Proposing several hybrid SSA—machine learning techniques for estimating rock cuttability by conical pick with relieved cutting modes

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...

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Vydané v:	Acta geotechnica Ročník 18; číslo 3; s. 1431 - 1446
Hlavní autori:	Zhou, Jian, Dai, Yong, Huang, Shuai, Armaghani, Danial Jahed, Qiu, Yingui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023 Springer Nature B.V
Predmet:	Algorithms Angle of attack Artificial neural networks Back propagation networks Complex Fluids and Microfluidics Compressive strength Cutting Cutting parameters Engineering Excavation Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Kernel functions Learning algorithms Machine learning Mathematical models Mechanical properties Neural networks Parameters Predictions Rake angle Research Paper Rock Rocks Search algorithms Sediment samples Soft and Granular Matter Soil Science & Conservation Solid Mechanics Specific energy Support vector machines Tensile strength Rock cuttability Specific energy Sparrow search algorithm Machine learning
ISSN:	1861-1125, 1861-1133
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Popis
Shrnutí:	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 σ t and uniaxial compressive strength of the rock σ c ), 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 ( R 2 ) 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%), R 2 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 , σ c and σ t are the most significant input variables for SE prediction.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1861-1125 1861-1133
DOI:	10.1007/s11440-022-01685-4