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An efficient neural network model to determine maximum swelling pressure of clayey soils
Abstract Expansive soils exhibit excessive volume increases upon contact with water, which can pose a serious threat to stability of structures and foundations. Therefore, it is essential to determine the swelling properties, e.g. maximum swelling pressure, of these problematic soils. We employed a feed-forward neural network algorithm trained with Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient, and genetic algorithm to build a network model capable of determining the maximum swelling pressure of clayey soils over a wide range of conditions. The models were developed based on a sufficiently large experimental dataset that takes into account key factors that influence the soil swelling. The results show that the feed-forward neural network algorithm trained with Bayesian regularization has the highest overall accuracy, as its predictions agree well with the experimental data. Besides, a simplified network model was developed to be used in cases of limited data availability. The developed model provides accurate predictions over a wide range of conditions and can serve as a valuable tool for researchers and engineers dealing with expansive soils.
An efficient neural network model to determine maximum swelling pressure of clayey soils
Abstract Expansive soils exhibit excessive volume increases upon contact with water, which can pose a serious threat to stability of structures and foundations. Therefore, it is essential to determine the swelling properties, e.g. maximum swelling pressure, of these problematic soils. We employed a feed-forward neural network algorithm trained with Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient, and genetic algorithm to build a network model capable of determining the maximum swelling pressure of clayey soils over a wide range of conditions. The models were developed based on a sufficiently large experimental dataset that takes into account key factors that influence the soil swelling. The results show that the feed-forward neural network algorithm trained with Bayesian regularization has the highest overall accuracy, as its predictions agree well with the experimental data. Besides, a simplified network model was developed to be used in cases of limited data availability. The developed model provides accurate predictions over a wide range of conditions and can serve as a valuable tool for researchers and engineers dealing with expansive soils.
An efficient neural network model to determine maximum swelling pressure of clayey soils
Taherdangkoo, Reza (author) / Tyurin, Vladimir (author) / Shehab, Muntasir (author) / Doulati Ardejani, Faramarz (author) / Minh Tang, Anh (author) / Narmandakh, Dulguun (author) / Butscher, Christoph (author)
2023-07-23
Article (Journal)
Electronic Resource
English
Swelling parameters of clayey soils
Online Contents | 2006
|Swelling parameters of clayey soils
British Library Online Contents | 2006
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