Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques
•Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the algorithms of XGBoost, BRegressor, GBM, ERTRegressor (mode I), XGBoost, GBM, ETRegressor, ERTRegressor (mode II), and BRegressor, GBM, ETRegr...
Saved in:
| Published in: | Engineering fracture mechanics Vol. 276; p. 108914 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.12.2022
|
| Subjects: | |
| ISSN: | 0013-7944, 1873-7315 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the algorithms of XGBoost, BRegressor, GBM, ERTRegressor (mode I), XGBoost, GBM, ETRegressor, ERTRegressor (mode II), and BRegressor, GBM, ETRegressor (mixed-mode) had the highest prediction accuracy.•For fracture toughness, the algorithms of BRegressor, ETRegressor, NuSVR, ANNs (mode I), ANNs (mode II), and XGBoost, RDF, BRegressor, ETRegressor, ERTRegressor, ANNs (mixed-mode) had the highest prediction accuracy.•Graphical User Interface (GUI) was developed for fracture prediction.
Machine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian discs with central cracks” were used as a training and testing dataset. The main features of the experimental technique in each specimen are the fracture mode, the tensile strength of the specimen, the inclination of the crack with loading direction, the thickness of specimens and the half-length of the crack. The improved ML algorithms were implemented using Python programming language. The results of the coefficient of restitution (R2) and statistical metrics confirm that the ML algorithms are able to predict the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) with high accuracy. To validate the reliability of the proposed ML-based prediction models, three experimental tests were used. Moreover, the Graphical User Interface (GUI) of the ML-based models was created as a practical tool for estimating the fracture load and fracture toughness for engineering problems. |
|---|---|
| AbstractList | •Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the algorithms of XGBoost, BRegressor, GBM, ERTRegressor (mode I), XGBoost, GBM, ETRegressor, ERTRegressor (mode II), and BRegressor, GBM, ETRegressor (mixed-mode) had the highest prediction accuracy.•For fracture toughness, the algorithms of BRegressor, ETRegressor, NuSVR, ANNs (mode I), ANNs (mode II), and XGBoost, RDF, BRegressor, ETRegressor, ERTRegressor, ANNs (mixed-mode) had the highest prediction accuracy.•Graphical User Interface (GUI) was developed for fracture prediction.
Machine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian discs with central cracks” were used as a training and testing dataset. The main features of the experimental technique in each specimen are the fracture mode, the tensile strength of the specimen, the inclination of the crack with loading direction, the thickness of specimens and the half-length of the crack. The improved ML algorithms were implemented using Python programming language. The results of the coefficient of restitution (R2) and statistical metrics confirm that the ML algorithms are able to predict the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) with high accuracy. To validate the reliability of the proposed ML-based prediction models, three experimental tests were used. Moreover, the Graphical User Interface (GUI) of the ML-based models was created as a practical tool for estimating the fracture load and fracture toughness for engineering problems. |
