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Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models
https://orcid.org/http://orcid.org/0000-0003-1493-806X
California bearing ratio (CBR) and resilient modulus are critical factors for designing pavements. However, the measurement of CBR and resilient modulus of crushed waste rocks that are widely used for the construction of mine haul roads can be costly and time-consuming which is often prohibitive, especially since service lifetime of haul roads is relatively short. The recent development of machine learning techniques makes it possible to develop more efficient models, but many algorithms exist and it is not always clear which one is better for predicting CBR and resilient modulus. The main objective of this study was therefore to evaluate and compare the performance of multiple models, such as multiple linear regression (MLR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and neuroevolution of augmenting topologies (NEAT) for predicting CBR and resilient modulus of crushed waste rocks. Thirty-nine and 2320 datasets, obtained from a series of CBR and repeated load triaxial tests, were applied to develop CBR and resilient modulus models, respectively. The study of input features and hyperparameters was conducted to determine the optimal architecture of the machine learning models. A comparison study showed that RF models provided better results with coefficient of determination R2 greater than 0.9. NEAT models showed good generalizability and simple structure although their performance was lower than RF models.
Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models
https://orcid.org/http://orcid.org/0000-0003-1493-806X
California bearing ratio (CBR) and resilient modulus are critical factors for designing pavements. However, the measurement of CBR and resilient modulus of crushed waste rocks that are widely used for the construction of mine haul roads can be costly and time-consuming which is often prohibitive, especially since service lifetime of haul roads is relatively short. The recent development of machine learning techniques makes it possible to develop more efficient models, but many algorithms exist and it is not always clear which one is better for predicting CBR and resilient modulus. The main objective of this study was therefore to evaluate and compare the performance of multiple models, such as multiple linear regression (MLR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and neuroevolution of augmenting topologies (NEAT) for predicting CBR and resilient modulus of crushed waste rocks. Thirty-nine and 2320 datasets, obtained from a series of CBR and repeated load triaxial tests, were applied to develop CBR and resilient modulus models, respectively. The study of input features and hyperparameters was conducted to determine the optimal architecture of the machine learning models. A comparison study showed that RF models provided better results with coefficient of determination R2 greater than 0.9. NEAT models showed good generalizability and simple structure although their performance was lower than RF models.
Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models
https://orcid.org/http://orcid.org/0000-0003-1493-806X
California bearing ratio (CBR) and resilient modulus are critical factors for designing pavements. However, the measurement of CBR and resilient modulus of crushed waste rocks that are widely used for the construction of mine haul roads can be costly and time-consuming which is often prohibitive, especially since service lifetime of haul roads is relatively short. The recent development of machine learning techniques makes it possible to develop more efficient models, but many algorithms exist and it is not always clear which one is better for predicting CBR and resilient modulus. The main objective of this study was therefore to evaluate and compare the performance of multiple models, such as multiple linear regression (MLR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and neuroevolution of augmenting topologies (NEAT) for predicting CBR and resilient modulus of crushed waste rocks. Thirty-nine and 2320 datasets, obtained from a series of CBR and repeated load triaxial tests, were applied to develop CBR and resilient modulus models, respectively. The study of input features and hyperparameters was conducted to determine the optimal architecture of the machine learning models. A comparison study showed that RF models provided better results with coefficient of determination R2 greater than 0.9. NEAT models showed good generalizability and simple structure although their performance was lower than RF models.
Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models
Acta Geotech.
Hao, Shengpeng (author) / Pabst, Thomas (author)
Acta Geotechnica ; 17 ; 1383-1402
2022-04-01
20 pages
Article (Journal)
Electronic Resource
English
CBR , Crushed waste rocks , Machine learning , Neuroevolution of augmenting topologies , Random forest , Resilient modulus Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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