</a>, <a href="https://publications.waset.org/search?q=Ready%20Mix%20Concrete" title=" Ready Mix Concrete"> Ready Mix Concrete</a>, <a href="https://publications.waset.org/search?q=Slump%20value." title=" Slump value."> Slump value.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1096180" target="_blank">doi.org/10.5281/zenodo.1096180</a> </p> <a href="https://publications.waset.org/9999439/modeling-and-analysis-of-concrete-slump-using-hybrid-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999439/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999439/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999439/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999439/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999439/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999439/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999439/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999439/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999439/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999439/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2903</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] P.K. Mehta and P.J.M. Monteiro, Concrete: Structure, Properties and Materials. 3rd ed. New York: McGraw Hill, 2006. <br>[2] Z. Li, Advanced Concrete Technology. 1st ed. New Jersey: John Wiley & Sons, Inc, 2011. <br>[3] W.P.S. Dias and S.P. Pooliyadda, "Neural Networks for predicting properties of concrete with admixtures,” Construction and Building Materials, vol. 15, no. 7, pp. 371-379, 2001. <br>[4] I.-C. Yeh, "Exploring concrete slump model using artificial neural networks,” Journal of Computing in Civil Engineering, vol. 20, no. 3, pp. 217-221, 2006. <br>[5] A. Oztas, M. Pala, E. Ozbay, E. Kanca, N. Caglar and M.A. Bhatti, "Predicting the compressive strength and slump of high strength concrete using neural network,” Construction and Building Materials, vol. 20, no. 9, pp. 769-775, 2006. <br>[6] I.-C.Yeh, "Modeling slump flow of concrete using second-order regressions and artificial neural networks,” Cement and Concrete Composites, vol. 29, pp. 474-480, 2007. <br>[7] A. Jain, S.K. Jha and S. Misra, "Modeling and analysis of concrete slump using artificial neural networks,” Journal of Materials in Civil Engineering, vol. 20, no. 9, pp. 628-633, 2008. <br>[8] M. Saridemir, "Prediction of compressive strength of concretes containing metakaolin and silica fumes by artificial neural networks,” Advances in Engineering Software, vol. 40, no. 5, pp. 350-355, 2009. <br>[9] S.J. Kwon and H.W. Song, "Analysis of carbonation behaviour in concrete using neural network algorithm and carbonation modeling,” Cement and Concrete Research, vol. 40, no. 1, pp. 119-127, 2010. <br>[10] R. Siddique, P. Aggarwal and Y. Aggarwal, "Prediction of compressive strength of self compacting concrete containing bottom ash using artificial neural networks,” Advances in Engineering Software, vol. 42, no. 10, pp. 780-786, 2011. <br>[11] M.I. Khan, "Predicting properties of high performance concrete containing composite cementitious materials using Artificial Neural Networks,” Automation in Construction, vol.22, pp. 516-524, 2012. <br>[12] O.A. Hodhod and H.I. Ahmed, "Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete,” HBRC Journal, vol.9, no. 1, pp. 15-21, 2013. <br>[13] A.M. Diab, H.E. Elyamany, A.E.M.A. Elmoaty and A.H. Shalan, "Prediction of concrete compressive strength due to long term sulphate attack using neural networks,” Alexandria Engineering Journal, vol.53, pp. 627-642, 2014. <br>[14] C.L. Su, S.M. Yang and W.L. Huang, "A two stage algorithm integrating genetic algorithms and modified Newton method for neural network training in engineering systems,” Expert Systems with Applications, vol. 38, no. 10, pp. 12189-12194, 2011. <br>[15] A. Johari, A.A. Javadi and G. Habibagahi, "Modelling the mechanical bahaviour of unsaturated soils using a genetic algorithm based neural network,” Computers and Geotechnics, vol. 