A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An efficient neural network model to determine maximum swelling pressure of clayey soils
https://orcid.org/0000-0002-0290-8106
https://orcid.org/0000-0003-2233-0003
https://orcid.org/0009-0005-4448-8698
https://orcid.org/0000-0002-5443-2349
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
https://orcid.org/0000-0002-0290-8106
https://orcid.org/0000-0003-2233-0003
https://orcid.org/0009-0005-4448-8698
https://orcid.org/0000-0002-5443-2349
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
https://orcid.org/0000-0002-0290-8106
https://orcid.org/0000-0003-2233-0003
https://orcid.org/0009-0005-4448-8698
https://orcid.org/0000-0002-5443-2349
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