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...
Saved in:
| Published in | Computers and geotechnics Vol. 172; p. 106465 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.08.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-352X 1873-7633 |
| DOI | 10.1016/j.compgeo.2024.106465 |
Cover
| 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 |
| Author_xml | – sequence: 1 givenname: Mohammad Matin surname: Rouhani fullname: Rouhani, Mohammad Matin email: m.rouhani@aut.ac.ir – sequence: 2 givenname: Ebrahim surname: Farrokh fullname: Farrokh, Ebrahim email: e.farrokh@aut.ac.ir |
| BookMark | eNqFkM1KAzEUhYNUsK0-gpAXmJrJ38ysRItWoeBGYXYhk7lpU9pJSVJBn96U6d7VhcM5h3O_GZoMfgCE7kuyKEkpH3YL4w_HDfgFJZRnTXIprtC0rCtWVJKxCZoSKmXBBG1v0CzGHcm5pm6mqF1uXYQ97lyK2JxScsMGWx8MYIjJHXRyfsCneJbb1bP3MWE99Lh31kKAIWF_zDb3Oxr1fuODS9tDvEXXVu8j3F3uHH29vnwu34r1x-p9-bQuTJ6Xio4KqjWUrKbWGGkJiK7pSN0bZknPa2tJxRqjpdScG8mtEJJ2-akeGs11xeZIjL0m-BgDWHUMeXb4USVRZzxqpy541BmPGvHk3OOYgzzu20FQ0TgYDPQugEmq9-6fhj-w2XU5 |
| 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 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.compgeo.2024.106465 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-7633 |
| ExternalDocumentID | 10_1016_j_compgeo_2024_106465 S0266352X24004014 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABJNI ABMAC ABQEM ABQYD ABTAH ABXDB ACDAQ ACGFS ACIWK ACLVX ACNNM ACRLP ACSBN ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG ATOGT AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE IMUCA J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SES SET SEW SPC SPCBC SSE SST SSV SSZ T5K TN5 WH7 WUQ ZMT ZY4 ~02 ~G- AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c187t-b252aae1382fcc6f0e5b9b08dc3f0d48ff0739ca66a44c64f5562b763de9a4a73 |
| IEDL.DBID | .~1 |
| ISSN | 0266-352X |
| IngestDate | Wed Oct 01 02:09:13 EDT 2025 Sat Jun 29 15:30:40 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Grid search Chisel bit Bayesian algorithm XGBoost Cutting force |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c187t-b252aae1382fcc6f0e5b9b08dc3f0d48ff0739ca66a44c64f5562b763de9a4a73 |
| ParticipantIDs | crossref_primary_10_1016_j_compgeo_2024_106465 elsevier_sciencedirect_doi_10_1016_j_compgeo_2024_106465 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | August 2024 2024-08-00 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: August 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Computers and geotechnics |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| 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 |
| SSID | ssj0016989 |
| Score | 2.4114413 |
| 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,... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| 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 |
| Volume | 172 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1873-7633 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0016989 issn: 0266-352X databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1873-7633 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0016989 issn: 0266-352X databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Complete Freedom Collection [SCCMFC] customDbUrl: eissn: 1873-7633 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0016989 issn: 0266-352X databaseCode: ACRLP dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals [SCFCJ] customDbUrl: eissn: 1873-7633 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0016989 issn: 0266-352X databaseCode: AIKHN dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1873-7633 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0016989 issn: 0266-352X databaseCode: AKRWK dateStart: 19850101 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT4NAEN409aIH4zPWR7MHr1soXRY41sZaNfaiTbiRffYRC01Lr_52d3hoTYwHj2xYQj7INzPwzTcI3VJwiDFUER6yiFjCEySyN0yMkkZH0gjqQe_wy5iNJvQp9uMGGtS9MCCrrLi_5PSCrasVp0LTWc3nzqutHmy09GJQQdoqATxBKQ1gikHn40vm0YUBieV3Fkbg7O8uHmcB115Nix5Aj9o1RiHG_BafdmLO8AgdVski7pf3c4waOj1BBzsWgqcoHsxgYBIW83yD5bZQMWObiEqNwT-jbEzEoG6f4vjhLss2OeapwvVglBxnljSWVTcm5u_TbD3PZ8vNGZoM798GI1JNSyCyGwY5EZ7vca7BU9BIyYyrfREJN1SyZ1xFQ2Pgp5zkjHFKJaPGt6mPsPSidMQpD3rnqJlmqb5A2BhqKxXXRL60BZSUoYp6mipp4xj3aTdooU6NUbIqTTGSWi22SCpQEwA1KUFtobBGMvnxdBNL3H9vvfz_1iu0D0elXO8aNfP1Vt_YFCIX7eIdaaO9_uPzaPwJWovJFA |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3LTsJAFJ0gLtSF8RnxOQu3Y6FMp-1SiYgKbISku8k8oURaAmXrtzvTh2JiXLhtO83ktDn33vacewG4xbZDjMYSsYCEyBAeR6HZMNJSaBUKzbFrvcODIemN8UvkRTXQqbwwVlZZcn_B6Tlbl0ecEk1nEcfOm6keTLR0I6uCNFUC3gLb2HN9W4HdfXzpPFp2QmLxoYUge_m3jceZ2ZsvJrkJ0MXmGME2yPwWoDaCTvcA7JfZIrwvNnQIaio5AnsbPQSPQdSZ2olJkMfZCop1LmOGJhMVCtoGGoUzEVp5-wRGTw9pusogSySsJqNkMDWsMS_tmJC9T9JlnE3nqxMw7j6OOj1UjktAohX4GeKu5zKmbFNBLQTRTeXxkDcDKdq6KXGgtf0rJxghDGNBsPZM7sMNv0gVMsz89imoJ2mizgDUGptSpalDT5gKSohAhm2FpTCBjHm45TfAXYURXRRdMWglF5vRElRqQaUFqA0QVEjSH4-XGub-e-n5_5fegJ3eaNCn_efh6wXYtWcK7d4lqGfLtboy-UTGr_P35RM3kcqp |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Chisel+bits+cutting+force+estimation+using+XGBoost+and+different+optimization+algorithms&rft.jtitle=Computers+and+geotechnics&rft.au=Rouhani%2C+Mohammad+Matin&rft.au=Farrokh%2C+Ebrahim&rft.date=2024-08-01&rft.issn=0266-352X&rft.volume=172&rft.spage=106465&rft_id=info:doi/10.1016%2Fj.compgeo.2024.106465&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_compgeo_2024_106465 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0266-352X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0266-352X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0266-352X&client=summon |