Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and ver...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3267018 |
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| author | Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze |
| author_facet | Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze |
| author_sort | Ahmed M. Ebid |
| collection | DOAJ |
| description | Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise. |
| format | Article |
| id | doaj-art-7292a1f4f4684580a490e95857cced07 |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-7292a1f4f4684580a490e95857cced072025-02-03T01:28:22ZengWileyJournal of Engineering2314-49122021-01-01202110.1155/2021/3267018Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) TechniquesAhmed M. Ebid0Kennedy C. Onyelowe1Emmanuel E. Arinze2Department of Structural EngineeringDepartment of Civil and Mechanical EngineeringDepartment of Civil EngineeringNumerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise.http://dx.doi.org/10.1155/2021/3267018 |
| spellingShingle | Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques Journal of Engineering |
| title | Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques |
| title_full | Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques |
| title_fullStr | Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques |
| title_full_unstemmed | Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques |
| title_short | Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques |
| title_sort | estimating the ultimate bearing capacity for strip footing near and within slopes using ai gp ann and epr techniques |
| url | http://dx.doi.org/10.1155/2021/3267018 |
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