Development of Models for Predicting California Bearing Ratio of Lateritic Soil Using Selected Soft Computing Techniques

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dc.contributor.authorF.E Eze
dc.contributor.authorT.E Adejumo
dc.contributor.authorA. A Amadi
dc.contributor.authorYusuf A
dc.date.accessioned2024-11-04T10:19:22Z
dc.date.available2024-11-04T10:19:22Z
dc.date.issued2024-10-28
dc.description.abstractSoft computing approaches were used to create models that estimate the California bearing ratio values of lateritic soil. Soft computing techniques are algorithms that find provably correct and optimal solutions to problems. The Soaked CBR values used in pavement design take about 96 hours to complete the test process. This can be time-consuming and expensive, Hence the need for researchers to seek alternate means of obtaining it. Various studies have employed artificial intelligence techniques, including neural networks, genetic algorithms, and support vector machines, to estimate CBR values. While these approaches offer potential benefits, they also exhibit certain drawbacks, such as sensitivity to parameter settings, restricted adaptability, and difficulty in understanding the underlying relationships. This study proposes a new model to address this challenge, Artificial Neural Networks (ANN) and its hybrid (ANFIS) were considered. Soil samples were taken from a burrow pit, and the necessary testing was performed on the acquired soil samples. Index, compaction, and California bearing ratio tests were conducted. Two machine learning models, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), were developed to predict CBR values of lateritic soil. The models were trained on 70% of the data and tested on the remaining 30%. Both models demonstrated satisfactory performance, but the ANFIS model exhibited superior accuracy, as evidenced by a higher R2 value (0.98), lower RMSE (0.11), and lower MSE (0.33). These results suggest that ANFIS is particularly effective in capturing complex relationships within the data and is a promising tool for predicting CBR values in lateritic soils.
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13016
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleDevelopment of Models for Predicting California Bearing Ratio of Lateritic Soil Using Selected Soft Computing Techniques
dc.typeArticle
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