Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques
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...
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2021-07-01
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doaj-df82c378af9f48509275ee73e1131cd52021-07-15T15:30:53ZengMDPI AGApplied Sciences2076-34172021-07-01116167616710.3390/app11136167Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning TechniquesMahmood Ahmad0Paweł Kamiński1Piotr Olczak2Muhammad Alam3Muhammad Junaid Iqbal4Feezan Ahmad5Sasui Sasui6Beenish Jehan Khan7Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, PakistanFaculty of Mining and Geoengineering, AGH University of Science and Technology, Mickiewicza 30 Av., 30-059 Kraków, PolandMineral and Energy Economy Research Institute, Polish Academy of Sciences, 7A Wybickiego St., 31-261 Cracow, PolandDepartment of Civil Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, PakistanState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaDepartment of Architectural Engineering, Chungnam National University, Daejeon 34134, KoreaDepartment of Civil Engineering, CECOS University of IT and Emerging Sciences, Peshawar 25000, PakistanSupervised 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 (R<sup>2</sup>), 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 R<sup>2</sup> together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R<sup>2</sup> = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R<sup>2</sup> = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R<sup>2</sup> = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R<sup>2</sup> = 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.https://www.mdpi.com/2076-3417/11/13/6167AdaBoostsupport vector machinek-nearest neighborrandom forestrockfill materialsshear strength |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Mahmood Ahmad Paweł Kamiński Piotr Olczak Muhammad Alam Muhammad Junaid Iqbal Feezan Ahmad Sasui Sasui Beenish Jehan Khan |
| spellingShingle |
Mahmood Ahmad Paweł Kamiński Piotr Olczak Muhammad Alam Muhammad Junaid Iqbal Feezan Ahmad Sasui Sasui Beenish Jehan Khan Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques Applied Sciences AdaBoost support vector machine k-nearest neighbor random forest rockfill materials shear strength |
| author_facet |
Mahmood Ahmad Paweł Kamiński Piotr Olczak Muhammad Alam Muhammad Junaid Iqbal Feezan Ahmad Sasui Sasui Beenish Jehan Khan |
| author_sort |
Mahmood Ahmad |
| title |
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques |
| title_short |
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques |
| title_full |
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques |
| title_fullStr |
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques |
| title_full_unstemmed |
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques |
| title_sort |
development of prediction models for shear strength of rockfill material using machine learning techniques |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2021-07-01 |
| description |
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 (R<sup>2</sup>), 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 R<sup>2</sup> together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R<sup>2</sup> = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R<sup>2</sup> = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R<sup>2</sup> = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R<sup>2</sup> = 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. |
| topic |
AdaBoost support vector machine k-nearest neighbor random forest rockfill materials shear strength |
| url |
https://www.mdpi.com/2076-3417/11/13/6167 |
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