Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques

• Published in: Applied sciences Vol. 11; no. 13; p. 6167
• Main Authors: Ahmad, Mahmood, Kamiński, Paweł, Olczak, Piotr, Alam, Muhammad, Iqbal, Muhammad Junaid, Ahmad, Feezan, Sasui, Sasui, Khan, Beenish Jehan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2021
• Subjects: AdaBoost; Algorithms; Civil engineering; Datasets; Decision trees; k-nearest neighbor; Laboratories; Learning algorithms; Machine learning; Material properties; Mechanical properties; Parameter sensitivity; Particle size; Performance evaluation; Prediction models; random forest; Rockfill; rockfill materials; Root-mean-square errors; Sensitivity analysis; Shear strength; Shear tests; Standard deviation; Statistical analysis; support vector machine; Support vector machines
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11136167

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.