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Compressive Strength Prediction of Aluminosilicate Precursors Based Geopolymers Through Artificial Neural Network (ANN)

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Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—Structure and Construction Management

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 221))

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Abstract

The need for the present decade is an environmentally friendly approach to the utilization of building materials. Geopolymers which are produced from 100% aluminosilicate-based waste materials have the potential to pave the path for sustainable development and construction practices. But many factors affect the compressive strength of geopolymers that needs to be evaluated before its utilization in any field practices. Hence the purpose of this article is to formulate a model by employing Artificial Neural Network (ANN) for prediction of compressive strength. Seven input parameters were taken into consideration which influences the compressive strength of geopolymers, to develop four different ANN models. A total of 396 sets of data were analyzed in training, validation, and testing phases to estimate the geopolymeric potential of different aluminosilicate-based precursors. The best performing model with an R2 value of 0.9994 is Model—4 which contains two hidden layers with 14 artificial neurons. Finally, ANN showed strong potential in predicting the compressive strength of geopolymers within the considered range.

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Authors and Affiliations

  1. Department of Civil Engineering, Manipal University Jaipur, Jaipur, India

    Sourav Kumar Das

  2. Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Sourav Kumar Das & Sandeep Shrivastava

Authors
  1. Sourav Kumar Das
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  2. Sandeep Shrivastava
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Corresponding author

Correspondence to Sandeep Shrivastava .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Karnataka, Surathkal, India

    B. B. Das

  2. Department of Construction Management, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Christy P. Gomez

  3. School of Civil Engineering, KIIT Deemed University, Bhubaneswar, India

    Benu. G. Mohapatra

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Das, S.K., Shrivastava, S. (2022). Compressive Strength Prediction of Aluminosilicate Precursors Based Geopolymers Through Artificial Neural Network (ANN). In: Das, B.B., Gomez, C.P., Mohapatra, B.G. (eds) Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—Structure and Construction Management. Lecture Notes in Civil Engineering, vol 221. Springer, Singapore. https://doi.org/10.1007/978-981-16-8433-3_3

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  • DOI: https://doi.org/10.1007/978-981-16-8433-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8432-6

  • Online ISBN: 978-981-16-8433-3

  • eBook Packages: EngineeringEngineering (R0)

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