Prediction of bearing capacity of thin-walled foundation: a simulation approach
In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a...
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my.utm.856552020-07-07T05:16:22Z http://eprints.utm.my/id/eprint/85655/ Prediction of bearing capacity of thin-walled foundation: a simulation approach Momeni, Ehsan Armaghani, Danial Jahed Fatemi, Seyed Alireza Nazir, Ramli TA Engineering (General). Civil engineering (General) In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a data set comprising nearly 150 recorded cases of footing load tests was compiled from literature. Footing width, wall length-to-footing width ratio, internal friction angle, and unit weight of soil were set as inputs of the predictive model of bearing capacity. In addition, a pre-developed artificial neural network (ANN) model was utilized to estimate the bearing capacity of thin-walled foundations. The results recommend the workability of ANFIS in predicting the bearing capacity of thin-walled foundation. The coefficient of determination (R2) results of 0.933 and 0.875, and root mean square error (RMSE) results of 0.075 and 0.048 for training and testing data sets show higher accuracy and efficiency level of ANFIS in estimating bearing capacity of thin-walled spread foundations compared to the ANN model (R2 = 0.710, RMSE = 0.512 for train, R2 = 0.420, RMSE = 0.529 for test). Overall, findings of the study suggest utilization of ANFIS, as a feasible and quick tool, for predicting the bearing capacity of thin-walled spread foundations, though further study is still recommended to enhance the reliability of the proposed model. Springer London 2018-04 Article PeerReviewed Momeni, Ehsan and Armaghani, Danial Jahed and Fatemi, Seyed Alireza and Nazir, Ramli (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Engineering with Computers, 34 (2). pp. 319-327. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-017-0542-x |
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TA Engineering (General). Civil engineering (General) Momeni, Ehsan Armaghani, Danial Jahed Fatemi, Seyed Alireza Nazir, Ramli Prediction of bearing capacity of thin-walled foundation: a simulation approach |
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In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a data set comprising nearly 150 recorded cases of footing load tests was compiled from literature. Footing width, wall length-to-footing width ratio, internal friction angle, and unit weight of soil were set as inputs of the predictive model of bearing capacity. In addition, a pre-developed artificial neural network (ANN) model was utilized to estimate the bearing capacity of thin-walled foundations. The results recommend the workability of ANFIS in predicting the bearing capacity of thin-walled foundation. The coefficient of determination (R2) results of 0.933 and 0.875, and root mean square error (RMSE) results of 0.075 and 0.048 for training and testing data sets show higher accuracy and efficiency level of ANFIS in estimating bearing capacity of thin-walled spread foundations compared to the ANN model (R2 = 0.710, RMSE = 0.512 for train, R2 = 0.420, RMSE = 0.529 for test). Overall, findings of the study suggest utilization of ANFIS, as a feasible and quick tool, for predicting the bearing capacity of thin-walled spread foundations, though further study is still recommended to enhance the reliability of the proposed model. |
| format |
Article |
| author |
Momeni, Ehsan Armaghani, Danial Jahed Fatemi, Seyed Alireza Nazir, Ramli |
| author_facet |
Momeni, Ehsan Armaghani, Danial Jahed Fatemi, Seyed Alireza Nazir, Ramli |
| author_sort |
Momeni, Ehsan |
| title |
Prediction of bearing capacity of thin-walled foundation: a simulation approach |
| title_short |
Prediction of bearing capacity of thin-walled foundation: a simulation approach |
| title_full |
Prediction of bearing capacity of thin-walled foundation: a simulation approach |
| title_fullStr |
Prediction of bearing capacity of thin-walled foundation: a simulation approach |
| title_full_unstemmed |
Prediction of bearing capacity of thin-walled foundation: a simulation approach |
| title_sort |
prediction of bearing capacity of thin-walled foundation: a simulation approach |
| publisher |
Springer London |
| publishDate |
2018 |
| url |
http://eprints.utm.my/id/eprint/85655/ http://dx.doi.org/10.1007/s00366-017-0542-x |
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1672610565043781632 |
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13.211869 |