Szczegóły publikacji
Opis bibliograficzny
Prediction of liquefaction-Induced lateral displacements using Gaussian process regression / Mahmood Ahmad, Maaz Amjad, Ramez A. Al-Mansob, Paweł KAMIŃSKI, Piotr Olczak, Beenish Jehan Khan, Arnold C. Alguno // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2022 — vol. 12 iss. 4 art. no. 1977, s. 1–17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 15–17, Abstr. — Publikacja dostępna online od: 2022-02-14
Autorzy (7)
- Ahmad Mahmood
- Amjad Maaz
- Al-Mansob Ramez A.
- AGHKamiński Paweł
- Olczak Piotr
- Khan Beenish Jehan
- Alguno Arnold C.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139212 |
|---|---|
| Data dodania do BaDAP | 2022-02-23 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app12041977 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement.