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Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

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Abstract

This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as Bayesian regularization method (BRNN), Levenberg- Marquardt algorithm (LMNN) and differential evolution algorithm (DENN). The second technique uses the support vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε-insensitive loss function has been adopted. The inputs of both models are liquid limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content (Ce). The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Rourkela, Rourkela, 769 008, India

    Sarat Kumar Das

  2. Center for Disaster Mitigation and Management, VIT University, Vellore, 632 014, India

    Pijush Samui

  3. School of Civil Engineering, KIIT University, Bhubaneswar, 751 024, India

    Akshaya K. Sabat

Authors
  1. Sarat Kumar Das
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  2. Pijush Samui
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  3. Akshaya K. Sabat
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Correspondence to Pijush Samui.

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Das, S.K., Samui, P. & Sabat, A.K. Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil. Geotech Geol Eng 29, 329–342 (2011). https://doi.org/10.1007/s10706-010-9379-4

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  • DOI: https://doi.org/10.1007/s10706-010-9379-4

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