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

Zhao, Liuming; Wilson, Shane B.; Van Thieu, Nguyen; Zhou, Jian; Romulus, Costache; Tran, Trung Tin

doi:10.1007/s11600-023-01194-2

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Zhao Liuming , Wilson Shane B. , Van Thieu Nguyen , Zhou Jian , Romulus Costache , Tran Trung Tin

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01194-2

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

clay compressibility geotechnical engineering metaheuristic algorithms predictive modeling soft ground improvement extreme learning machine

ściśliwość gliny inżynieria geotechniczna algorytmy metaheurystyczne modelowanie predykcyjne maszyna do ekstremalnego uczenia się

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 2

Strony

579--595

Opis fizyczny

Bibliogr. 69 poz.

Twórcy

autor

Zhao Liuming

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

autor

Wilson Shane B.

shaneb.wilson2685@gmail.com

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

autor

Van Thieu Nguyen

thieu.nguyenvan@phenikaa-uni.edu.vn

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

autor

Zhou Jian

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

autor

Romulus Costache

National Institute of Hydrology and Water Management, Bucharest, Romania
Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania
Danube Delta National Institute for Research and Development, Tulcea, Romania

autor

Tran Trung Tin

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

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Bibliografia

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