38, no. 1, pp. 2-13, 2011. <br>[16] H. Karimi and F. Yousefi, " Application of artificial neural networkgenetic algorithm (ANN-GA) to correlation of density in nanofluids,” Fluid Phase Equilibria, vol. 336, pp. 79-83, 2012. <br>[17] R. Wang,C. Zhou, Z. Deng, B. Ni and Z. Zhao, "Predicting foF2 in China region using the artificial neural networks improved by the genetic algorithms,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 92, pp. 7-17, 2013. <br>[18] Y. Xue, L. Cheng, J. Mou and W. Zhao, "A new fracture prediction method by combining genetic algorithm with neural network in lowpermeability reservoirs,” Journal of Petroleum Science and Engineering, vol. 121, pp. 159-166, 2014. <br>[19] M.M. Alshihri, A.M. Azmy and M.S. El-Bisy, "Neural Networks for predicting compressive strength of structural light weight concrete,” Construction and Building Materials, vol. 23, no. 6, pp. 2214-2219, 2009. <br>[20] K. Hornik, M. Stinchcombe and H. White, "Multilayer feed forward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359-366, 1989. <br>[21] S. Tamura and M. Tateishi, "Capabilities of four layered feedforward neural network: four layer versus three,” IEEE Trasactions on Neural Networks, vol. 8, no. 2, pp. 251-255, 1997. <br>[22] P.C. Pendharkar and J.A.Rodger, "Technical efficiency based selection of learning cases to improve the forecasting efficiency of neural networks under monotonicity assumption,”, Decision Support Systems, vol. 36, no. 1, pp. 117-136, 2003. <br>[23] K. Jinchuan and L. Xinzhe, "Empirical analysis of optimal hidden layer neurons in neural network modeling for stock prediction,” in Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Applications, vol. 2, pp. 828-832, Dec. 2008. <br>[24] D. Hunter, Y. Hao, M.S. Pukish, J. Kolbusz and B.M Wilamowski, "Selection of proper Neural Network sizes and architectures-A comparative study,” IEEE Transaction on Industrial Informatics, vol. 8, no. 2, pp. 228-240, 2012. <br>[25] S. Rajasekaran and G.A.V. Pai, "Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications,” New Delhi: Prentice- Hall of India Private Limited, 2003.. <br>[26] B.M. Wilamowski, Y. Chen, A. Malinowski, "Efficient Algorithm for Training Neural Networks with One Hidden Layer,” in Proceedings of International Joint Conference on Neural Networks IEEE, pp. 1725- 1728, 1999. <br>[27] K. Wang, Computational Intelligence in Agile Manufacturing Engineering, in: Gunasekaran A, editor. Agile Manufacturing The 21st Century Competitive Strategy, Oxford, UK: Elsevier Science Ltd, 2001, pp. 297-315. <br>[28] J.E. Nash and J.V. Sutcliffe, "River flow forecasting through conceptual models Part I – a discussion of principles,” Journal of Hydrology, vol. 10, no. 3, pp. 282–290, 1970. <br>[29] S. Srinivasulu and A. Jain, "A comparative analysis of training methods for artificial neural network rainfall-runoff models,” Applied Soft Computing, vol. 6, pp. 295-306, 2006. <br>[30] J.D. Olden and D.A. Jackson, "Illuminating the "Black Box”: a randomization approach for understanding variable contributions in artificial neural networks.” Ecological Modeling ,vol. 154, pp. 135-150, 2002. <br>[31] G. Acciani, E. Chiarantoni and G. Fornarelli, A neural network approach to study O3 and PM10 concentration, in: Kollias S, Staflopatis A, Duch W, Oja E, editors. ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II, Berlin, Germany: Springer Verlag, 2006, pp. 913-922. <br>[32] G.D. Garson, "Interpreting neural network connection weights,”Artificial Intelligence Expert , vol. 6, pp. 47-51, 1991. <br>[33] M. Gevrey, I. Dimopoulos and S. Lek, "Review and comparison of methods to study the contribution of variables in artificial neural network models,” Ecological Modeling , vol. 160, pp. 249-264, 2003. <br>[34] J.J. Montano and A. Palmer, "Numeric sensitivity analysis applied to feedforward neural networks,” Neural Computing and Applications, vol. 12, pp. 119-125, 2003. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks