Chisel bits cutting force estimation using XGBoost and different optimization algorithms

The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground, with the required cutting force being a vital factor in tool design. However, current methods either lack accuracy or fail to consider all n...

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Published inComputers and geotechnics Vol. 172; p. 106465
Main Authors Rouhani, Mohammad Matin, Farrokh, Ebrahim
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2024
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Online AccessGet full text
ISSN0266-352X
1873-7633
DOI10.1016/j.compgeo.2024.106465

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Abstract The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground, with the required cutting force being a vital factor in tool design. However, current methods either lack accuracy or fail to consider all necessary parameters. To address this issue, a new cutting force prediction model was developed to improve response quality and consider the most effective parameters. 206 data points were collected from studies on rock cutting with different strength, including information such as Uniaxial compressive strength, Brazilian tensile strength, Depth, Width, Rake angle, and clearance angle. Five different algorithms were then used to optimize the Hyperparameters in the XGBoost method, including Grid Search, Random Search, Bayesian Algorithm, Differential Evolution, and Optuna. Results showed that while most algorithms provided appropriate responses, the Grid Search and Bayesian Algorithm methods were the most effective, with R2 values (in test and train) of 0.876 and 0.93 in GS and 0.872 and 0.926 in BO, respectively. Upon comparison of the two methods, it was discovered that the GS approach yields a superior solution; however, it is also more sensitive to tuning, requires more calculations, and takes longer to provide a solution. When assessing the newly presented approach against existing methods, it was noted that the Evans and Roxborough methods produced R2 values of 0.48 and 0.53, respectively, which are notably lower than those of the new method. Lastly, the parametric analysis revealed that the cutting force is primarily affected by the depth, width, and rake angle in that order.
AbstractList The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground, with the required cutting force being a vital factor in tool design. However, current methods either lack accuracy or fail to consider all necessary parameters. To address this issue, a new cutting force prediction model was developed to improve response quality and consider the most effective parameters. 206 data points were collected from studies on rock cutting with different strength, including information such as Uniaxial compressive strength, Brazilian tensile strength, Depth, Width, Rake angle, and clearance angle. Five different algorithms were then used to optimize the Hyperparameters in the XGBoost method, including Grid Search, Random Search, Bayesian Algorithm, Differential Evolution, and Optuna. Results showed that while most algorithms provided appropriate responses, the Grid Search and Bayesian Algorithm methods were the most effective, with R2 values (in test and train) of 0.876 and 0.93 in GS and 0.872 and 0.926 in BO, respectively. Upon comparison of the two methods, it was discovered that the GS approach yields a superior solution; however, it is also more sensitive to tuning, requires more calculations, and takes longer to provide a solution. When assessing the newly presented approach against existing methods, it was noted that the Evans and Roxborough methods produced R2 values of 0.48 and 0.53, respectively, which are notably lower than those of the new method. Lastly, the parametric analysis revealed that the cutting force is primarily affected by the depth, width, and rake angle in that order.
ArticleNumber 106465
Author Farrokh, Ebrahim
Rouhani, Mohammad Matin
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Cites_doi 10.1016/j.compgeo.2022.105156
10.1007/s00603-020-02078-3
10.1016/j.jocs.2022.101587
10.1016/j.ijrmms.2005.04.009
10.1007/s11069-015-1842-3
10.1007/s00603-020-02331-9
10.1109/JPROC.2015.2494218
10.1190/IGC2017-351
10.1007/s11069-023-06137-0
10.1007/s11053-019-09548-8
10.1016/j.ijrmms.2006.09.001
10.1016/j.undsp.2020.05.008
10.1007/s12517-019-4585-8
10.1023/A:1008202821328
10.1016/j.gsf.2019.12.003
10.1007/s00170-016-9342-5
10.1007/s44196-022-00070-z
10.1016/j.ijrmms.2018.03.003
10.1016/j.tust.2011.06.006
10.3390/a15090315
10.1061/(ASCE)CP.1943-5487.0000553
10.1061/(ASCE)1532-3641(2008)8:1(39)
10.1016/j.ijrmms.2010.04.012
10.3390/app13179726
10.1007/s11069-021-04862-y
10.1016/j.petrol.2021.109869
10.1016/j.eswa.2017.02.017
10.1214/07-AOAS148
10.3390/app12094489
10.18280/ijsse.100103
10.1007/s00603-021-02758-8
10.1016/j.enggeo.2015.06.016
10.3390/app9132714
10.1007/s00366-020-00958-4
10.1016/j.oceaneng.2023.115262
10.1016/0148-9062(65)90018-5
10.1007/s00603-019-01984-5
10.1007/s00603-022-03095-0
10.1016/j.ijrmms.2007.02.005
10.1023/A:1012771025575
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Keywords Grid search
Chisel bit
Bayesian algorithm
XGBoost
Cutting force
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References Xie, Nie, Saffari, Robledo, Descote, Jian (b0220) 2021; 109
Zhang, Zhang, Wu, Goh, Lacasse, Liu, Liu (b0245) 2020; 11
Fathipour-Azar (b0085) 2023; 56
Bilgin, Copur, Balci (b0045) 2012; 27
Guo, Lv, Wang, Zhang (b0110) 2020; 10
Bergstra, Bengio (b0030) 2012; 13
Zhou, Li, Mitri (b0265) 2016; 30
Akiba, Sano, Yanase, Ohta, Koyama (b0005) 2019
Ouyang, Chen, Yang, Xu, Qiu (b0175) 2021; 54
Liu, Lv, Kong, Liu, Wei (b0150) 2023; 285
Roxborough, F.F., Phillips, H.R., 1974. Experimental studies on the excavation of rocks using picks.
Zhou, Qiu, Zhu, Armaghani, Khandelwal, Mohamad (b0270) 2021; 6
Shahriari, Swersky, Wang, Adams, De Freitas (b0190) 2015; 104
Mohammadi, Khademi Hamidi, Rostami, Goshtasbi (b0170) 2020; 53
Zhou, Zhao, Bian (b0275) 2023; 154
Butterworths, London.
Anggoro, Mukti (b0010) 2021; 14
Grima, Miedema, Van de Ketterij, Yenigül, Van Rhee (b0105) 2015; 196
Liu, Zhu (b0155) 2019; 12
Evans, I., 1958.
.
Yadav, Saldana, Murthy (b0225) 2018; 105
Friedman, J.H., Popescu, B.E., 2008.
Xia, Liu, Li, Liu (b0215) 2017; 78
Mendoza Rizo (b0160) 2010
Balci, Bilgin (b0025) 2007; 44
Chen, Guestrin (b0050) 2016
Zhang, L., Zhan, C., 2017. Machine learning in rock facies classification: An application of XGBoost.
Bilgin, Demircin, Copur, Balci, Tuncdemir, Akcin (b0040) 2006; 43
Larson, D.A., Morrell, R.J., Swanson, D.E., 1986.
pp. 1371–1374.
Hastie, Tibshirani, Friedman, Friedman (b0115) 2009; Vol. 2
Ding, Nguyen, Bui, Zhou, Moayedi (b0065) 2020; 29
Kim, Nam, Kyeon, Rehman, Yoo (b0135) 2022; 12
Menezes (b0165) 2017; 90
Zhou, Li, Mitri (b0260) 2015; 79
Aresh (b0015) 2012
Aresh, Khan, Haider (b0020) 2022; 209
Yilmaz, Yurdakul, Goktan (b0235) 2007; 44
Zhou (b0255) 2012
Fathipour-Azar (b0080) 2022; 55
Zhao, Wang, Li, Guo, Lin (b0250) 2023; 13
pp. 1407–1412.
Yasar (b0230) 2020; 53
Debnath, Baishya, Sen, Arif (b0060) 2021; 37
Putatunda, Rama (b0180) 2018
Sipper (b0195) 2022; 15
Chintakindi, Alsamhan, Abidi, Kumar (b0055) 2022; 15
Storn, Price (b0200) 1997; 11
Evans (b0070) 1965; 2
Friedman (b0090) 2001
Kim, Kang (b0130) 2020; 24
Jones (b0125) 2001; 21
Geng, Wu, Zhang, Sun, Cheng, Zhang, Pu (b0100) 2023; 119
Sun, Wu, Zhang, Zhang, Wang (b0205) 2022; 59
Huang, Detournay (b0120) 2008; 8
Tiryaki, Boland, Li (b0210) 2010; 47
Bilgin (b0035) 1977
Le, Nguyen, Zhou, Dou, Moayedi (b0145) 2019; 9
Ding (10.1016/j.compgeo.2024.106465_b0065) 2020; 29
Shahriari (10.1016/j.compgeo.2024.106465_b0190) 2015; 104
Balci (10.1016/j.compgeo.2024.106465_b0025) 2007; 44
Bilgin (10.1016/j.compgeo.2024.106465_b0035) 1977
Liu (10.1016/j.compgeo.2024.106465_b0155) 2019; 12
10.1016/j.compgeo.2024.106465_b0240
Menezes (10.1016/j.compgeo.2024.106465_b0165) 2017; 90
Friedman (10.1016/j.compgeo.2024.106465_b0090) 2001
Debnath (10.1016/j.compgeo.2024.106465_b0060) 2021; 37
Storn (10.1016/j.compgeo.2024.106465_b0200) 1997; 11
Fathipour-Azar (10.1016/j.compgeo.2024.106465_b0080) 2022; 55
Xia (10.1016/j.compgeo.2024.106465_b0215) 2017; 78
Mendoza Rizo (10.1016/j.compgeo.2024.106465_b0160) 2010
Le (10.1016/j.compgeo.2024.106465_b0145) 2019; 9
Sipper (10.1016/j.compgeo.2024.106465_b0195) 2022; 15
10.1016/j.compgeo.2024.106465_b0075
Zhou (10.1016/j.compgeo.2024.106465_b0265) 2016; 30
Zhou (10.1016/j.compgeo.2024.106465_b0275) 2023; 154
Zhang (10.1016/j.compgeo.2024.106465_b0245) 2020; 11
Yadav (10.1016/j.compgeo.2024.106465_b0225) 2018; 105
Ouyang (10.1016/j.compgeo.2024.106465_b0175) 2021; 54
Fathipour-Azar (10.1016/j.compgeo.2024.106465_b0085) 2023; 56
Zhao (10.1016/j.compgeo.2024.106465_b0250) 2023; 13
Kim (10.1016/j.compgeo.2024.106465_b0135) 2022; 12
Anggoro (10.1016/j.compgeo.2024.106465_b0010) 2021; 14
Akiba (10.1016/j.compgeo.2024.106465_b0005) 2019
Yilmaz (10.1016/j.compgeo.2024.106465_b0235) 2007; 44
Xie (10.1016/j.compgeo.2024.106465_b0220) 2021; 109
Putatunda (10.1016/j.compgeo.2024.106465_b0180) 2018
Kim (10.1016/j.compgeo.2024.106465_b0130) 2020; 24
Aresh (10.1016/j.compgeo.2024.106465_b0015) 2012
10.1016/j.compgeo.2024.106465_b0140
10.1016/j.compgeo.2024.106465_b0185
Chen (10.1016/j.compgeo.2024.106465_b0050) 2016
Mohammadi (10.1016/j.compgeo.2024.106465_b0170) 2020; 53
Chintakindi (10.1016/j.compgeo.2024.106465_b0055) 2022; 15
Yasar (10.1016/j.compgeo.2024.106465_b0230) 2020; 53
Aresh (10.1016/j.compgeo.2024.106465_b0020) 2022; 209
Hastie (10.1016/j.compgeo.2024.106465_b0115) 2009; Vol. 2
Huang (10.1016/j.compgeo.2024.106465_b0120) 2008; 8
Grima (10.1016/j.compgeo.2024.106465_b0105) 2015; 196
Guo (10.1016/j.compgeo.2024.106465_b0110) 2020; 10
Zhou (10.1016/j.compgeo.2024.106465_b0260) 2015; 79
Bilgin (10.1016/j.compgeo.2024.106465_b0045) 2012; 27
10.1016/j.compgeo.2024.106465_b0095
Bilgin (10.1016/j.compgeo.2024.106465_b0040) 2006; 43
Sun (10.1016/j.compgeo.2024.106465_b0205) 2022; 59
Jones (10.1016/j.compgeo.2024.106465_b0125) 2001; 21
Geng (10.1016/j.compgeo.2024.106465_b0100) 2023; 119
Zhou (10.1016/j.compgeo.2024.106465_b0255) 2012
Zhou (10.1016/j.compgeo.2024.106465_b0270) 2021; 6
Tiryaki (10.1016/j.compgeo.2024.106465_b0210) 2010; 47
Evans (10.1016/j.compgeo.2024.106465_b0070) 1965; 2
Bergstra (10.1016/j.compgeo.2024.106465_b0030) 2012; 13
Liu (10.1016/j.compgeo.2024.106465_b0150) 2023; 285
References_xml – volume: 90
  start-page: 127
  year: 2017
  end-page: 139
  ident: b0165
  article-title: Influence of rock mechanical properties and rake angle on the formation of rock fragments during cutting operation
  publication-title: Int. J. Adv. Manuf. Technol.
– volume: 10
  start-page: 17
  year: 2020
  end-page: 25
  ident: b0110
  article-title: Rock-breaking performance of cutters of tunnel boring machine in broken coal rock formation
  publication-title: Int. J. Saf. Secur. Eng
– volume: 11
  start-page: 341
  year: 1997
  end-page: 359
  ident: b0200
  article-title: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
  publication-title: J. Glob. Optim.
– reference: Zhang, L., Zhan, C., 2017. Machine learning in rock facies classification: An application of XGBoost.
– reference: Evans, I., 1958.
– volume: 8
  start-page: 39
  year: 2008
  end-page: 44
  ident: b0120
  article-title: Intrinsic length scales in tool-rock interaction
  publication-title: Int. J. Geomech.
– volume: 13
  start-page: 9726
  year: 2023
  ident: b0250
  article-title: Prediction of maximum tunnel uplift caused by overlying excavation using XGBoost algorithm with Bayesian optimization
  publication-title: Appl. Sci.
– volume: 30
  start-page: 4016003
  year: 2016
  ident: b0265
  article-title: Classification of rockburst in underground projects: comparison of ten supervised learning methods
  publication-title: J. Comput. Civ. Eng.
– volume: 44
  start-page: 962
  year: 2007
  end-page: 970
  ident: b0235
  article-title: Prediction of radial bit cutting force in high-strength rocks using multiple linear regression analysis
  publication-title: Int. J. Rock Mech. Min. Sci.
– volume: 109
  start-page: 931
  year: 2021
  end-page: 948
  ident: b0220
  article-title: Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
– start-page: 785
  year: 2016
  end-page: 794
  ident: b0050
  article-title: Xgboost: A scalable tree boosting system
  publication-title: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining
– volume: 6
  start-page: 506
  year: 2021
  end-page: 515
  ident: b0270
  article-title: Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
  publication-title: Underground Space
– volume: 56
  start-page: 221
  year: 2023
  end-page: 236
  ident: b0085
  article-title: Mean cutting force prediction of conical picks using ensemble learning paradigm
  publication-title: Rock Mech. Rock Eng.
– volume: 209
  year: 2022
  ident: b0020
  article-title: Experimental investigation and numerical simulation of chip formation mechanisms in cutting rock-like materials
  publication-title: J. Pet. Sci. Eng.
– volume: 12
  start-page: 4489
  year: 2022
  ident: b0135
  article-title: Analysis of the effect of the tool shape on the performance of pre-cutting machines during tunneling using linear cutting tests
  publication-title: Appl. Sci.
– volume: 79
  start-page: 291
  year: 2015
  end-page: 316
  ident: b0260
  article-title: Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction
  publication-title: Nat. Hazards
– volume: 47
  start-page: 858
  year: 2010
  end-page: 864
  ident: b0210
  article-title: Empirical models to predict mean cutting forces on point-attack pick cutters
  publication-title: Int. J. Rock Mech. Min. Sci.
– start-page: 169
  year: 2012
  ident: b0015
  article-title: Fundamental Study into the Mechanics of Material Removal in Rock Cutting: Doktoral thesis
  publication-title: University of Northumbria at Newcastle upon Tyne
– reference: , pp. 1371–1374.
– start-page: 1189
  year: 2001
  end-page: 1232
  ident: b0090
  article-title: Greedy function approximation: a gradient boosting machine
  publication-title: Ann. Stat.
– volume: 2
  start-page: 1
  year: 1965
  end-page: 12
  ident: b0070
  article-title: The force required to cut coal with blunt wedges
  publication-title: Int. J. Rock Mech. Mining Sci. Geomech. Abstracts
– volume: 15
  start-page: 18
  year: 2022
  ident: b0055
  article-title: Annealing of monel 400 alloy using principal component analysis, hyper-parameter optimization, machine learning techniques, and multi-objective particle swarm optimization
  publication-title: Int. J. Comput. Intell. Syst.
– volume: 119
  start-page: 751
  year: 2023
  end-page: 771
  ident: b0100
  article-title: Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity
  publication-title: Nat. Hazards
– volume: 78
  start-page: 225
  year: 2017
  end-page: 241
  ident: b0215
  article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
  publication-title: Expert Syst. Appl.
– volume: 154
  year: 2023
  ident: b0275
  article-title: Prediction of maximum ground surface settlement induced by shield tunneling using XGBoost algorithm with golden-sine seagull optimization
  publication-title: Comput. Geotech.
– volume: 37
  start-page: 2775
  year: 2021
  end-page: 2802
  ident: b0060
  article-title: A hybrid memory-based dragonfly algorithm with differential evolution for engineering application
  publication-title: Eng. Comput.
– volume: 196
  start-page: 24
  year: 2015
  end-page: 36
  ident: b0105
  article-title: Effect of high hyperbaric pressure on rock cutting process
  publication-title: Eng. Geol.
– year: 1977
  ident: b0035
  article-title: Investigations into the mechanical cutting characteristics of some medium and high strength rocks
– volume: 59
  year: 2022
  ident: b0205
  article-title: Based on multi-algorithm hybrid method to predict the slope safety factor– stacking ensemble learning with bayesian optimization
  publication-title: J. Comput. Sci.
– year: 2012
  ident: b0255
  article-title: Ensemble methods: foundations and algorithms
– volume: 285
  year: 2023
  ident: b0150
  article-title: Experimental study on rock breaking by single chisel pick under deep-sea hydrostatic pressure
  publication-title: Ocean Eng.
– volume: 104
  start-page: 148
  year: 2015
  end-page: 175
  ident: b0190
  article-title: Taking the human out of the loop: A review of Bayesian optimization
  publication-title: Proc. IEEE
– volume: 15
  start-page: 315
  year: 2022
  ident: b0195
  article-title: High per parameter: A large-scale study of hyperparameter tuning for machine learning Algorithms
  publication-title: Algorithms
– volume: 53
  start-page: 1375
  year: 2020
  end-page: 1392
  ident: b0170
  article-title: A closer look into chip shape/size and efficiency of rock cutting with a simple chisel pick: a laboratory scale investigation
  publication-title: Rock Mech. Rock Eng.
– volume: 13
  year: 2012
  ident: b0030
  article-title: Random search for hyper-parameter optimization
  publication-title: J. Mach. Learn. Res.
– volume: 9
  year: 2019
  ident: b0145
  article-title: Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost
  publication-title: Appl. Sci.
– volume: 53
  start-page: 2557
  year: 2020
  end-page: 2579
  ident: b0230
  article-title: A general semi-theoretical model for conical picks
  publication-title: Rock Mech. Rock Eng.
– volume: 12
  start-page: 1
  year: 2019
  end-page: 12
  ident: b0155
  article-title: Experimental study of the force response and chip formation in rock cutting
  publication-title: Arab. J. Geosci.
– start-page: 6
  year: 2018
  end-page: 10
  ident: b0180
  article-title: A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost
  publication-title: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning
– reference: , pp. 1407–1412.
– volume: 14
  year: 2021
  ident: b0010
  article-title: Performance comparison of grid search and random search methods for hyperparameter tuning in extreme gradient boosting algorithm to predict chronic kidney failure
  publication-title: Int. J. Intelligent Eng. Syst.
– volume: 43
  start-page: 139
  year: 2006
  end-page: 156
  ident: b0040
  article-title: Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results
  publication-title: Int. J. Rock Mech. Min. Sci.
– reference: Friedman, J.H., Popescu, B.E., 2008.
– reference: .
– reference: Larson, D.A., Morrell, R.J., Swanson, D.E., 1986.
– volume: 27
  start-page: 41
  year: 2012
  end-page: 51
  ident: b0045
  article-title: Effect of replacing disc cutters with chisel tools on performance of a TBM in difficult ground conditions
  publication-title: Tunn. Undergr. Space Technol.
– volume: 54
  start-page: 1609
  year: 2021
  end-page: 1619
  ident: b0175
  article-title: Experimental study on sandstone rock cutting with chisel picks
  publication-title: Rock Mech. Rock Eng.
– volume: 55
  start-page: 2071
  year: 2022
  end-page: 2089
  ident: b0080
  article-title: Polyaxial rock failure criteria: insights from explainable and interpretable data-driven models
  publication-title: Rock Mech. Rock Eng.
– volume: 11
  start-page: 1095
  year: 2020
  end-page: 1106
  ident: b0245
  article-title: State-of-the-art review of soft computing applications in underground excavations
  publication-title: Geosci. Front.
– volume: 29
  start-page: 751
  year: 2020
  end-page: 769
  ident: b0065
  article-title: Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms
  publication-title: Nat. Resour. Res.
– volume: 105
  start-page: 123
  year: 2018
  end-page: 132
  ident: b0225
  article-title: Experimental investigations on deformation of soft rock during cutting
  publication-title: Int. J. Rock Mech. Min. Sci.
– reference: . Butterworths, London.
– volume: Vol. 2
  year: 2009
  ident: b0115
  publication-title: The elements of statistical learning: data mining, inference, and prediction
– volume: 24
  start-page: 49
  year: 2020
  end-page: 54
  ident: b0130
  article-title: Comparison of hyper-parameter optimization methods for deep neural networks
  publication-title: 전기전자학회논문지
– start-page: 2623
  year: 2019
  end-page: 2631
  ident: b0005
  article-title: Optuna: A next-generation hyperparameter optimization framework
  publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
– volume: 44
  start-page: 468
  year: 2007
  end-page: 476
  ident: b0025
  article-title: Correlative study of linear small and full-scale rock cutting tests to select mechanized excavation machines
  publication-title: Int. J. Rock Mech. Min. Sci.
