Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach
[Display omitted] •Three hybrid RF models were developed to predict blast-induced overbreak.•The RF-TSA model outperformed other predictive RF models in estimating overbreak.•The blast design parameters showed a deep impact on the overbreak generation.•The PF was selected as the most influential fac...
Uloženo v:
| Vydáno v: | Tunnelling and underground space technology Ročník 133; s. 104979 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.03.2023
|
| Témata: | |
| ISSN: | 0886-7798, 1878-4364 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | [Display omitted]
•Three hybrid RF models were developed to predict blast-induced overbreak.•The RF-TSA model outperformed other predictive RF models in estimating overbreak.•The blast design parameters showed a deep impact on the overbreak generation.•The PF was selected as the most influential factor in the overbreak generation.
Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects. |
|---|---|
| AbstractList | [Display omitted]
•Three hybrid RF models were developed to predict blast-induced overbreak.•The RF-TSA model outperformed other predictive RF models in estimating overbreak.•The blast design parameters showed a deep impact on the overbreak generation.•The PF was selected as the most influential factor in the overbreak generation.
Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects. |
| ArticleNumber | 104979 |
| Author | Armaghani, Danial Jahed He, Biao Lai, Sai Hin |
| Author_xml | – sequence: 1 givenname: Biao surname: He fullname: He, Biao email: s2005282@siswa.um.edu.my organization: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 2 givenname: Danial Jahed surname: Armaghani fullname: Armaghani, Danial Jahed email: danial.jahedarmaghani@uts.edu.au organization: School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia – sequence: 3 givenname: Sai Hin surname: Lai fullname: Lai, Sai Hin email: laish@um.edu.my organization: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia |
| BookMark | eNp9kM1KAzEURoMo2FZfwFVeYGqSSWYScVOKf1Bwo-uQydyxqTNJSTIF394pdeWiq8t3-c6Fe-bo0gcPCN1RsqSEVve7ZR5TXjLC2LTgqlYXaEZlLQteVvwSzYiUVVHXSl6jeUo7QohgTM2QXaUEKQ3gMw4dzqP30OOmNyk7_1U4344WWhwOEJsI5vsBr7CfUo8HyGYLY3RT0xaNSVMtGt-GAXchQsrY7PcxGLu9QVed6RPc_s0F-nx--li_Fpv3l7f1alPYkpBcSAFAmQCpGG-g5NzymtCaC9F2jemo6tqWESpV3TSi6XglK2mlVEbUggAl5QLJ010bQ0oROm1dNtkFn6NxvaZEH2XpnT7K0kdZ-iRrQtk_dB_dYOLPeejxBMH01MFB1Mk68JMvF8Fm3QZ3Dv8FbEyHpQ |
| CitedBy_id | crossref_primary_10_1002_fsn3_70107 crossref_primary_10_1016_j_ijhydene_2024_09_054 crossref_primary_10_1016_j_jer_2024_05_009 crossref_primary_10_3390_app13031345 crossref_primary_10_1007_s10064_023_03154_y crossref_primary_10_1016_j_jocs_2024_102266 crossref_primary_10_1007_s10462_025_11266_y crossref_primary_10_1007_s11831_025_10228_5 crossref_primary_10_1016_j_eiar_2023_107229 crossref_primary_10_1016_j_tust_2024_105727 crossref_primary_10_1007_s10064_025_04216_z crossref_primary_10_1016_j_sna_2023_114973 crossref_primary_10_1016_j_powtec_2025_120638 crossref_primary_10_1111_exsy_70080 crossref_primary_10_1016_j_tust_2024_105586 crossref_primary_10_1080_15376494_2023_2224782 crossref_primary_10_1007_s00500_023_09613_8 crossref_primary_10_1016_j_trgeo_2024_101228 crossref_primary_10_3390_su15054230 crossref_primary_10_1061_JPSEA2_PSENG_1559 crossref_primary_10_1016_j_eswa_2024_125909 crossref_primary_10_1016_j_aei_2025_103227 crossref_primary_10_3390_app15115996 crossref_primary_10_1016_j_geoen_2023_212518 crossref_primary_10_1007_s42461_024_00976_6 crossref_primary_10_1016_j_simpat_2023_102854 crossref_primary_10_1080_17538947_2025_2459317 crossref_primary_10_1007_s00603_024_04055_6 crossref_primary_10_3389_fevo_2023_1255384 crossref_primary_10_1007_s13369_023_08360_0 crossref_primary_10_1007_s40948_024_00912_4 crossref_primary_10_1061_JCEMD4_COENG_15907 crossref_primary_10_1007_s00603_024_03947_x crossref_primary_10_3390_electronics13234755 crossref_primary_10_3390_machines13090758 crossref_primary_10_1016_j_envsoft_2024_106058 crossref_primary_10_1007_s10064_023_03138_y crossref_primary_10_1007_s42461_025_01311_3 crossref_primary_10_3390_math11102358 crossref_primary_10_3390_math11143064 crossref_primary_10_1007_s00603_023_03522_w crossref_primary_10_1007_s11771_024_5680_x crossref_primary_10_3390_buildings14113505 crossref_primary_10_32604_cmes_2023_046025 crossref_primary_10_3390_app13021208 crossref_primary_10_1007_s40948_025_00963_1 crossref_primary_10_3390_buildings13092278 crossref_primary_10_1016_j_tust_2023_105508 crossref_primary_10_3390_f14091864 crossref_primary_10_1007_s10462_024_10772_9 crossref_primary_10_1016_j_isci_2025_111924 crossref_primary_10_1016_j_compgeo_2023_105557 crossref_primary_10_1016_j_swevo_2024_101532 crossref_primary_10_1155_2024_9217395 crossref_primary_10_3390_infrastructures8080125 crossref_primary_10_1016_j_tust_2025_106997 crossref_primary_10_1007_s10462_024_10898_w crossref_primary_10_1007_s41660_025_00483_1 crossref_primary_10_3390_geosciences15020047 crossref_primary_10_3390_buildings14123998 crossref_primary_10_1007_s00521_025_11321_3 crossref_primary_10_1109_TEM_2023_3348991 crossref_primary_10_1016_j_mtcomm_2023_106403 crossref_primary_10_1038_s41598_024_85042_3 crossref_primary_10_1007_s10668_025_06635_0 crossref_primary_10_1061_IJGNAI_GMENG_9531 crossref_primary_10_1007_s42461_024_01074_3 crossref_primary_10_1007_s00202_023_02084_y |
| Cites_doi | 10.1016/j.tust.2020.103475 10.1109/ACCESS.2021.3072336 10.1007/s00603-019-01947-w 10.1016/j.advengsoft.2016.01.008 10.1016/j.asoc.2010.06.003 10.1016/j.beproc.2011.09.006 10.1007/s10064-020-02064-7 10.1007/978-3-642-31537-4_13 10.1142/9789812839640_0029 10.1016/j.trgeo.2021.100588 10.1155/2013/706491 10.1016/j.undsp.2020.05.005 10.1007/s00603-021-02723-5 10.1016/j.asoc.2021.107504 10.1016/j.tust.2019.103060 10.2307/1379766 10.1007/s10706-021-01834-8 10.1038/s42254-021-00314-5 10.1109/TKDE.2017.2720168 10.1016/j.ijmst.2015.05.012 10.1016/j.advengsoft.2013.12.007 10.1007/s00366-018-0658-7 10.1016/j.tust.2018.05.023 10.1145/3447814 10.1088/1755-1315/833/1/012165 10.1080/10618600.2014.907095 10.1016/j.tust.2017.09.007 10.1007/s40948-015-0009-8 10.1007/978-3-642-58069-7_38 10.1061/9780784480106.014 10.1016/j.enggeo.2007.10.009 10.1016/j.tust.2016.12.009 10.1016/j.tust.2021.104017 10.1016/S1003-6326(13)62487-5 10.1016/j.eswa.2019.113134 10.1016/j.gsf.2020.03.007 10.1016/j.elerap.2018.10.004 10.1016/j.jrmge.2013.11.001 10.1016/j.ijmst.2018.04.013 10.1007/BF00421946 10.1525/bio.2013.63.2.5 10.1007/s10064-017-1116-2 10.1016/j.trgeo.2022.100745 10.1007/BF00421947 10.1016/j.tust.2013.06.003 10.1007/s11053-021-09929-y 10.1023/A:1010933404324 10.1016/j.ijmst.2015.03.018 10.1016/j.ijrmms.2014.03.003 10.1016/j.tust.2014.04.009 10.1007/s10706-017-0336-3 10.1002/widm.8 10.1016/0148-9062(71)90018-0 10.1016/j.engappai.2020.103541 10.1198/016214504000001466 10.1016/j.tust.2020.103493 10.1016/j.tust.2018.07.023 10.1080/17480930.2019.1709012 10.1007/s00521-010-0457-6 10.1016/j.tust.2011.09.004 10.1109/ACCESS.2021.3056407 10.1016/j.engappai.2020.104015 10.1016/j.tust.2008.08.002 10.1007/s00366-017-0520-3 |
| ContentType | Journal Article |
| Copyright | 2022 |
| Copyright_xml | – notice: 2022 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.tust.2022.104979 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1878-4364 |
| ExternalDocumentID | 10_1016_j_tust_2022_104979 S0886779822006204 |
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29Q 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABFNM ABJNI ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACIWK ACLVX ACNNM ACRLP ACSBN ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HVGLF HZ~ IHE IMUCA J1W JJJVA KOM LY3 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SEP SES SET SEW SPC SPCBC SSE SST SSZ T5K WUQ ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-85ee125e8924be344c47017455dfbaf19fdd201897bb5bf46868c889a5750e103 |
| ISICitedReferencesCount | 81 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000989243700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0886-7798 |
| IngestDate | Sat Nov 29 07:14:54 EST 2025 Tue Nov 18 21:26:35 EST 2025 Fri Feb 23 02:39:00 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Tunicate swarm algorithm Tunnel blasting Overbreak: random forest Metaheuristic algorithms |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-85ee125e8924be344c47017455dfbaf19fdd201897bb5bf46868c889a5750e103 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_tust_2022_104979 crossref_primary_10_1016_j_tust_2022_104979 elsevier_sciencedirect_doi_10_1016_j_tust_2022_104979 |
| PublicationCentury | 2000 |
| PublicationDate | March 2023 2023-03-00 |
| PublicationDateYYYYMMDD | 2023-03-01 |
| PublicationDate_xml | – month: 03 year: 2023 text: March 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Tunnelling and underground space technology |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Ibarra, Maerz, Franklin (b0115) 1996; 14 Zhou, Qiu, Zhu, Armaghani, Li, Nguyen, Yagiz (b0375) 2021; 97 Soroush, Mehdi, Arash (b0300) 2015; 25 Cui, Sheng, Zhang, min, Liu, H. (b0050) 2021; 80 Saadat, Khandelwal, Monjezi (b0290) 2014; 6 Karniadakis, Kevrekidis, Lu, Perdikaris, Wang, Yang (b0140) 2021; 3 Yang, Li, Jie, Zhang (b0330) 2018; 81 Beni, Wang (b0025) 1993 Yang, Zeng, Lan, Zhou (b0335) 2014; 69 Armaghani, Harandizadeh, Momeni, Maizir, Zhou (b0010) 2021 Yagiz, Yazitova, Karahan (b0315) 2020; 34 Xu, Zhou, Asteris, Armaghani, Tahir (b0310) 2019; 9 Goldbogen, Friedlaender, Calambokidis, McKenna, Simon, Nowacek (b0090) 2013; 63 Armaghani, Mohamad, Narayanasamy, Narita, Yagiz (b0015) 2017; 63 Li, Zhou, Armaghani, Li (b0185) 2021; 6 Du, Liu, Zhou, Khandelwal (b0070) 2022; 39 Zhang, Hu, Liu, Tan (b0360) 2020; 103 Ye, Dong, an, Ma, D. (b0340) 2018; 32 Jang, Topal (b0125) 2013; 38 Karpatne, Atluri, Faghmous, Steinbach, Banerjee, Ganguly, Shekhar, Samatova, Kumar (b0145) 2017; 29 Koopialipoor, Jahed Armaghani, Haghighi, Ghaleini (b0170) 2019; 78 Dong, L.J., Li, X.B., Peng, K., 2013. Prediction of rockburst classification using Random Forest. Trans. Nonferrous Met. Soc. China (English Ed.) 23, 472–477. <https://doi.org/10.1016/S1003-6326(13)62487-5>. Breiman (b0035) 2001; 45 Chen, Qiu, Zhao, Rai, Ai, Wang (b0040) 2021; 115 Friedman (b0100) 2001; 34 Liu, Liu (b0210) 2017; 70 Hong, Dong, Chen, Wei (b0105) 2011; 11 Kohestani, Bazargan-Lari, Asgari-Marnani (b0160) 2017; 5 Mohammadi, Hossaini, Mirzapour, Hajiantilaki (b0245) 2015; 25 Liu, Yang, Li (b0205) 2020; 145 Zhou, Zhu, Qiu, Armaghani, Zhou, Yong (b0385) 2022; 7 Khandelwal, Rai, Shrivastva (b0155) 2015; 1 Oshiro, T.M., Perez, P.S., 2012. How Many Trees in a Random Forest? 7376. <https://doi.org/10.1007/978-3-642-31537-4>. Yang, Wang, Song (b0325) 2020 Zorlu, Gokceoglu, Ocakoglu, Nefeslioglu, Acikalin (b0390) 2008; 96 Chen, Chen, Wu, Dai, Xv, Wu (b0045) 2022; 10 Kumar, Mishra, Choudhary (b0175) 2021; 2 Lin (b0195) 1998 Daraei, Zare (b0055) 2018; 28 Muro, Escobedo, Spector, Coppinger (b0265) 2011; 88 Ramulu, Chakraborty, Sitharam (b0285) 2009; 24 Yu, Monjezi, Mohammed, Dehghani, Armaghani, Ulrikh (b0350) 2021; 13 Zhou, Dai, Khandelwal, Monjezi, Yu, Qiu (b0370) 2021; 30 Kutter, Fairhurst (b0180) 1971; 8 Kaur, Awasthi, Sangal, Dhiman (b0150) 2020; 90 Weinan, E., Han, J., Zhang, L., 2020. Integrating Machine Learning with Physics-Based Modeling 1–23. Zhou, J., Shi, X., Du, K., Qiu, X., Li, X., Mitri, H.S., 2016. Development of Ground Movements Due to a Shield Tunnelling Prediction Model Using Random Forests 108–115. <https://doi.org/10.1061/9780784480106.014>. Koopialipoor, Ghaleini, Haghighi, Kanagarajan, Maarefvand, Mohamad (b0165) 2019; 35 Mottahedi, Sereshki, Ataei (b0250) 2018; 34 Goldstein, Kapelner, Bleich, Pitkin (b0095) 2015; 24 Jang, Kawamura, Shinji (b0120) 2019; 92 Li, Liu, Tseng, Zheng, Lim (b0190) 2021; 108 Jia, Willard, Karpatne, Read, Zwart, Steinbach, Kumar (b0130) 2021; 2 Mirjalili, Lewis (b0230) 2016; 95 Agrawal, Abutarboush, Ganesh, Mohamed (b0005) 2021; 9 Watkins, Schevill (b0305) 1979; 60 Mohammadi, Barati, Chamzini (b0240) 2018; 36 Loh (b0215) 2011; 1 Maerz, Ibarra, Franklin (b0220) 1996; 14 Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.-T., Kardani, N., Armaghani, D.J., 2021. Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp. Geotech. 100588. Ekeberg, Holmes, Paraskevopoulou (b0080) 2021; 833 Yilmaz, Unlu (b0345) 2014; 43 Zhang, Zhou, Armaghani, Tahir, Pham, Huynh (b0355) 2020; 10 Yang, Song, Zhou (b0320) 2022; 55 Shaorui, Jiaming, Jihong (b9000) 2013; 2013 Zhang, Wu, Zhong, Li, Wang (b0365) 2021; 12 Liu, Yang, Karekal (b0200) 2020; 53 Karir, D., Ray, A., Bharati, A.K., Chaturvedi, U., Rai, R., Khandelwal, M., 2022. Stability prediction of a natural and man-made slope using various machine learning algorithms. Transp. Geotech. 100745. Mei, Zhang, Xu, Zhu, Wang (b0225) 2021; 39 Mirjalili, Mirjalili, Lewis (b0235) 2014; 69 Foderà, Voza, Barovero, Tinti, Boldini (b0085) 2020; 105 Sim, Gan, Chang (b0295) 2005; 100 Mottahedi, Sereshki, Ataei (b0255) 2018; 80 Houssein, Helmy, Elngar, Abdelminaam, Shaban (b0110) 2021; 9 Dey, Murthy (b0060) 2012; 28 Bhatnagar, Khandelwal (b0030) 2012; 21 Kaur (10.1016/j.tust.2022.104979_b0150) 2020; 90 Ekeberg (10.1016/j.tust.2022.104979_b0080) 2021; 833 Koopialipoor (10.1016/j.tust.2022.104979_b0170) 2019; 78 Foderà (10.1016/j.tust.2022.104979_b0085) 2020; 105 Kohestani (10.1016/j.tust.2022.104979_b0160) 2017; 5 Dey (10.1016/j.tust.2022.104979_b0060) 2012; 28 Yilmaz (10.1016/j.tust.2022.104979_b0345) 2014; 43 Zhang (10.1016/j.tust.2022.104979_b0365) 2021; 12 Goldstein (10.1016/j.tust.2022.104979_b0095) 2015; 24 Zhou (10.1016/j.tust.2022.104979_b0375) 2021; 97 Ramulu (10.1016/j.tust.2022.104979_b0285) 2009; 24 Mohammadi (10.1016/j.tust.2022.104979_b0245) 2015; 25 Yagiz (10.1016/j.tust.2022.104979_b0315) 2020; 34 10.1016/j.tust.2022.104979_b0270 Muro (10.1016/j.tust.2022.104979_b0265) 2011; 88 Yu (10.1016/j.tust.2022.104979_b0350) 2021; 13 10.1016/j.tust.2022.104979_b0075 Mottahedi (10.1016/j.tust.2022.104979_b0255) 2018; 80 Armaghani (10.1016/j.tust.2022.104979_b0015) 2017; 63 Sim (10.1016/j.tust.2022.104979_b0295) 2005; 100 Soroush (10.1016/j.tust.2022.104979_b0300) 2015; 25 Armaghani (10.1016/j.tust.2022.104979_b0010) 2021 Karpatne (10.1016/j.tust.2022.104979_b0145) 2017; 29 Koopialipoor (10.1016/j.tust.2022.104979_b0165) 2019; 35 Maerz (10.1016/j.tust.2022.104979_b0220) 1996; 14 Chen (10.1016/j.tust.2022.104979_b0045) 2022; 10 Li (10.1016/j.tust.2022.104979_b0185) 2021; 6 Liu (10.1016/j.tust.2022.104979_b0200) 2020; 53 Yang (10.1016/j.tust.2022.104979_b0330) 2018; 81 Kumar (10.1016/j.tust.2022.104979_b0175) 2021; 2 Zhang (10.1016/j.tust.2022.104979_b0355) 2020; 10 Cui (10.1016/j.tust.2022.104979_b0050) 2021; 80 Agrawal (10.1016/j.tust.2022.104979_b0005) 2021; 9 Ibarra (10.1016/j.tust.2022.104979_b0115) 1996; 14 Daraei (10.1016/j.tust.2022.104979_b0055) 2018; 28 10.1016/j.tust.2022.104979_b0135 Ye (10.1016/j.tust.2022.104979_b0340) 2018; 32 Bhatnagar (10.1016/j.tust.2022.104979_b0030) 2012; 21 Chen (10.1016/j.tust.2022.104979_b0040) 2021; 115 Khandelwal (10.1016/j.tust.2022.104979_b0155) 2015; 1 Zorlu (10.1016/j.tust.2022.104979_b0390) 2008; 96 Friedman (10.1016/j.tust.2022.104979_b0100) 2001; 34 Liu (10.1016/j.tust.2022.104979_b0210) 2017; 70 Watkins (10.1016/j.tust.2022.104979_b0305) 1979; 60 Loh (10.1016/j.tust.2022.104979_b0215) 2011; 1 Jang (10.1016/j.tust.2022.104979_b0120) 2019; 92 Xu (10.1016/j.tust.2022.104979_b0310) 2019; 9 Zhang (10.1016/j.tust.2022.104979_b0360) 2020; 103 Mei (10.1016/j.tust.2022.104979_b0225) 2021; 39 Zhou (10.1016/j.tust.2022.104979_b0385) 2022; 7 Du (10.1016/j.tust.2022.104979_b0070) 2022; 39 Yang (10.1016/j.tust.2022.104979_b0320) 2022; 55 Mirjalili (10.1016/j.tust.2022.104979_b0235) 2014; 69 Jang (10.1016/j.tust.2022.104979_b0125) 2013; 38 Saadat (10.1016/j.tust.2022.104979_b0290) 2014; 6 Shaorui (10.1016/j.tust.2022.104979_b9000) 2013; 2013 Houssein (10.1016/j.tust.2022.104979_b0110) 2021; 9 Kutter (10.1016/j.tust.2022.104979_b0180) 1971; 8 Jia (10.1016/j.tust.2022.104979_b0130) 2021; 2 Lin (10.1016/j.tust.2022.104979_b0195) 1998 Li (10.1016/j.tust.2022.104979_b0190) 2021; 108 Breiman (10.1016/j.tust.2022.104979_b0035) 2001; 45 Mottahedi (10.1016/j.tust.2022.104979_b0250) 2018; 34 Yang (10.1016/j.tust.2022.104979_b0335) 2014; 69 Mohammadi (10.1016/j.tust.2022.104979_b0240) 2018; 36 Yang (10.1016/j.tust.2022.104979_b0325) 2020 Goldbogen (10.1016/j.tust.2022.104979_b0090) 2013; 63 Beni (10.1016/j.tust.2022.104979_b0025) 1993 Mirjalili (10.1016/j.tust.2022.104979_b0230) 2016; 95 Zhou (10.1016/j.tust.2022.104979_b0370) 2021; 30 Liu (10.1016/j.tust.2022.104979_b0205) 2020; 145 10.1016/j.tust.2022.104979_b0380 Hong (10.1016/j.tust.2022.104979_b0105) 2011; 11 10.1016/j.tust.2022.104979_b0020 Karniadakis (10.1016/j.tust.2022.104979_b0140) 2021; 3 10.1016/j.tust.2022.104979_b0065 |
| References_xml | – volume: 34 start-page: 45 year: 2018 end-page: 58 ident: b0250 article-title: Development of overbreak prediction models in drill and blast tunneling using soft computing methods publication-title: Eng. Comput. – volume: 60 start-page: 155 year: 1979 end-page: 163 ident: b0305 article-title: Aerial observation of feeding behavior in four baleen whales: eubalaena glacialis, balaenoptera borealis, megaptera novaeangliae, and balaenoptera physalus publication-title: J. Mammal. – volume: 1 start-page: 14 year: 2011 end-page: 23 ident: b0215 article-title: Classification and regression trees publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. – reference: Karir, D., Ray, A., Bharati, A.K., Chaturvedi, U., Rai, R., Khandelwal, M., 2022. Stability prediction of a natural and man-made slope using various machine learning algorithms. Transp. Geotech. 100745. – volume: 38 start-page: 161 year: 2013 end-page: 169 ident: b0125 article-title: Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network publication-title: Tunn. Undergr. Sp. Technol. – start-page: 317 year: 1998 end-page: 327 ident: b0195 article-title: An introduction of the chinese standard for engineering classification of rock masses (GB50218-94) publication-title: Adv. rock Mech. – volume: 24 start-page: 208 year: 2009 end-page: 221 ident: b0285 article-title: Damage assessment of basaltic rock mass due to repeated blasting in a railway tunnelling project - a case study publication-title: Tunn. Undergr. Sp. Technol. – volume: 10 year: 2020 ident: b0355 article-title: A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration publication-title: Appl. Sci. – volume: 8 start-page: 181 year: 1971 end-page: 202 ident: b0180 article-title: On the fracture process in blasting publication-title: Int. J. Rock Mech. Min. Sci. – volume: 53 start-page: 799 year: 2020 end-page: 813 ident: b0200 article-title: Effect of water content on argillization of mudstone during the tunnelling process publication-title: Rock Mech. Rock Eng. – start-page: 703 year: 1993 end-page: 712 ident: b0025 article-title: Swarm intelligence in cellular robotic systems publication-title: Robot. Biol. Syst. Towar. a New Bionics? – reference: Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.-T., Kardani, N., Armaghani, D.J., 2021. Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp. Geotech. 100588. – volume: 145 year: 2020 ident: b0205 article-title: A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems publication-title: Expert Syst. Appl. – volume: 833 year: 2021 ident: b0080 article-title: A quantitative approach to predict tunnel overbreak based on the Q-system publication-title: IOP Conf. Ser. Earth Environ. Sci. – volume: 78 start-page: 981 year: 2019 end-page: 990 ident: b0170 article-title: A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels publication-title: Bull. Eng. Geol. Environ. – volume: 35 start-page: 1191 year: 2019 end-page: 1202 ident: b0165 article-title: Overbreak prediction and optimization in tunnel using neural network and bee colony techniques publication-title: Eng. Comput. – volume: 9 start-page: 1 year: 2019 end-page: 19 ident: b0310 article-title: Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate publication-title: Appl. Sci. – volume: 36 start-page: 425 year: 2018 end-page: 437 ident: b0240 article-title: Prediction of blast-induced overbreak based on geo-mechanical parameters, blasting factors and the area of tunnel face publication-title: Geotech. Geol. Eng. – volume: 63 start-page: 29 year: 2017 end-page: 43 ident: b0015 article-title: Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition publication-title: Tunn. Undergr. Sp. Technol. – volume: 21 start-page: 763 year: 2012 end-page: 770 ident: b0030 article-title: An intelligent approach to evaluate drilling performance publication-title: Neural Comput. Appl. – volume: 80 start-page: 2249 year: 2021 end-page: 2260 ident: b0050 article-title: A modified rock mass classification considering seismic effects in the basic quality (BQ) system publication-title: Bull. Eng. Geol. Environ. – volume: 13 year: 2021 ident: b0350 article-title: Optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation publication-title: Sustain. – volume: 34 year: 2001 ident: b0100 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – volume: 32 start-page: 23 year: 2018 end-page: 36 ident: b0340 article-title: Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score publication-title: Electron. Commer. Res. Appl. – reference: Oshiro, T.M., Perez, P.S., 2012. How Many Trees in a Random Forest? 7376. <https://doi.org/10.1007/978-3-642-31537-4>. – volume: 14 start-page: 307 year: 1996 end-page: 323 ident: b0220 article-title: Overbreak and underbreak in underground openings Part 1: Measurement using the light sectioning method and digital image processing publication-title: Geotech. Geol. Eng. – volume: 70 start-page: 363 year: 2017 end-page: 374 ident: b0210 article-title: Optimization of smooth blasting parameters for mountain tunnel construction with specified control indices based on a GA and ISVR coupling algorithm publication-title: Tunn. Undergr. Sp. Technol. – volume: 39 start-page: 5309 year: 2021 end-page: 5323 ident: b0225 article-title: Optimization methods of blasting parameters of large cross-section tunnel in horizontal layered rock mass publication-title: Geotech. Geol. Eng. – volume: 25 start-page: 439 year: 2015 end-page: 445 ident: b0245 article-title: Use of fuzzy set theory for minimizing overbreak in underground blasting operations – a case study of Alborz Tunnel publication-title: Iran. Int. J. Min. Sci. Technol. – reference: Weinan, E., Han, J., Zhang, L., 2020. Integrating Machine Learning with Physics-Based Modeling 1–23. – volume: 25 start-page: 595 year: 2015 end-page: 599 ident: b0300 article-title: Trend analysis and comparison of basic parameters for tunnel blast design models publication-title: Int. J. Min. Sci. Technol. – volume: 28 start-page: 49 year: 2012 end-page: 56 ident: b0060 article-title: Prediction of blast-induced overbreak from uncontrolled burn-cut blasting in tunnels driven through medium rock class publication-title: Tunn. Undergr. Sp. Technol. – volume: 81 start-page: 112 year: 2018 end-page: 120 ident: b0330 article-title: Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass publication-title: Tunn. Undergr. Sp. Technol. – volume: 39 start-page: 433 year: 2022 end-page: 452 ident: b0070 article-title: Investigating the slurry fluidity and strength characteristics of cemented backfill and strength prediction models by developing hybrid GA-SVR and PSO-SVR publication-title: Min. Metall. Explor. – volume: 100 start-page: 642 year: 2005 end-page: 652 ident: b0295 article-title: Outlier labeling with boxplot procedures publication-title: J. Am. Stat. Assoc. – volume: 103 year: 2020 ident: b0360 article-title: TBM performance prediction with Bayesian optimization and automated machine learning publication-title: Tunn. Undergr. Sp. Technol. – volume: 69 start-page: 59 year: 2014 end-page: 66 ident: b0335 article-title: Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading publication-title: Int. J. Rock Mech. Min. Sci. – reference: Dong, L.J., Li, X.B., Peng, K., 2013. Prediction of rockburst classification using Random Forest. Trans. Nonferrous Met. Soc. China (English Ed.) 23, 472–477. <https://doi.org/10.1016/S1003-6326(13)62487-5>. – volume: 88 start-page: 192 year: 2011 end-page: 197 ident: b0265 article-title: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations publication-title: Behav. Process. – volume: 6 start-page: 67 year: 2014 end-page: 76 ident: b0290 article-title: An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine publication-title: Iran. J. Rock Mech. Geotech. Eng. – volume: 92 year: 2019 ident: b0120 article-title: An empirical approach of overbreak resistance factor for tunnel blasting publication-title: Tunn. Undergr. Sp. Technol. – volume: 6 start-page: 379 year: 2021 end-page: 395 ident: b0185 article-title: Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques publication-title: Undergr. Sp. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b0230 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – volume: 96 start-page: 141 year: 2008 end-page: 158 ident: b0390 article-title: Prediction of uniaxial compressive strength of sandstones using petrography-based models publication-title: Eng. Geol. – volume: 108 year: 2021 ident: b0190 article-title: Improved tunicate swarm algorithm: solving the dynamic economic emission dispatch problems publication-title: Appl. Soft Comput. – volume: 97 year: 2021 ident: b0375 article-title: Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate publication-title: Eng. Appl. Artif. Intell. – volume: 30 start-page: 4753 year: 2021 end-page: 4771 ident: b0370 article-title: Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations publication-title: Nat. Resour. Res. – volume: 55 start-page: 1499 year: 2022 end-page: 1516 ident: b0320 article-title: Automated recognition model of geomechanical information based on operational data of tunneling boring machines publication-title: Rock Mech. Rock Eng. – volume: 7 year: 2022 ident: b0385 article-title: Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm publication-title: Acta Geotech. – volume: 14 start-page: 325 year: 1996 end-page: 340 ident: b0115 article-title: Overbreak and underbreak in underground openings Part 2: Causes and implications publication-title: Geotech. Geol. Eng. – volume: 90 year: 2020 ident: b0150 article-title: Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization publication-title: Eng. Appl. Artif. Intell. – volume: 115 year: 2021 ident: b0040 article-title: Experimental and numerical investigation on overbreak control considering the influence of initial support in tunnels publication-title: Tunn. Undergr. Sp. Technol. – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b0235 article-title: Grey wolf optimizer publication-title: Adv. Eng. Softw. – volume: 12 start-page: 469 year: 2021 end-page: 477 ident: b0365 article-title: Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization publication-title: Geosci. Front. – volume: 1 start-page: 69 year: 2015 end-page: 77 ident: b0155 article-title: Evaluation of dump slope stability of a coal mine using artificial neural network publication-title: Geomech. Geophys. Geo-Energy Geo-Resources – volume: 9 start-page: 56066 year: 2021 end-page: 56092 ident: b0110 article-title: An improved tunicate swarm algorithm for global optimization and image segmentation publication-title: IEEE Access – volume: 2 year: 2021 ident: b0175 article-title: Prediction of back break in blasting using random decision trees publication-title: Eng. Comput. – volume: 24 start-page: 44 year: 2015 end-page: 65 ident: b0095 article-title: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation publication-title: J. Comput. Graph. Stat. – volume: 43 start-page: 113 year: 2014 end-page: 122 ident: b0345 article-title: An application of the modified Holmberg-Persson approach for tunnel blasting design publication-title: Tunn. Undergr. Sp. Technol. – volume: 10 year: 2022 ident: b0045 article-title: Optimization of genetic algorithm through use of back propagation neural network in forecasting smooth wall blasting parameters publication-title: Mathematics – volume: 105 year: 2020 ident: b0085 article-title: Factors influencing overbreak volumes in drill-and-blast tunnel excavation. A statistical analysis applied to the case study of the Brenner Base Tunnel – BBT publication-title: Tunn. Undergr. Sp. Technol. – volume: 9 start-page: 26766 year: 2021 end-page: 26791 ident: b0005 article-title: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019) publication-title: IEEE Access – year: 2021 ident: b0010 article-title: An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity publication-title: Artif. Intell. Rev. – volume: 80 start-page: 1 year: 2018 end-page: 9 ident: b0255 article-title: Overbreak prediction in underground excavations using hybrid ANFIS-PSO model publication-title: Tunn. Undergr. Sp. Technol. – volume: 63 start-page: 90 year: 2013 end-page: 100 ident: b0090 article-title: Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology publication-title: Bioscience – volume: 3 start-page: 422 year: 2021 end-page: 440 ident: b0140 article-title: Physics-informed machine learning publication-title: Nat. Rev. Phys. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0035 article-title: Random forests publication-title: Mach. Learn. – volume: 2 start-page: 1 year: 2021 end-page: 26 ident: b0130 article-title: Physics-guided machine learning for scientific discovery: an application in simulating lake temperature profiles publication-title: ACM/IMS Trans. Data Sci. – year: 2020 ident: b0325 article-title: A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance publication-title: Eng. Comput. – volume: 2013 year: 2013 ident: b9000 article-title: Predictions of overbreak blocks in tunnels based on the wavelet neural network method and the geological statistics theory publication-title: Math. Probl. Eng. – volume: 29 start-page: 2318 year: 2017 end-page: 2331 ident: b0145 article-title: Theory-guided data science: a new paradigm for scientific discovery from data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 28 start-page: 679 year: 2018 end-page: 684 ident: b0055 article-title: Prediction of overbreak depth in Ghalaje road tunnel using strength factor publication-title: Int. J. Min. Sci. Technol. – volume: 5 start-page: 127 year: 2017 end-page: 135 ident: b0160 article-title: Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest publication-title: J. AI Data Min. – volume: 34 start-page: 672 year: 2020 end-page: 685 ident: b0315 article-title: Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability publication-title: Int. J. Mining Reclam. Environ. – reference: Zhou, J., Shi, X., Du, K., Qiu, X., Li, X., Mitri, H.S., 2016. Development of Ground Movements Due to a Shield Tunnelling Prediction Model Using Random Forests 108–115. <https://doi.org/10.1061/9780784480106.014>. – volume: 11 start-page: 1881 year: 2011 end-page: 1890 ident: b0105 article-title: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting publication-title: Appl. Soft Comput. J. – volume: 10 year: 2022 ident: 10.1016/j.tust.2022.104979_b0045 article-title: Optimization of genetic algorithm through use of back propagation neural network in forecasting smooth wall blasting parameters publication-title: Mathematics – volume: 105 year: 2020 ident: 10.1016/j.tust.2022.104979_b0085 article-title: Factors influencing overbreak volumes in drill-and-blast tunnel excavation. A statistical analysis applied to the case study of the Brenner Base Tunnel – BBT publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2020.103475 – volume: 9 start-page: 56066 year: 2021 ident: 10.1016/j.tust.2022.104979_b0110 article-title: An improved tunicate swarm algorithm for global optimization and image segmentation publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3072336 – volume: 53 start-page: 799 year: 2020 ident: 10.1016/j.tust.2022.104979_b0200 article-title: Effect of water content on argillization of mudstone during the tunnelling process publication-title: Rock Mech. Rock Eng. doi: 10.1007/s00603-019-01947-w – volume: 39 start-page: 433 year: 2022 ident: 10.1016/j.tust.2022.104979_b0070 article-title: Investigating the slurry fluidity and strength characteristics of cemented backfill and strength prediction models by developing hybrid GA-SVR and PSO-SVR publication-title: Min. Metall. Explor. – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.tust.2022.104979_b0230 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 11 start-page: 1881 year: 2011 ident: 10.1016/j.tust.2022.104979_b0105 article-title: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2010.06.003 – volume: 5 start-page: 127 year: 2017 ident: 10.1016/j.tust.2022.104979_b0160 article-title: Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest publication-title: J. AI Data Min. – volume: 88 start-page: 192 year: 2011 ident: 10.1016/j.tust.2022.104979_b0265 article-title: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations publication-title: Behav. Process. doi: 10.1016/j.beproc.2011.09.006 – volume: 80 start-page: 2249 year: 2021 ident: 10.1016/j.tust.2022.104979_b0050 article-title: A modified rock mass classification considering seismic effects in the basic quality (BQ) system publication-title: Bull. Eng. Geol. Environ. doi: 10.1007/s10064-020-02064-7 – ident: 10.1016/j.tust.2022.104979_b0270 doi: 10.1007/978-3-642-31537-4_13 – volume: 13 year: 2021 ident: 10.1016/j.tust.2022.104979_b0350 article-title: Optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation publication-title: Sustain. – start-page: 317 year: 1998 ident: 10.1016/j.tust.2022.104979_b0195 article-title: An introduction of the chinese standard for engineering classification of rock masses (GB50218-94) publication-title: Adv. rock Mech. doi: 10.1142/9789812839640_0029 – ident: 10.1016/j.tust.2022.104979_b0020 doi: 10.1016/j.trgeo.2021.100588 – volume: 2013 year: 2013 ident: 10.1016/j.tust.2022.104979_b9000 article-title: Predictions of overbreak blocks in tunnels based on the wavelet neural network method and the geological statistics theory publication-title: Math. Probl. Eng. doi: 10.1155/2013/706491 – volume: 9 start-page: 1 year: 2019 ident: 10.1016/j.tust.2022.104979_b0310 article-title: Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate publication-title: Appl. Sci. – volume: 6 start-page: 379 year: 2021 ident: 10.1016/j.tust.2022.104979_b0185 article-title: Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques publication-title: Undergr. Sp. doi: 10.1016/j.undsp.2020.05.005 – volume: 55 start-page: 1499 year: 2022 ident: 10.1016/j.tust.2022.104979_b0320 article-title: Automated recognition model of geomechanical information based on operational data of tunneling boring machines publication-title: Rock Mech. Rock Eng. doi: 10.1007/s00603-021-02723-5 – volume: 108 year: 2021 ident: 10.1016/j.tust.2022.104979_b0190 article-title: Improved tunicate swarm algorithm: solving the dynamic economic emission dispatch problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107504 – volume: 7 year: 2022 ident: 10.1016/j.tust.2022.104979_b0385 article-title: Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm publication-title: Acta Geotech. – volume: 92 year: 2019 ident: 10.1016/j.tust.2022.104979_b0120 article-title: An empirical approach of overbreak resistance factor for tunnel blasting publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2019.103060 – volume: 60 start-page: 155 year: 1979 ident: 10.1016/j.tust.2022.104979_b0305 article-title: Aerial observation of feeding behavior in four baleen whales: eubalaena glacialis, balaenoptera borealis, megaptera novaeangliae, and balaenoptera physalus publication-title: J. Mammal. doi: 10.2307/1379766 – volume: 39 start-page: 5309 year: 2021 ident: 10.1016/j.tust.2022.104979_b0225 article-title: Optimization methods of blasting parameters of large cross-section tunnel in horizontal layered rock mass publication-title: Geotech. Geol. Eng. doi: 10.1007/s10706-021-01834-8 – volume: 3 start-page: 422 year: 2021 ident: 10.1016/j.tust.2022.104979_b0140 article-title: Physics-informed machine learning publication-title: Nat. Rev. Phys. doi: 10.1038/s42254-021-00314-5 – volume: 29 start-page: 2318 year: 2017 ident: 10.1016/j.tust.2022.104979_b0145 article-title: Theory-guided data science: a new paradigm for scientific discovery from data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2017.2720168 – volume: 25 start-page: 595 year: 2015 ident: 10.1016/j.tust.2022.104979_b0300 article-title: Trend analysis and comparison of basic parameters for tunnel blast design models publication-title: Int. J. Min. Sci. Technol. doi: 10.1016/j.ijmst.2015.05.012 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.tust.2022.104979_b0235 article-title: Grey wolf optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – volume: 35 