A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes
https://orcid.org/http://orcid.org/0000-0003-4769-4487
During excavation of roadheader, specific energy (SE) is a key component of rock cuttability evaluation and cutting head design. Previous studies have shown that the specific energy is simultaneously affected by physical and mechanical parameters of rock, pick geometry, and pick operation parameters. In the paper, six machine learning (ML) algorithms (back-propagation neural network, Elman neural network, extreme learning machine, kernel extreme learning machine, random forest, support vector regression) optimized by sparrow search algorithm (SSA) for SE prediction are developed by simultaneously considering two rock mechanical parameters (tensile strength of the rock σt and uniaxial compressive strength of the rock σc), one pick geometry (cone angle θ) and five pick operation parameters (cutting depth d, tool spacing s, rake angle α, attack angle γ, back-clearance angle β). 213 rock samples containing 26 rock types were selected to build the SSA-ML model. Mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R2) between the measured and predicted values are assigned as evaluation indicators to compare prediction performance of SSA-ML models. The importance of input variables is calculated internally using random forest (RF) algorithm. The results indicated that SSA-RF model with MAE of (0.7938 and 1.0438), MAPE of (12.76% and 16.98%), R2 of (0.9632 and 0.8943) on the training set and testing set has the most potential for SE prediction. The sensitive analysis shows the d, σc and σt are the most significant input variables for SE prediction.
Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes
https://orcid.org/http://orcid.org/0000-0003-4769-4487
During excavation of roadheader, specific energy (SE) is a key component of rock cuttability evaluation and cutting head design. Previous studies have shown that the specific energy is simultaneously affected by physical and mechanical parameters of rock, pick geometry, and pick operation parameters. In the paper, six machine learning (ML) algorithms (back-propagation neural network, Elman neural network, extreme learning machine, kernel extreme learning machine, random forest, support vector regression) optimized by sparrow search algorithm (SSA) for SE prediction are developed by simultaneously considering two rock mechanical parameters (tensile strength of the rock σt and uniaxial compressive strength of the rock σc), one pick geometry (cone angle θ) and five pick operation parameters (cutting depth d, tool spacing s, rake angle α, attack angle γ, back-clearance angle β). 213 rock samples containing 26 rock types were selected to build the SSA-ML model. Mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R2) between the measured and predicted values are assigned as evaluation indicators to compare prediction performance of SSA-ML models. The importance of input variables is calculated internally using random forest (RF) algorithm. The results indicated that SSA-RF model with MAE of (0.7938 and 1.0438), MAPE of (12.76% and 16.98%), R2 of (0.9632 and 0.8943) on the training set and testing set has the most potential for SE prediction. The sensitive analysis shows the d, σc and σt are the most significant input variables for SE prediction.
Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes
https://orcid.org/http://orcid.org/0000-0003-4769-4487
During excavation of roadheader, specific energy (SE) is a key component of rock cuttability evaluation and cutting head design. Previous studies have shown that the specific energy is simultaneously affected by physical and mechanical parameters of rock, pick geometry, and pick operation parameters. In the paper, six machine learning (ML) algorithms (back-propagation neural network, Elman neural network, extreme learning machine, kernel extreme learning machine, random forest, support vector regression) optimized by sparrow search algorithm (SSA) for SE prediction are developed by simultaneously considering two rock mechanical parameters (tensile strength of the rock σt and uniaxial compressive strength of the rock σc), one pick geometry (cone angle θ) and five pick operation parameters (cutting depth d, tool spacing s, rake angle α, attack angle γ, back-clearance angle β). 213 rock samples containing 26 rock types were selected to build the SSA-ML model. Mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R2) between the measured and predicted values are assigned as evaluation indicators to compare prediction performance of SSA-ML models. The importance of input variables is calculated internally using random forest (RF) algorithm. The results indicated that SSA-RF model with MAE of (0.7938 and 1.0438), MAPE of (12.76% and 16.98%), R2 of (0.9632 and 0.8943) on the training set and testing set has the most potential for SE prediction. The sensitive analysis shows the d, σc and σt are the most significant input variables for SE prediction.
Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes
Acta Geotech.
Zhou, Jian (author) / Dai, Yong (author) / Huang, Shuai (author) / Armaghani, Danial Jahed (author) / Qiu, Yingui (author)
Acta Geotechnica ; 18 ; 1431-1446
2023-03-01
16 pages
Article (Journal)
Electronic Resource
English
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