Szczegóły publikacji
Opis bibliograficzny
Development of prediction models for shear strength of rockfill material using machine learning techniques / Mahmood Ahmad, Paweł KAMIŃSKI, Piotr Olczak, Muhammad Alam, Muhammad Junaid Iqbal, Feezan Ahmad, Sasui Sasui, Beenish Jehan Khan // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2021 — vol. 11 iss. 13 art. no. 6167, s. 1–22. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 20–22, Abstr. — Publikacja dostępna online od: 2021-07-02
Autorzy (8)
- Ahmad Mahmood
- AGHKamiński Paweł
- Olczak Piotr
- Alam Muhammad
- Iqbal Muhammad Junaid
- Ahmad Feezan
- Sasui Sasui
- Khan Beenish Jehan
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135050 |
|---|---|
| Data dodania do BaDAP | 2021-07-07 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app11136167 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R2 together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R2 = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R2 = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R2 = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM.