start-page: 1191 year: 2019 ident: 10.1016/j.tust.2022.104979_b0165 article-title: Overbreak prediction and optimization in tunnel using neural network and bee colony techniques publication-title: Eng. Comput. doi: 10.1007/s00366-018-0658-7 – volume: 80 start-page: 1 year: 2018 ident: 10.1016/j.tust.2022.104979_b0255 article-title: Overbreak prediction in underground excavations using hybrid ANFIS-PSO model publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2018.05.023 – volume: 2 start-page: 1 year: 2021 ident: 10.1016/j.tust.2022.104979_b0130 article-title: Physics-guided machine learning for scientific discovery: an application in simulating lake temperature profiles publication-title: ACM/IMS Trans. Data Sci. doi: 10.1145/3447814 – volume: 833 year: 2021 ident: 10.1016/j.tust.2022.104979_b0080 article-title: A quantitative approach to predict tunnel overbreak based on the Q-system publication-title: IOP Conf. Ser. Earth Environ. Sci. doi: 10.1088/1755-1315/833/1/012165 – volume: 24 start-page: 44 year: 2015 ident: 10.1016/j.tust.2022.104979_b0095 article-title: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation publication-title: J. Comput. Graph. Stat. doi: 10.1080/10618600.2014.907095 – volume: 70 start-page: 363 year: 2017 ident: 10.1016/j.tust.2022.104979_b0210 article-title: Optimization of smooth blasting parameters for mountain tunnel construction with specified control indices based on a GA and ISVR coupling algorithm publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2017.09.007 – volume: 1 start-page: 69 year: 2015 ident: 10.1016/j.tust.2022.104979_b0155 article-title: Evaluation of dump slope stability of a coal mine using artificial neural network publication-title: Geomech. Geophys. Geo-Energy Geo-Resources doi: 10.1007/s40948-015-0009-8 – year: 2021 ident: 10.1016/j.tust.2022.104979_b0010 article-title: An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity publication-title: Artif. Intell. Rev. – start-page: 703 year: 1993 ident: 10.1016/j.tust.2022.104979_b0025 article-title: Swarm intelligence in cellular robotic systems publication-title: Robot. Biol. Syst. Towar. a New Bionics? doi: 10.1007/978-3-642-58069-7_38 – volume: 2 year: 2021 ident: 10.1016/j.tust.2022.104979_b0175 article-title: Prediction of back break in blasting using random decision trees publication-title: Eng. Comput. – ident: 10.1016/j.tust.2022.104979_b0380 doi: 10.1061/9780784480106.014 – volume: 96 start-page: 141 year: 2008 ident: 10.1016/j.tust.2022.104979_b0390 article-title: Prediction of uniaxial compressive strength of sandstones using petrography-based models publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2007.10.009 – volume: 63 start-page: 29 year: 2017 ident: 10.1016/j.tust.2022.104979_b0015 article-title: Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2016.12.009 – volume: 115 year: 2021 ident: 10.1016/j.tust.2022.104979_b0040 article-title: Experimental and numerical investigation on overbreak control considering the influence of initial support in tunnels publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2021.104017 – ident: 10.1016/j.tust.2022.104979_b0065 doi: 10.1016/S1003-6326(13)62487-5 – volume: 145 year: 2020 ident: 10.1016/j.tust.2022.104979_b0205 article-title: A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.113134 – volume: 12 start-page: 469 year: 2021 ident: 10.1016/j.tust.2022.104979_b0365 article-title: Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization publication-title: Geosci. Front. doi: 10.1016/j.gsf.2020.03.007 – volume: 32 start-page: 23 year: 2018 ident: 10.1016/j.tust.2022.104979_b0340 article-title: Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score publication-title: Electron. Commer. Res. Appl. doi: 10.1016/j.elerap.2018.10.004 – volume: 6 start-page: 67 year: 2014 ident: 10.1016/j.tust.2022.104979_b0290 article-title: An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine publication-title: Iran. J. Rock Mech. Geotech. Eng. doi: 10.1016/j.jrmge.2013.11.001 – volume: 28 start-page: 679 year: 2018 ident: 10.1016/j.tust.2022.104979_b0055 article-title: Prediction of overbreak depth in Ghalaje road tunnel using strength factor publication-title: Int. J. Min. Sci. Technol. doi: 10.1016/j.ijmst.2018.04.013 – volume: 14 start-page: 307 year: 1996 ident: 10.1016/j.tust.2022.104979_b0220 article-title: Overbreak and underbreak in underground openings Part 1: Measurement using the light sectioning method and digital image processing publication-title: Geotech. Geol. Eng. doi: 10.1007/BF00421946 – volume: 63 start-page: 90 year: 2013 ident: 10.1016/j.tust.2022.104979_b0090 article-title: Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology publication-title: Bioscience doi: 10.1525/bio.2013.63.2.5 – volume: 78 start-page: 981 year: 2019 ident: 10.1016/j.tust.2022.104979_b0170 article-title: A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels publication-title: Bull. Eng. Geol. Environ. doi: 10.1007/s10064-017-1116-2 – ident: 10.1016/j.tust.2022.104979_b0135 doi: 10.1016/j.trgeo.2022.100745 – volume: 14 start-page: 325 year: 1996 ident: 10.1016/j.tust.2022.104979_b0115 article-title: Overbreak and underbreak in underground openings Part 2: Causes and implications publication-title: Geotech. Geol. Eng. doi: 10.1007/BF00421947 – volume: 38 start-page: 161 year: 2013 ident: 10.1016/j.tust.2022.104979_b0125 article-title: Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2013.06.003 – volume: 30 start-page: 4753 year: 2021 ident: 10.1016/j.tust.2022.104979_b0370 article-title: Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations publication-title: Nat. Resour. Res. doi: 10.1007/s11053-021-09929-y – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.tust.2022.104979_b0035 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 25 start-page: 439 year: 2015 ident: 10.1016/j.tust.2022.104979_b0245 article-title: Use of fuzzy set theory for minimizing overbreak in underground blasting operations – a case study of Alborz Tunnel publication-title: Iran. Int. J. Min. Sci. Technol. doi: 10.1016/j.ijmst.2015.03.018 – volume: 69 start-page: 59 year: 2014 ident: 10.1016/j.tust.2022.104979_b0335 