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Employing multi-layer perceptron model via meta-heuristic algorithms for predicting California bearing capacity of stabilized soil

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Abstract

The California bearing ratio (CBR) value is a pivotal soil characteristic for designing flexible pavements and airport runways. Additionally, it can be harnessed to ascertain the subgrade's soil reaction through correlation. This parameter is paramount in soil engineering, particularly in formulating the subgrade design for rural road networks. The CBR value of soil is subject to a multitude of influencing factors, including but not limited to maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), soil type, and soil permeability. Furthermore, whether the soil is soaked or unsoaked also impacts this value. The process of CBR determination is notably protracted and demands a considerable amount of time. Recognizing the significance of this determination, the study introduces an innovative machine-learning approach. This novel method employs a multi-layer perceptron as its foundational model, harnessing the formidable capabilities of this algorithm in addressing regression challenges. To elevate the performance of the MLP and attain optimal outcomes, a hybridization approach has been employed, integrating the Bonobo Optimizer (BO), Smell Agent Optimization (SAO), and Dynamic Control Cuckoo Search (DCCS). The hybrid models proposed in this study showcase encouraging outcomes in CBR value prediction. Notably, the MLAO3 hybrid model emerges as the most precise predictor among the various models, achieving an impressive R2 value of 0.994 and an RMSE value of 2.80.

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Abbreviations

\(\mathrm{CBR}\) :

California bearing ratio

\(\mathrm{OMC}\) :

Optimum moisture content

CP:

Curing period

DCCS:

Dynamic control cuckoo search

BO:

Bonobo optimizer

MDAPE:

Median absolute percentage error

WAPE:

Weighted absolute percentage error

LI:

Lime percentage

\(\mathrm{MDD}\) :

Maximum dry density

MLP:

Multi-layer perceptron

SAO:

Smell agent optimization

R 2 :

Coefficient of determination

MSE:

Mean squared error

RMSE:

Root mean squared error

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Acknowledgements

This work was supported by "the Key project of teaching and research planning of Anhui Vocational and Adult Education Association, Anhui China(No.azcg44)", "key Research Project of Social Sciences in Anhui Universities, Anhui China(No.2022AH053106) ", "Anhui Provincial Department of Education University Quality Project, Anhui China ( No.2022jpkc041).

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  1. Department of Information Technology, Anhui Vocational College of Grain Engineering, Hefei, 230011, Anhui, China

    Lulu Zhang

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LZ: writing-original draft preparation, conceptualization, supervision, project administration.

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Zhang, L. Employing multi-layer perceptron model via meta-heuristic algorithms for predicting California bearing capacity of stabilized soil. Multiscale and Multidiscip. Model. Exp. and Des. 7, 1375–1391 (2024). https://doi.org/10.1007/s41939-023-00277-3

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