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Frontiers of Structural and Civil Engineering

, Volume 13, Issue 1, pp 215–239 | Cite as

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

  • Ali Reza GhanizadehEmail author
  • Hakime Abbaslou
  • Amir Tavana Amlashi
  • Pourya Alidoust
Research Article
  • 42 Downloads

Abstract

Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

Keywords

bentonite/sepiolite plastic concrete compressive strength artificial neural network support vector machine parametric analysis 

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ali Reza Ghanizadeh
    • 1
    Email author
  • Hakime Abbaslou
    • 1
  • Amir Tavana Amlashi
    • 1
  • Pourya Alidoust
    • 2
  1. 1.Department of Civil EngineeringSirjan University of TechnologySirjanIran
  2. 2.Department of Civil EngineeringIran University of Science & TechnologyTehranIran

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