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Assessment of fine-grained soil compaction parameters using advanced soft computing techniques

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Abstract

The compaction parameters are the most important parameters for any civil engineering project. In this work, the sand content, fine content, liquid limit, plastic limit, and plasticity index were used as input parameters by the soft computing models to predict the compaction parameters of soil. These soft computing models are gene expression programming (GEP), least square support vector machine (LSSVM), long short-term memory (LSTM), and artificial neural network (ANNs). For this purpose, three databases, i.e., training, testing, and validation, are prepared from the database available in the literature. Also, twelve soil samples are collected from and around Kota, Rajasthan, and tested in a laboratory for cross-validation of the best architecture models. The performance of models is measured by thirteen performance indicators, including three new indicators, i.e., a20-index, index of agreement (IOA), and index of scattering (IOS). The test performance comparison reveals that the polynomial LSSVM model MD15 (a20-index = 100.00%, IOA = 0.9371, IOS = 0.0519) and linear LSSVM model MD110 (a20-index = 100.00%, IOA = 0.9179, IOS = 0.0122) have the highest performance in predicting optimum moisture content (OMC) and maximum dry density (MDD), respectively. Also, models MD15 and MD110 have higher performance in the validation phase. Models MD15 and MD110 have predicted OMC and MDD of twelve soil samples with residuals of ± 1.776% and ± 0.044 g/cc, respectively. This study demonstrates ANN achieves high overfitting than the LSTM model in predicting the compaction parameters of soil. The LSSVM model represents overfitting in predicting OMC and underfitting in predicting MDD of soil. Finally, the present research introduces high-performance soft computing models for predicting the compaction parameters of fine-grained soil.

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Data availability

All data, models, and code generated or used during the study appear in the submitted article. The database may be provided on request.

Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural networks

ASTM:

American Society for Testing and Materials

C.L.:

Confidence level

CC/ R:

Correlation coefficient

CH:

Inorganic clays of high plasticity

CI:

Inorganic clays of medium plasticity

CL:

Inorganic clays of low plasticity

D30 :

particle size at 30% finer

D50 :

particle size at 50% finer

FC:

Fine content

GEP:

Gene expression programming

GMDH:

Group method of data handling

IS:

Indian Standards

LL:

Liquid limit

LSSVM:

Least-squares support vector machine

LSTM:

Long short-term memory

MAE:

Mean absolute error

MARS:

Multivariate adaptive regression splines

MD:

Model

MDD:

Maximum dry density

MEP:

Multiple expression programming

MH:

Inorganic silts of high compressibility

MI:

Inorganic silts of medium plasticity

ML:

Inorganic silts with none to low plasticity

MLR:

Multi-linear regression

MPT:

Modified proctor test

OH:

Organic clays of medium to high plasticity

OI:

Organic silts of medium plasticity

OL:

Organic silts of low plasticity

OMC:

Optimum moisture content

PI:

Plasticity index

PL:

Plastic limit

RMSE:

Root mean square error

S:

Sand content

SM:

Supplementary materials

SPT:

Standard proctor test

St.Dev.:

Standard deviation

SVM:

Support vector machine

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, Rajasthan Technical University, Kota, 324010, Rajasthan, India

    Jitendra Khatti & Kamaldeep Singh Grover

Authors
  1. Jitendra Khatti
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  2. Kamaldeep Singh Grover
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Contributions

Jitendra Khatti: Main author, conceptualization, literature review, manuscript preparation, application of AI models, methodological development, statistical analysis, detailing, and overall analysis; Kamaldeep Singh Grover: Conceptualization, overall analysis, manuscript finalization, detailed review, and editing;

Corresponding author

Correspondence to Jitendra Khatti.

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Conflict of interest

The authors declare that they have no competing interests.

Additional information

Responsible Editor: Zeynal Abiddin Erguler

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1675 KB)

Appendices

Appendix 1

Table 10 Results of sensitivity analysis for OMC
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Table 11 Results of sensitivity analysis for MDD
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Appendix 2

Table 12 Training and Testing Performance of GEP, LSSVM, LSTM, and ANN models in assessing soil OMC
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Table 13 Training and Testing Performance of GEP, LSSVM, LSTM, and ANN models in assessing soil MDD (Conti…)
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Appendix 3

Table 14 Details of validation performance of better-performing models in predicting each OMC and MDD of soil
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Appendix 4

Table 15 Score analysis for better-performing models of OMC prediction
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Table 16 Score analysis for better-performing models of MDD prediction
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Appendix 5

Table 17 Prediction of OMC and MDD of soil (validation database) using the best architecture model and published models
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Khatti, J., Grover, K.S. Assessment of fine-grained soil compaction parameters using advanced soft computing techniques. Arab J Geosci 16, 208 (2023). https://doi.org/10.1007/s12517-023-11268-6

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  • DOI: https://doi.org/10.1007/s12517-023-11268-6

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