| ArticleNumber | 108914 |
| Author | Abdi, R. Kazemi, F. Dehestani, A. Nitka, M. |
| Author_xml | – sequence: 1 givenname: A. surname: Dehestani fullname: Dehestani, A. organization: Department of Mining Engineering, Imam Khomeini International University, Qazvin, Iran – sequence: 2 givenname: F. surname: Kazemi fullname: Kazemi, F. organization: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland – sequence: 3 givenname: R. surname: Abdi fullname: Abdi, R. organization: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland – sequence: 4 givenname: M. surname: Nitka fullname: Nitka, M. email: micnitka@pg.edu.pl organization: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland |
| BookMark | eNqNkE1rGzEQhkVwIM7Hf1DvWVfa1X7oVIpp04IhPTRnoZ0d2XJtqR1pDc2vzy7uoeTk0wwzzMO8zy1bhBiQsQ9SrKSQzcf9CsPWkYUjwm5VirKc5p2W6ootZddWRVvJesGWQsip10rdsNuU9kKItunEkr3-IBw8ZB8Dj47PpDwS8hzH7S5gStwH7nxPWBD64CIBDhxiAMKMj_wYKVt65DYMnCL8SnxMPmz5yZKPY-JHCzsfkB_QUpgXefoz-D8jpnt27ewh4cO_esdevn75uf5WbJ6fvq8_bwqoSpmLrnZ17xBa12LT675TskSnOlS6sdWUGBohdakBOyUqLLVyNfR6UDW6anBddcc-nblAMSVCZ8BnOyfOZP3BSGFmlWZv_lNpZpXmrHIi6HeE3-SPlv5edLs-3-IU8eSRTAKPYbLoCSGbIfoLKG9LOJua |
| CitedBy_id | crossref_primary_10_1007_s00603_024_04034_x crossref_primary_10_1016_j_engfracmech_2023_109577 crossref_primary_10_1007_s40571_024_00862_4 crossref_primary_10_1016_j_mtcomm_2023_105547 crossref_primary_10_1016_j_tafmec_2024_104493 crossref_primary_10_1007_s10064_024_03975_5 crossref_primary_10_1016_j_apmt_2025_102601 crossref_primary_10_1007_s10064_024_03629_6 crossref_primary_10_1016_j_tafmec_2025_104975 crossref_primary_10_1038_s41598_025_01327_1 crossref_primary_10_1016_j_engfracmech_2024_110757 crossref_primary_10_1016_j_jobe_2024_109677 crossref_primary_10_1111_exsy_70080 crossref_primary_10_1016_j_engfracmech_2024_110759 crossref_primary_10_1016_j_eswa_2024_124897 crossref_primary_10_1016_j_tafmec_2023_103892 crossref_primary_10_1016_j_istruc_2025_109233 crossref_primary_10_1038_s41598_025_16516_1 crossref_primary_10_1007_s42107_025_01308_9 crossref_primary_10_1515_rams_2023_0162 crossref_primary_10_3390_app15126595 crossref_primary_10_3390_app15147954 crossref_primary_10_1016_j_engfracmech_2023_109677 crossref_primary_10_1016_j_engappai_2023_106387 crossref_primary_10_1016_j_engfracmech_2025_110949 crossref_primary_10_1016_j_ijpvp_2024_105420 crossref_primary_10_1016_j_compstruc_2023_107181 crossref_primary_10_1016_j_compositesa_2024_108287 crossref_primary_10_1016_j_soildyn_2023_107761 crossref_primary_10_1016_j_engappai_2025_111533 crossref_primary_10_1016_j_cscm_2023_e02622 crossref_primary_10_1016_j_jrmge_2025_02_003 crossref_primary_10_1098_rsta_2022_0397 crossref_primary_10_1016_j_engfracmech_2023_109321 crossref_primary_10_1038_s41598_024_62020_3 crossref_primary_10_1007_s40996_025_02006_x crossref_primary_10_1016_j_istruc_2025_108441 