– volume: 21
  start-page: 345
  year: 2001
  end-page: 383
  ident: b0125
  article-title: A taxonomy of global optimization methods based on response surfaces
  publication-title: J. Glob. Optim.
– reference: Roxborough, F.F., Phillips, H.R., 1974. Experimental studies on the excavation of rocks using picks.
– year: 2010
  ident: b0160
  article-title: Modeling rock cutting using DEM with crushable particles
– volume: 154
  year: 2023
  ident: 10.1016/j.compgeo.2024.106465_b0275
  article-title: Prediction of maximum ground surface settlement induced by shield tunneling using XGBoost algorithm with golden-sine seagull optimization
  publication-title: Comput. Geotech.
  doi: 10.1016/j.compgeo.2022.105156
– volume: 53
  start-page: 2557
  issue: 6
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0230
  article-title: A general semi-theoretical model for conical picks
  publication-title: Rock Mech. Rock Eng.
  doi: 10.1007/s00603-020-02078-3
– volume: 59
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0205
  article-title: Based on multi-algorithm hybrid method to predict the slope safety factor– stacking ensemble learning with bayesian optimization
  publication-title: J. Comput. Sci.
  doi: 10.1016/j.jocs.2022.101587
– volume: 43
  start-page: 139
  issue: 1
  year: 2006
  ident: 10.1016/j.compgeo.2024.106465_b0040
  article-title: Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results
  publication-title: Int. J. Rock Mech. Min. Sci.
  doi: 10.1016/j.ijrmms.2005.04.009
– volume: 79
  start-page: 291
  issue: 1
  year: 2015
  ident: 10.1016/j.compgeo.2024.106465_b0260
  article-title: Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-015-1842-3
– volume: 13
  issue: 2
  year: 2012
  ident: 10.1016/j.compgeo.2024.106465_b0030
  article-title: Random search for hyper-parameter optimization
  publication-title: J. Mach. Learn. Res.
– volume: 54
  start-page: 1609
  year: 2021
  ident: 10.1016/j.compgeo.2024.106465_b0175
  article-title: Experimental study on sandstone rock cutting with chisel picks
  publication-title: Rock Mech. Rock Eng.
  doi: 10.1007/s00603-020-02331-9
– ident: 10.1016/j.compgeo.2024.106465_b0075
– volume: 104
  start-page: 148
  issue: 1
  year: 2015
  ident: 10.1016/j.compgeo.2024.106465_b0190
  article-title: Taking the human out of the loop: A review of Bayesian optimization
  publication-title: Proc. IEEE
  doi: 10.1109/JPROC.2015.2494218
– ident: 10.1016/j.compgeo.2024.106465_b0240
  doi: 10.1190/IGC2017-351
– volume: 119
  start-page: 751
  issue: 1
  year: 2023
  ident: 10.1016/j.compgeo.2024.106465_b0100
  article-title: Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-023-06137-0
– year: 2010
  ident: 10.1016/j.compgeo.2024.106465_b0160
– volume: 29
  start-page: 751
  issue: 2
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0065
  article-title: Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms
  publication-title: Nat. Resour. Res.
  doi: 10.1007/s11053-019-09548-8
– volume: 44
  start-page: 468
  issue: 3
  year: 2007
  ident: 10.1016/j.compgeo.2024.106465_b0025
  article-title: Correlative study of linear small and full-scale rock cutting tests to select mechanized excavation machines
  publication-title: Int. J. Rock Mech. Min. Sci.
  doi: 10.1016/j.ijrmms.2006.09.001
– volume: 6
  start-page: 506
  issue: 5
  year: 2021
  ident: 10.1016/j.compgeo.2024.106465_b0270
  article-title: Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
  publication-title: Underground Space
  doi: 10.1016/j.undsp.2020.05.008
– volume: 12
  start-page: 1
  year: 2019
  ident: 10.1016/j.compgeo.2024.106465_b0155
  article-title: Experimental study of the force response and chip formation in rock cutting
  publication-title: Arab. J. Geosci.
  doi: 10.1007/s12517-019-4585-8
– ident: 10.1016/j.compgeo.2024.106465_b0185
– volume: 11
  start-page: 341
  year: 1997
  ident: 10.1016/j.compgeo.2024.106465_b0200
  article-title: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
  publication-title: J. Glob. Optim.
  doi: 10.1023/A:1008202821328
– volume: 11
  start-page: 1095
  issue: 4
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0245
  article-title: State-of-the-art review of soft computing applications in underground excavations
  publication-title: Geosci. Front.
