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Prediction and performance analysis of compression index of multiple-binder-treated soil by genetic programming approach

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Abstract

The use and its advantage in overcoming time and equipment needs of an evolutionary prediction technique known as the genetic programming have been studied using unsaturated sample of soft soil treated with multiple binders. The soil classified as weak and highly plastic was stabilized and multiple experiments were conducted to measure the effect of the dosages of the treatment on the selected properties. The geotechnics of the exercise showed that the studied parameters substantially improved with increased proportion of hybrid cement (HC) and nanostructured quarry fines (NQF). These measured selected properties were further deployed to predict the compression index of the soil. The prediction operation proposed four-model equation by the degree of importance, sensitivity and influence of the independent parameters. This shows eventually that plasticity index has the greatest sensitivity on the compression behaviour of clay soils. The performance analysis shows that the models have very low error with model trial 4 presented in Eq. 7: \(C_{C}^{GP} = \frac{{\left( { I_{P} - Hc \cdot {\text{NQF}}} \right) \left[ {\left( {\sigma_{{{\text{part}}}} /\sigma_{\max } } \right)^{{{\text{NQF}}}} } \right]^{{\left( {I_{p} /w_{\max } } \right)}} }}{{Ln\left( {w_{\max } + 3.0} \right)}}\), showing the least error with more consideration for the influence of more of the selected variables. It also exhibited the highest degree of determination. Generally, GP has proven to be flexible, fast and able to predict models for engineering problems for use in design and performance study.

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Abbreviations

HC:

Hybrid cement (%)

NQF:

Nanostructured quarry fines (%)

\(C_{C}\) :

Coefficient of curvature

\(C_{u}\) :

Coefficient of uniformity

\(\delta_{{{\text{max}}}}\) :

Maximum dry density (g/cm3)

\(w_{\max }\) :

Optimum moisture content (%)

\(\delta_{{{\text{part}}}}\) :

Partial maximum dry density (g/cm3)

\(w_{L}\) :

Liquid limit (%)

\(I_{P}\) :

Plasticity index (%)

\(C_{C}^{s}\) :

Skempton’s compression index

\(C_{C}^{GP}\) :

Genetic programming proposed compression index

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Authors and Affiliations

  1. Department of Civil Engineering, Michael Okpara University of Agric, Umudike, Nigeria

    Kennedy C. Onyelowe & Light Nwobia

  2. Department of Civil and Mechanical Engineering, Kampala International University, Kampala, Uganda

    Kennedy C. Onyelowe

  3. Department of Structural Engineering, Faculty of Engineering and Technology, Future University, Cairo, Egypt

    Ahmed M. Ebid

  4. Structure-Materials for Highway Section, Faculty of Civil Engineering, University of Transport Technology, Hanoi, Vietnam

    Lam Dao-Phuc

Authors
  1. Kennedy C. Onyelowe
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  2. Ahmed M. Ebid
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  3. Light Nwobia
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  4. Lam Dao-Phuc
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Correspondence to Kennedy C. Onyelowe.

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Appendices

Appendix 1

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Table 5 Treated and untreated soil (degree of saturation, Sr of 60%) with nine input parameters, one output parameter and 121 datasets
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5.

Appendix 2

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Table 6 Treated and untreated soil (degree of saturation, Sr of 60%) with seven input parameters, one output parameter and 121 datasets
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6.

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Onyelowe, K.C., Ebid, A.M., Nwobia, L. et al. Prediction and performance analysis of compression index of multiple-binder-treated soil by genetic programming approach. Nanotechnol. Environ. Eng. 6, 28 (2021). https://doi.org/10.1007/s41204-021-00123-2

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