crossref_primary_10_1002_eng2_12676 crossref_primary_10_1016_j_engfracmech_2023_109560 crossref_primary_10_1061_JMCEE7_MTENG_19538 crossref_primary_10_1016_j_engfracmech_2025_111449 crossref_primary_10_1016_j_mtcomm_2024_108804 crossref_primary_10_1016_j_mtcomm_2024_108600 crossref_primary_10_1007_s11831_024_10143_1 crossref_primary_10_1007_s11831_023_10043_w crossref_primary_10_1007_s43452_023_00631_9 crossref_primary_10_1016_j_engfracmech_2024_109948 crossref_primary_10_1016_j_tafmec_2025_104966 crossref_primary_10_1016_j_tafmec_2023_103999 crossref_primary_10_1016_j_compstruc_2025_107657 |
| Cites_doi | 10.1023/A:1022627411411 10.1016/j.ijrmms.2009.12.015 10.1111/ffe.12672 10.1016/j.actamat.2020.03.016 10.1016/j.matdes.2017.05.027 10.1109/ICDAR.1995.598994 10.1073/pnas.2104765118 10.1016/j.ijrmms.2015.06.010 10.1016/j.ijrmms.2017.01.017 10.1016/j.compstruc.2022.106886 10.1177/0731684420915984 10.1016/j.engfracmech.2015.11.020 10.1016/j.engfailanal.2014.11.005 10.1073/pnas.1911815116 10.1016/j.tafmec.2019.102448 10.1016/j.measurement.2018.05.069 10.1109/34.709601 10.1016/j.tafmec.2020.102512 10.1016/j.commatsci.2015.02.045 10.1007/b94608_8 10.1016/j.ijrmms.2013.12.009 10.1016/j.petrol.2020.108202 10.1098/rsif.2017.0844 10.1063/5.0012055 10.1016/j.engfracmech.2022.108334 10.1016/j.engfracmech.2021.107890 10.1016/S0013-7944(03)00120-6 10.1016/j.tafmec.2021.102910 10.1016/j.engstruct.2022.114953 10.1016/j.eml.2017.10.001 10.1016/j.engfailanal.2017.07.011 10.1007/BF00015688 10.1061/(ASCE)IS.1943-555X.0000512 10.1023/A:1018054314350 10.1016/0148-9062(94)00015-U 10.1006/jcss.1997.1504 10.1016/j.engfracmech.2020.106907 10.1016/j.ijrmms.2011.06.015 10.1016/j.tafmec.2013.11.008 10.1016/j.tafmec.2021.103185 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd |
| Copyright_xml | – notice: 2022 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.engfracmech.2022.108914 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-7315 |
| ExternalDocumentID | 10_1016_j_engfracmech_2022_108914 S0013794422006324 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 6TJ 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABDEX ABEFU ABFNM ABMAC ABTAH ABXDB ABYKQ ACDAQ ACGFS ACIWK ACNNM ACRLP ADBBV ADEZE ADIYS ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AI. AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SDP SES SET SEW SPC SPCBC SST SSZ T5K TN5 VH1 WUQ XPP ZMT ZY4 ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c321t-85f5bfec7f7e6b9b8412ef48e496a3202c601929ce8403e294f5cb9d45ef3df83 |
| ISICitedReferencesCount | 54 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000898742300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0013-7944 |
| IngestDate | Tue Nov 18 22:11:27 EST 2025 Sat Nov 29 07:28:34 EST 2025 Fri Feb 23 02:39:45 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Data-driven techniques Fracture load Supervised learning Machine learning algorithm Prediction model Fracture toughness |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c321t-85f5bfec7f7e6b9b8412ef48e496a3202c601929ce8403e294f5cb9d45ef3df83 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_engfracmech_2022_108914 crossref_primary_10_1016_j_engfracmech_2022_108914 elsevier_sciencedirect_doi_10_1016_j_engfracmech_2022_108914 |
