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Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN

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Abstract

The present research introduces a robust approach for predicting the maximum dry density (MDD) and optimum moisture content (OMC) of compacted soil by comparing models based on least-square support vector machine (LSSVM), long short-term memory (LSTM), least-square boost random forest (LSBoostRF), and artificial neural network (ANN) approaches. For this purpose, the database of 243 soil samples (190 training + 53 testing) has been used to train and test the developed models. For the measurement of the training and testing performance, the root-mean-square error (RMSE), mean absolute error (MAE), correlation coefficient (R), variance accounted for (VAF), performance index (PI), a20 index, index of agreement (IOA), and index of scatter (IOS), statistical parameters have been utilized. The performance comparison demonstrates that models based on the LSSVM approach have gained the highest performance in predicting MDD (RMSE = 0.0171 g/cc, R = 0.9920, MAE = 0.0125 g/cc, VAF = 98.41, PI = 1.95, a20-index = 100.00, IOA = 0.9446, and IOS = 0.0093) and OMC (RMSE = 0.6070%, R = 0.9936, MAE = 0.5294%, VAF = 98.49, PI = 1.37, a20-index = 100.00, IOA = 0.9357, and IOS = 0.0450) of compacted soil than LSTM, LSBoostRF, and ANN models. This study also reports that the Adam-optimized LSTM model performs better than the RMSProp LSTM model in predicting the compaction parameters of soil. Also, the number of leaves affects the performance of LSBoostRF models. The performance comparison of ANN models depicts that selecting suitable hyperparameters such as backpropagation algorithm, number of hidden layers, and neurons is necessary to achieve better prediction. However, Levenberg–Marquardt algorithm-based ANN model (single hidden layer interconnected with 5/10 neurons) has predicted compaction parameters better than other ANN models. The score analysis has been performed to confirm that models based on the LSSVM approach are the best architecture model for predicting MDD and OMC. The Monte Carlo sensitivity analysis demonstrates that fine content, sand content, liquid limit, and plasticity index are the most influencing parameters in predicting the compaction parameters of soil.

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Data availability

The data would remain confidential and shared by the corresponding author at the request.

Abbreviations

\({D}_{10}\) :

Particle size at 10% finer

\({D}_{U}\) :

Unit density

AI:

Artificial intelligence

ANNs:

Artificial neural networks

Apt:

Asphalt

BFG:

BFG quasi-Newton

BL:

Blended learning

C:

Clay content

CC:

Correlation coefficient

Ce:

Cement

CFB_NN:

Cascade forward backpropagation neural network

CI:

Confidence interval

Cu:

Coefficient of uniformity

DL:

Deep learning

E:

Compaction energy

ELM:

Extreme learning machines

FA:

Fly ash

FC:

Fine content

FMS:

Fineness modulus

G:

Gravel content

GD:

Gradient descent

GDA:

Gradient descent with adaptive learning

GDM:

Gradient descent with momentum

GEP:

Gene expression programming

GPs:

Gradational parameters

HL:

Hybrid learning

HLi:

Hydrated lime

Li:

Lime

LL:

Liquid limit

LM:

Levenberg–Marquardt

LS:

Linear shrinkage

LSBoostRF:

Least-square boosting random forest

LSSVM:

Least-square support vector machine

LSTM:

Long short-term memory

M:

Silt content

MAE:

Mean absolute error

MARS:

Multivariate adaptive regression splines

MDD:

Maximum dry density

MDDs:

Standard proctor test maximum dry density

ML:

Machine learning

MLP_NN:

Multilayer perceptron neural network

MRA:

Multiple regression analysis

OMC:

Optimum moisture content

OMCs:

Standard proctor test optimum moisture content

PI:

Plasticity index

PL:

Plastic limit

Pz:

Pozzolans

R :

Correlation coefficient/performance

R2 :

Coefficient of determination

RA:

Regression analysis

RBF_NN:

Radial basis function neural network

RMSE:

Root-mean-square error

S/ SC:

Sand content

SCG:

Scaled conjugate gradient

SG:

Specific gravity

SRA:

Simple regression analysis

ST:

Statistical model

ST + ANN:

Statistical-based artificial neural network

STI:

Soil type index

SVM:

Support vector machine

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

    Jitendra Khatti & Kamaldeep Singh Grover

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  1. Jitendra Khatti
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Contributions

JK is the main author and contributed to conceptualization, literature review, manuscript preparation, application of AI models, methodological development, statistical analysis, detailing, and overall analysis; KSG was involved in conceptualization, overall analysis, manuscript finalization, and detailed review and editing.

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Correspondence to Jitendra Khatti.

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Khatti, J., Grover, K.S. Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN. Innov. Infrastruct. Solut. 8, 76 (2023). https://doi.org/10.1007/s41062-023-01048-2

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