article-title: Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading publication-title: Int. J. Rock Mech. Min. Sci. doi: 10.1016/j.ijrmms.2014.03.003 – volume: 43 start-page: 113 year: 2014 ident: 10.1016/j.tust.2022.104979_b0345 article-title: An application of the modified Holmberg-Persson approach for tunnel blasting design publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2014.04.009 – volume: 36 start-page: 425 year: 2018 ident: 10.1016/j.tust.2022.104979_b0240 article-title: Prediction of blast-induced overbreak based on geo-mechanical parameters, blasting factors and the area of tunnel face publication-title: Geotech. Geol. Eng. doi: 10.1007/s10706-017-0336-3 – volume: 1 start-page: 14 year: 2011 ident: 10.1016/j.tust.2022.104979_b0215 article-title: Classification and regression trees publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.8 – volume: 8 start-page: 181 year: 1971 ident: 10.1016/j.tust.2022.104979_b0180 article-title: On the fracture process in blasting publication-title: Int. J. Rock Mech. Min. Sci. doi: 10.1016/0148-9062(71)90018-0 – ident: 10.1016/j.tust.2022.104979_b0075 – volume: 90 year: 2020 ident: 10.1016/j.tust.2022.104979_b0150 article-title: Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103541 – volume: 10 year: 2020 ident: 10.1016/j.tust.2022.104979_b0355 article-title: A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration publication-title: Appl. Sci. – volume: 100 start-page: 642 year: 2005 ident: 10.1016/j.tust.2022.104979_b0295 article-title: Outlier labeling with boxplot procedures publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214504000001466 – volume: 34 year: 2001 ident: 10.1016/j.tust.2022.104979_b0100 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – volume: 103 year: 2020 ident: 10.1016/j.tust.2022.104979_b0360 article-title: TBM performance prediction with Bayesian optimization and automated machine learning publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2020.103493 – volume: 81 start-page: 112 year: 2018 ident: 10.1016/j.tust.2022.104979_b0330 article-title: Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2018.07.023 – volume: 34 start-page: 672 year: 2020 ident: 10.1016/j.tust.2022.104979_b0315 article-title: Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability publication-title: Int. J. Mining Reclam. Environ. doi: 10.1080/17480930.2019.1709012 – year: 2020 ident: 10.1016/j.tust.2022.104979_b0325 article-title: A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance publication-title: Eng. Comput. – volume: 21 start-page: 763 year: 2012 ident: 10.1016/j.tust.2022.104979_b0030 article-title: An intelligent approach to evaluate drilling performance publication-title: Neural Comput. Appl. doi: 10.1007/s00521-010-0457-6 – volume: 28 start-page: 49 year: 2012 ident: 10.1016/j.tust.2022.104979_b0060 article-title: Prediction of blast-induced overbreak from uncontrolled burn-cut blasting in tunnels driven through medium rock class publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2011.09.004 – volume: 9 start-page: 26766 year: 2021 ident: 10.1016/j.tust.2022.104979_b0005 article-title: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019) publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3056407 – volume: 97 year: 2021 ident: 10.1016/j.tust.2022.104979_b0375 article-title: Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.104015 – volume: 24 start-page: 208 year: 2009 ident: 10.1016/j.tust.2022.104979_b0285 article-title: Damage assessment of basaltic rock mass due to repeated blasting in a railway tunnelling project - a case study publication-title: Tunn. Undergr. Sp. Technol. doi: 10.1016/j.tust.2008.08.002 – volume: 34 start-page: 45 year: 2018 ident: 10.1016/j.tust.2022.104979_b0250 article-title: Development of overbreak prediction models in drill and blast tunneling using soft computing methods publication-title: Eng. Comput. doi: 10.1007/s00366-017-0520-3 |
| SSID | ssj0005229 |
| Score | 2.5876167 |
| Snippet | [Display omitted]
•Three hybrid RF models were developed to predict blast-induced overbreak.•The RF-TSA model outperformed other predictive RF models in... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 104979 |
| SubjectTerms | Metaheuristic algorithms Overbreak: random forest Tunicate swarm algorithm Tunnel blasting |
| Title | Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach |
| URI | https://dx.doi.org/10.1016/j.tust.2022.104979 |
| Volume | 133 |
| WOSCitedRecordID | wos000989243700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1878-4364 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005229 issn: 0886-7798 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9swEBeh3cP2MPbJuq5DD3szLv6QbalvaenI9lAGzSBvRpbkNV3qlMwp3X_fO5_tmLSUbbAXk5jIMrpfTnen-90x9ikJXZYqK_ygELEvQqN96bCQayhNGRhbSBc0zSayszM5m6lvo9Hvjgtzs8iqSt7equv_Kmq4B8JG6uxfiLt_KNyAzyB0uILY4fpHgh_3tTab8_81JrJ4BRjJmODsgwu-xiN_zNwEb1j_JGp6Bd-RRlLrC7em4s0-bnDWg73MLq8wGxH2j74E-dCmnTZTLDq2I7LSVkgWwYg8eOTOq--F7ydNGPV4rpcDwOkfF9RgquG9Y3wSXsb2GUPUN_tcz71JWy28DVZE8SZbiyJoHYtmk7JEii4FK5-6UR86UsQSACNiqnDea2qqmXFP61MA4vKwRpYKTBvhybWiJjVb1bTPcTKcK8JYSoSVZHejLFGgEHfHX05nXwf5QU2Lu_7lWsYVJQduz_SwVTOwVKYv2PPWxeBjgsZLNnLVK_ZsUHjyNTMbkPBlyQkkfBskvAfJER_zBiL8AYhwgggniPAOIm_Y98-n05OJ37bb8E0cBLUvE-fA3HUSXPLCxUIYkYG-Fkliy0KXoSqtBXNRqqwokqIUqUylkVJpsPgDFwbxW7ZTLSv3jvEsAk8AnqeEKURqhdTwp5dxGSCNW1u5x8JuuXLT1qLHliiLvEs6vMxxiXNc4pyWeI95_ZhrqsTy6K-TTgp5a0uSjZgDaB4Z9_4fx-2zpxu8f2A79WrtDtgTc1PPf60-tti6A7w1nD8 |
| linkProvider | Elsevier |
| 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=Assessment+of+tunnel+blasting-induced+overbreak%3A+A+novel+metaheuristic-based+random+forest+approach&rft.jtitle=Tunnelling+and+underground+space+technology&rft.au=He%2C+Biao&rft.au=Armaghani%2C+Danial+Jahed&rft.au=Lai%2C+Sai+Hin&rft.date=2023-03-01&rft.pub=Elsevier+Ltd&rft.issn=0886-7798&rft.eissn=1878-4364&rft.volume=133&rft_id=info:doi/10.1016%2Fj.tust.2022.104979&rft.externalDocID=S0886779822006204 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0886-7798&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0886-7798&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0886-7798&client=summon |