| PublicationCentury | 2000 |
| PublicationDate | December 2022 2022-12-00 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: December 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Engineering fracture mechanics |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Daghigh, Lacy, Daghigh, Gu, Baghaei, Horstemeyer (b0100) 2020; 39 Cortes, Vapnik (b0155) 1995; 20 Ho TK. Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, IEEE; 1995;(1): p. 278-282. Refaeilzadeh, Tang, Liu (b0120) 2009 Piryonesi, El-Diraby (b0140) 2020; 26 Aliha, Ashtari, Ayatollahi (b0230) 2006; Vol. 5 Hua, Dong, Li, Wang (b0195) 2016; 153 Aliha, Ayatollahi (b0175) 2014; 69 Wiangkham, Ariyarit, Aengchuan (b0090) 2021; 112 Cui, Liu, An, Sun, Zhou, Cao (b0235) 2010; 47 Hua, Dong, Li, Xu, Wang (b0190) 2015; 78 Amrollahi, Baghbanan, Hashemolhosseini (b0225) 2011; 48 Broek (b0010) 1982 Breiman (b0135) 1996; 24 Hudson, Harrison (b0015) 2000 Ho (b0130) 1998; 20 Funatsu, Kuruppu, Matsui (b0185) 2014; 67 Liu, Athanasiou, Padture, Sheldon, Gao (b0035) 2021; 118 Hua, Dong, Peng, Li, Wang (b0200) 2017; 93 Mahmoodzadeh, Nejati, Mohammadi, Hashim Ibrahim, Khishe, Rashidi (b0115) 2022; 264 Hamdia, Lahmer, Nguyen-Thoi, Rabczuk (b0065) 2015; 102 Sharma, Anand Kumar, Kushvaha (b0085) 2020; 228 Nasiri, Khosravani, Weinberg (b0060) 2017; 81 Liu, Jia, Kong, Guan, Zhang (b0075) 2017; 129 Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction; New York: springer; 2009;(2): p. 1-758. Freund, Schapire (b0160) 1997; 55 Kazemi, Asgarkhani, Jankowski (b0170) 2023; 274 Ebrahimi, Hosseini, Taleb beydokhti (b0220) 2022; 117 Wei, Dai, Xu, Zhao (b0240) 2018; 41 Wang, Zhang, Liu (b0110) 2021; 253 Roy, Singh, Kodikara (b0080) 2018; 126 Kazemi, Jankowski (b0165) 2023; 274 Atkinson, Smelser, Sanchez (b0020) 1982; 18 Liang, Liu, Martin, Sun (b0045) 2018; 15 Fowell (b0180) 1995; 32 Nasrabadi NM. Book review: Pattern recognition and machine. Learning; 2007. Mazhnik, Oganov (b0105) 2020; 128 Liu, Athanasiou, Padture, Sheldon, Gao (b0030) 2020; 190 Salavati, Alizadeh, Kazemi-Moridani, Berto (b0095) 2015; 48 Gu, Chen, Buehler (b0050) 2018; 18 Friedman (b0145) 2001 Dong, Wang, Xia (b0025) 2004; 71 Anderson (b0005) 2017 Dehestani, Hosseini, Beydokhti (b0205) 2020; 107 Jorbat, Hosseini, Mahdikhani (b0215) 2020; 109 Mozaffar, Bostanabad, Chen, Ehmann, Cao, Bessa (b0055) 2019; 116 Alipour, Esatyana, Sakhaee-Pour, Sadooni, Al-Kuwari (b0070) 2021; 200 Dehestani, Hosseini, Taleb Beydokhti (b0210) 2020; 106 Mahmoodzadeh (10.1016/j.engfracmech.2022.108914_b0115) 2022; 264 Kazemi (10.1016/j.engfracmech.2022.108914_b0170) 2023; 274 Dehestani (10.1016/j.engfracmech.2022.108914_b0205) 2020; 107 Alipour (10.1016/j.engfracmech.2022.108914_b0070) 2021; 200 Friedman (10.1016/j.engfracmech.2022.108914_b0145) 2001 Dong (10.1016/j.engfracmech.2022.108914_b0025) 2004; 71 Aliha (10.1016/j.engfracmech.2022.108914_b0230) 2006; Vol. 5 Liu (10.1016/j.engfracmech.2022.108914_b0075) 2017; 129 Hua (10.1016/j.engfracmech.2022.108914_b0195) 