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A new intelligence model for evaluating clay compressibility in soft ground improvement: a combined approach of bees optimization and extreme learning machine

  • Research Article - Solid Earth Sciences
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Abstract

This study investigated the compressibility of clay (Cc) for soft ground improvement and developed six optimized metaheuristic-based extreme learning machine (ELM) models (particle swarm optimization (PSO)-ELM, moth search optimization (MSO)-ELM, firefly optimization (FO)-ELM, cuckoo search optimization (CSO)-ELM, bees optimization (BO)-ELM, and ant colony optimization (ACO)-ELM) to predict Cc. A total of 739 laboratory tests were conducted to develop the models, and 517 datasets were used for training, while the remaining 222 samples were used for testing. The results showed that the accuracy of the developed models was improved by 3–5% compared to the original ELM model. The BO-ELM and MSO-ELM models were identified as the most effective models for predicting Cc, with accuracies ranging from 86.5% to 87%. The study suggests that the MSO-ELM model should be used if training time is critical. The developed models provide useful tools for predicting Cc, an essential parameter for soft ground improvement design, and can assist in the improvement of soft ground.

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Abbreviations

C c :

Compressibility of clay

ELM:

Extreme learning machine

PSO:

Particle swarm optimization

MSO:

Moth search optimization

FO:

Firefly optimization

CSO:

Cuckoo search optimization

BO:

Bees optimization

ACO:

Ant colony optimization

wn :

Natural water content

eo :

Initial void ratio

wLL :

Liquid limit

Gs :

Specific gravity

GA:

Genetic algorithm

ANN:

Artificial neural network

GWO-CFNN:

Grey wolf optimization-cascade forward neural network

GMDH:

Group method of data handling

AI:

Artificial intelligence

ABC:

Artificial bee colony

PI :

Plasticity index

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Authors and Affiliations

  1. Department of Mathematics Education, Zhumadian Preschool Education College, Zhumadian, 463000, China

    Liuming Zhao

  2. Artificial Intelligence in Engineering Innovation Research Group (AI-EngrInnovate), Toronto, Canada

    Shane B. Wilson

  3. Faculty of Computer Science, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam

    Nguyen Van Thieu

  4. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou

  5. National Institute of Hydrology and Water Management, Bucharest, Romania

    Costache Romulus

  6. Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania

    Costache Romulus

  7. Danube Delta National Institute for Research and Development, Tulcea, Romania

    Costache Romulus

  8. Department of Information Technology, Swinburne Vietnam - FPT University, Da Nang, Vietnam

    Trung Tin Tran

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  1. Liuming Zhao
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  2. Shane B. Wilson
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  3. Nguyen Van Thieu
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  4. Jian Zhou
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Correspondence to Shane B. Wilson.

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Edited by Prof. Fengqiang Gong (ASSOCIATE EDITOR) / Prof. Ramón Zúñiga (CO-EDITOR-IN-CHIEF).

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Zhao, L., Wilson, S.B., Van Thieu, N. et al. A new intelligence model for evaluating clay compressibility in soft ground improvement: a combined approach of bees optimization and extreme learning machine. Acta Geophys. 72, 579–595 (2024). https://doi.org/10.1007/s11600-023-01194-2

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