ArticleScientific reports2024
Predicting bentonite swelling pressure: optimized XGBoost versus neural networks.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- On the interpretability of machine and deep learning techniques for predicting CBR of stabilized soil containing agro-industrial wastes.Scientific reports · 2026Article
- USSGAN: Unsupervised Spectral and Spatial Attention-Based Generative Adversarial Network for Cholangiocarcinoma Detection.Chemical & biomedical imaging · 2025Article
- Nurse Staffing Management in the Context of Emergency Departments and Seasonal Respiratory Diseases: An Artificial Intelligence and Discrete-Event Simulation Approach.Journal of medical systems · 2025Article
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- Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction.Scientific reports · 2025Article
- Study on coal and gas outburst prediction technology based on multi-model fusion.Frontiers in big data · 2025Article
- Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis.Frontiers in veterinary science · 2025Article
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Authors and funding
2 authors.
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Abstract
The swelling pressure of bentonite and bentonite mixtures is critical in designing barrier systems for deep geological radioactive waste repositories. Accurately predicting the maximum swelling pressure is essential for ensuring these systems' long-term stability and sealing characteristics. In this study, we developed a constrained machine learning model based on the extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. A dataset containing 305 experimental data points was compiled, including relevant soil properties such as montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density. The GWO-XGBoost model, incorporating a penalty term in the loss function, achieved an R
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.