Hybrid Formulation of Resilient Modulus for Cohesive Subgrade Soils Utilizing CPT Test Parameters
AbstractIn the present study, a novel model is introduced for the prediction of a resilient modulus (MR) of cohesive subgrade soils considering cone-penetration test parameters to establish correlations with the MR. A reliable previously published database composed of 124 datasets was utilized for t...
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| Veröffentlicht in: | Journal of materials in civil engineering 2020-09, Vol.32 (9) |
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| container_title | Journal of materials in civil engineering |
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| creator | Ghorbani, Behnam Arulrajah, Arul Narsilio, Guillermo Horpibulsuk, Suksun Bo, Myint Win |
| description | AbstractIn the present study, a novel model is introduced for the prediction of a resilient modulus (MR) of cohesive subgrade soils considering cone-penetration test parameters to establish correlations with the MR. A reliable previously published database composed of 124 datasets was utilized for the development of the proposed model, which incorporates both cone penetration test (CPT) parameters and laboratory indices. In order to generate the predictive model, a hybrid algorithm combining a firefly algorithm with a multilayer perceptron neural network (FA-MLP) is proposed. The FA algorithm is employed in the MLP network structure to adjust the weights and the bias of the network and, hence, improve the overall performance of the network. The proposed FA-MLP formulation was found to have the capacity to predict, satisfactorily, the MR of cohesive subgrade soils using the results of the CPT test. |
| doi_str_mv | 10.1061/(ASCE)MT.1943-5533.0003329 |
| format | Article |
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A reliable previously published database composed of 124 datasets was utilized for the development of the proposed model, which incorporates both cone penetration test (CPT) parameters and laboratory indices. In order to generate the predictive model, a hybrid algorithm combining a firefly algorithm with a multilayer perceptron neural network (FA-MLP) is proposed. The FA algorithm is employed in the MLP network structure to adjust the weights and the bias of the network and, hence, improve the overall performance of the network. 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A reliable previously published database composed of 124 datasets was utilized for the development of the proposed model, which incorporates both cone penetration test (CPT) parameters and laboratory indices. In order to generate the predictive model, a hybrid algorithm combining a firefly algorithm with a multilayer perceptron neural network (FA-MLP) is proposed. The FA algorithm is employed in the MLP network structure to adjust the weights and the bias of the network and, hence, improve the overall performance of the network. 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| language | eng |
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| source | American Society of Civil Engineers:NESLI2:Journals:2014 |
| subjects | Algorithms Building materials Civil engineering Cohesion Cone penetration tests Heuristic methods Mathematical models Multilayer perceptrons Neural networks Parameters Penetration Prediction models Soils Technical Note Technical Notes |
| title | Hybrid Formulation of Resilient Modulus for Cohesive Subgrade Soils Utilizing CPT Test Parameters |
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