Szczegóły publikacji
Opis bibliograficzny
Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: a Gaussian process regression approach / Mahmood Ahmad, Feezan Ahmad, Piotr Wróblewski, Ramez A. Al-Mansob, Piotr Olczak, Paweł KAMIŃSKI, Muhammad Safdar, Partab Rai // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2021 — vol. 11 iss. 21 art. no. 10317, s. 1–14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–14, Abstr. — Publikacja dostępna online od: 2021-11-03
Autorzy (8)
- Ahmad Mahmood
- Ahmad Feezan
- Wróblewski Piotr
- Al-Mansob Ramez A.
- Olczak Piotr
- AGHKamiński Paweł
- Safdar Muhammad
- Rai Partab
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 137551 |
|---|---|
| Data dodania do BaDAP | 2021-11-15 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app112110317 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.
