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Prediction of Compressive Strength of Geopolymers Using Multi-objective Feature Selection

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Big Data in Engineering Applications

Part of the book series: Studies in Big Data ((SBD,volume 44))

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Abstract

To reduce the carbon dioxide emission to the environment, production of geopolymer is one of the effective binding materials to act as a substitute of cement. The strength of the geopolymer depends upon different factors such as chemical constituents, curing temperature, curing time, super plasticizer etc. In this paper, prediction models for compressive strength of geopolymer is presented using recently developed artificial intelligence techniques; multi-objective feature selection (MOFS), functional network (FN), multivariate adaptive regression spline (MARS) and multi gene genetic programming (MGGP). The MOFS is also used to find the subset of influential parameters responsible for the compressive strength of geopolymers. MOFS has been applied with artificial neural network (ANN) and non-dominated sorting genetic algorithm (NSGA II). The parameters considered for development of prediction models are curing time, NaOH concentration, Ca(OH)2 content, superplasticizer content, types of mold, types of geopolymer and H2O/Na2O molar ratio. The developed AI models were compared in terms of different statistical parameters such as average absolute error, root mean square error correlation coefficient, Nash-Sutcliff coefficient of efficiency.

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Authors and Affiliations

  1. Civil Engineering Department, National Institute of Technology, Rourkela, India

    Lasyamayee Garanayak & Ranajeet Mohanty

  2. Civil Engineering Department, Indian Institute of Technology (ISM), Dhanbad, India

    Sarat Kumar Das

Authors
  1. Lasyamayee Garanayak
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  3. Ranajeet Mohanty
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Correspondence to Lasyamayee Garanayak .

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Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

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Garanayak, L., Das, S.K., Mohanty, R. (2018). Prediction of Compressive Strength of Geopolymers Using Multi-objective Feature Selection. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_16

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  • DOI: https://doi.org/10.1007/978-981-10-8476-8_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

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