  doi: 10.1016/j.gsf.2019.12.003
– volume: 24
  start-page: 49
  issue: 4
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0130
  article-title: Comparison of hyper-parameter optimization methods for deep neural networks
  publication-title: 전기전자학회논문지
– volume: 90
  start-page: 127
  year: 2017
  ident: 10.1016/j.compgeo.2024.106465_b0165
  article-title: Influence of rock mechanical properties and rake angle on the formation of rock fragments during cutting operation
  publication-title: Int. J. Adv. Manuf. Technol.
  doi: 10.1007/s00170-016-9342-5
– volume: 15
  start-page: 18
  issue: 1
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0055
  article-title: Annealing of monel 400 alloy using principal component analysis, hyper-parameter optimization, machine learning techniques, and multi-objective particle swarm optimization
  publication-title: Int. J. Comput. Intell. Syst.
  doi: 10.1007/s44196-022-00070-z
– ident: 10.1016/j.compgeo.2024.106465_b0140
– volume: 105
  start-page: 123
  year: 2018
  ident: 10.1016/j.compgeo.2024.106465_b0225
  article-title: Experimental investigations on deformation of soft rock during cutting
  publication-title: Int. J. Rock Mech. Min. Sci.
  doi: 10.1016/j.ijrmms.2018.03.003
– volume: 27
  start-page: 41
  issue: 1
  year: 2012
  ident: 10.1016/j.compgeo.2024.106465_b0045
  article-title: Effect of replacing disc cutters with chisel tools on performance of a TBM in difficult ground conditions
  publication-title: Tunn. Undergr. Space Technol.
  doi: 10.1016/j.tust.2011.06.006
– volume: 15
  start-page: 315
  issue: 9
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0195
  article-title: High per parameter: A large-scale study of hyperparameter tuning for machine learning Algorithms
  publication-title: Algorithms
  doi: 10.3390/a15090315
– year: 2012
  ident: 10.1016/j.compgeo.2024.106465_b0255
– volume: 30
  start-page: 4016003
  issue: 5
  year: 2016
  ident: 10.1016/j.compgeo.2024.106465_b0265
  article-title: Classification of rockburst in underground projects: comparison of ten supervised learning methods
  publication-title: J. Comput. Civ. Eng.
  doi: 10.1061/(ASCE)CP.1943-5487.0000553
– volume: 8
  start-page: 39
  issue: 1
  year: 2008
  ident: 10.1016/j.compgeo.2024.106465_b0120
  article-title: Intrinsic length scales in tool-rock interaction
  publication-title: Int. J. Geomech.
  doi: 10.1061/(ASCE)1532-3641(2008)8:1(39)
– volume: 47
  start-page: 858
  issue: 5
  year: 2010
  ident: 10.1016/j.compgeo.2024.106465_b0210
  article-title: Empirical models to predict mean cutting forces on point-attack pick cutters
  publication-title: Int. J. Rock Mech. Min. Sci.
  doi: 10.1016/j.ijrmms.2010.04.012
– volume: 13
  start-page: 9726
  issue: 17
  year: 2023
  ident: 10.1016/j.compgeo.2024.106465_b0250
  article-title: Prediction of maximum tunnel uplift caused by overlying excavation using XGBoost algorithm with Bayesian optimization
  publication-title: Appl. Sci.
  doi: 10.3390/app13179726
– volume: 109
  start-page: 931
  issue: 1
  year: 2021
  ident: 10.1016/j.compgeo.2024.106465_b0220
  article-title: Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
  publication-title: Natural Hazards
  doi: 10.1007/s11069-021-04862-y
– volume: 209
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0020
  article-title: Experimental investigation and numerical simulation of chip formation mechanisms in cutting rock-like materials
  publication-title: J. Pet. Sci. Eng.
  doi: 10.1016/j.petrol.2021.109869
– volume: 78
  start-page: 225
  year: 2017
  ident: 10.1016/j.compgeo.2024.106465_b0215
  article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2017.02.017
– start-page: 2623
  year: 2019
  ident: 10.1016/j.compgeo.2024.106465_b0005
  article-title: Optuna: A next-generation hyperparameter optimization framework
– ident: 10.1016/j.compgeo.2024.106465_b0095
  doi: 10.1214/07-AOAS148
– volume: 12
  start-page: 4489
  issue: 9
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0135
  article-title: Analysis of the effect of the tool shape on the performance of pre-cutting machines during tunneling using linear cutting tests
  publication-title: Appl. Sci.
  doi: 10.3390/app12094489
– volume: 10
  start-page: 17
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0110
  article-title: Rock-breaking performance of cutters of tunnel boring machine in broken coal rock formation
  publication-title: Int. J. Saf. Secur. Eng
  doi: 10.18280/ijsse.100103
– volume: 55
  start-page: 2071
  issue: 4
  year: 2022
  ident: 10.1016/j.compgeo.2024.106465_b0080
  article-title: Polyaxial rock failure criteria: insights from explainable and interpretable data-driven models
  publication-title: Rock Mech. Rock Eng.
  doi: 10.1007/s00603-021-02758-8
– volume: 196
  start-page: 24
  year: 2015
  ident: 10.1016/j.compgeo.2024.106465_b0105
  article-title: Effect of high hyperbaric pressure on rock cutting process
  publication-title: Eng. Geol.
  doi: 10.1016/j.enggeo.2015.06.016
– volume: 9
  issue: 13
  year: 2019
  ident: 10.1016/j.compgeo.2024.106465_b0145
  article-title: Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost
  publication-title: Appl. Sci.
  doi: 10.3390/app9132714
– volume: 37
  start-page: 2775
  year: 2021
  ident: 10.1016/j.compgeo.2024.106465_b0060
  article-title: A hybrid memory-based dragonfly algorithm with differential evolution for engineering application
  publication-title: Eng. Comput.
  doi: 10.1007/s00366-020-00958-4
– start-page: 169
  year: 2012
  ident: 10.1016/j.compgeo.2024.106465_b0015
  article-title: Fundamental Study into the Mechanics of Material Removal in Rock Cutting: Doktoral thesis
– start-page: 1189
  year: 2001
  ident: 10.1016/j.compgeo.2024.106465_b0090
  article-title: Greedy function approximation: a gradient boosting machine
  publication-title: Ann. Stat.
– volume: 285
  year: 2023
  ident: 10.1016/j.compgeo.2024.106465_b0150
  article-title: Experimental study on rock breaking by single chisel pick under deep-sea hydrostatic pressure
  publication-title: Ocean Eng.
  doi: 10.1016/j.oceaneng.2023.115262
– volume: Vol. 2
  year: 2009
  ident: 10.1016/j.compgeo.2024.106465_b0115
– volume: 2
  start-page: 1
  issue: 1
  year: 1965
  ident: 10.1016/j.compgeo.2024.106465_b0070
  article-title: The force required to cut coal with blunt wedges
  publication-title: Int. J. Rock Mech. Mining Sci. Geomech. Abstracts
  doi: 10.1016/0148-9062(65)90018-5
– volume: 53
  start-page: 1375
  year: 2020
  ident: 10.1016/j.compgeo.2024.106465_b0170
  article-title: A closer look into chip shape/size and efficiency of rock cutting with a simple chisel pick: a laboratory scale investigation
  publication-title: Rock Mech. Rock Eng.
  doi: 10.1007/s00603-019-01984-5
– volume: 14
  issue: 6
  year: 2021
  ident: 10.1016/j.compgeo.2024.106465_b0010
  article-title: Performance comparison of grid search and random search methods for hyperparameter tuning in extreme gradient boosting algorithm to predict chronic kidney failure
  publication-title: Int. J. Intelligent Eng. Syst.
– year: 1977
  ident: 10.1016/j.compgeo.2024.106465_b0035
– start-page: 785
  year: 2016
  ident: 10.1016/j.compgeo.2024.106465_b0050
  article-title: Xgboost: A scalable tree boosting system
– start-page: 6
  year: 2018
  ident: 10.1016/j.compgeo.2024.106465_b0180
  article-title: A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost
– volume: 56
  start-page: 221
  issue: 1
  year: 2023
  ident: 10.1016/j.compgeo.2024.106465_b0085
  article-title: Mean cutting force prediction of conical picks using ensemble learning paradigm
  publication-title: Rock Mech. Rock Eng.
  doi: 10.1007/s00603-022-03095-0
– volume: 44
  start-page: 962
  issue: 6
  year: 2007
  ident: 10.1016/j.compgeo.2024.106465_b0235
  article-title: Prediction of radial bit cutting force in high-strength rocks using multiple linear regression analysis
  publication-title: Int. J. Rock Mech. Min. Sci.
  doi: 10.1016/j.ijrmms.2007.02.005
– volume: 21
  start-page: 345
  issue: 4
  year: 2001
  ident: 10.1016/j.compgeo.2024.106465_b0125
  article-title: A taxonomy of global optimization methods based on response surfaces
  publication-title: J. Glob. Optim.
  doi: 10.1023/A:1012771025575
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Snippet The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground,...
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StartPage 106465
SubjectTerms Bayesian algorithm
Chisel bit
Cutting force
Grid search
XGBoost
Title Chisel bits cutting force estimation using XGBoost and different optimization algorithms
URI https://dx.doi.org/10.1016/j.compgeo.2024.106465
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