2016; 153 Daghigh (10.1016/j.engfracmech.2022.108914_b0100) 2020; 39 Wang (10.1016/j.engfracmech.2022.108914_b0110) 2021; 253 Wei (10.1016/j.engfracmech.2022.108914_b0240) 2018; 41 Liu (10.1016/j.engfracmech.2022.108914_b0035) 2021; 118 Refaeilzadeh (10.1016/j.engfracmech.2022.108914_b0120) 2009 Mazhnik (10.1016/j.engfracmech.2022.108914_b0105) 2020; 128 10.1016/j.engfracmech.2022.108914_b0150 Fowell (10.1016/j.engfracmech.2022.108914_b0180) 1995; 32 Anderson (10.1016/j.engfracmech.2022.108914_b0005) 2017 Funatsu (10.1016/j.engfracmech.2022.108914_b0185) 2014; 67 Salavati (10.1016/j.engfracmech.2022.108914_b0095) 2015; 48 Dehestani (10.1016/j.engfracmech.2022.108914_b0210) 2020; 106 Nasiri (10.1016/j.engfracmech.2022.108914_b0060) 2017; 81 10.1016/j.engfracmech.2022.108914_b0125 Liu (10.1016/j.engfracmech.2022.108914_b0030) 2020; 190 Atkinson (10.1016/j.engfracmech.2022.108914_b0020) 1982; 18 Hua (10.1016/j.engfracmech.2022.108914_b0190) 2015; 78 Broek (10.1016/j.engfracmech.2022.108914_b0010) 1982 Kazemi (10.1016/j.engfracmech.2022.108914_b0165) 2023; 274 Freund (10.1016/j.engfracmech.2022.108914_b0160) 1997; 55 Aliha (10.1016/j.engfracmech.2022.108914_b0175) 2014; 69 Roy (10.1016/j.engfracmech.2022.108914_b0080) 2018; 126 Wiangkham (10.1016/j.engfracmech.2022.108914_b0090) 2021; 112 Hudson (10.1016/j.engfracmech.2022.108914_b0015) 2000 Ho (10.1016/j.engfracmech.2022.108914_b0130) 1998; 20 Sharma (10.1016/j.engfracmech.2022.108914_b0085) 2020; 228 Jorbat (10.1016/j.engfracmech.2022.108914_b0215) 2020; 109 Hamdia (10.1016/j.engfracmech.2022.108914_b0065) 2015; 102 Gu (10.1016/j.engfracmech.2022.108914_b0050) 2018; 18 Hua (10.1016/j.engfracmech.2022.108914_b0200) 2017; 93 Amrollahi (10.1016/j.engfracmech.2022.108914_b0225) 2011; 48 Mozaffar (10.1016/j.engfracmech.2022.108914_b0055) 2019; 116 Cui (10.1016/j.engfracmech.2022.108914_b0235) 2010; 47 Liang (10.1016/j.engfracmech.2022.108914_b0045) 2018; 15 Piryonesi (10.1016/j.engfracmech.2022.108914_b0140) 2020; 26 Breiman (10.1016/j.engfracmech.2022.108914_b0135) 1996; 24 Cortes (10.1016/j.engfracmech.2022.108914_b0155) 1995; 20 Ebrahimi (10.1016/j.engfracmech.2022.108914_b0220) 2022; 117 10.1016/j.engfracmech.2022.108914_b0040 |
| References_xml | – start-page: 1189 year: 2001 end-page: 1232 ident: b0145 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat – volume: 69 start-page: 17 year: 2014 end-page: 25 ident: b0175 article-title: Rock fracture toughness study using cracked chevron notched Brazilian disc specimen under pure modes I and II loading–A statistical approach publication-title: Theor Appl Fract Mech – year: 1982 ident: b0010 publication-title: Elementary engineering fracture mechanics – volume: 200 start-page: 108202 year: 2021 ident: b0070 article-title: Characterizing fracture toughness using machine learning publication-title: J Petrol Sci Engng – volume: 20 start-page: 832 year: 1998 end-page: 844 ident: b0130 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans Pattern Anal Mach Intell – volume: Vol. 5 start-page: 181 year: 2006 end-page: 188 ident: b0230 article-title: Mode I and mode II fracture toughness testing for a coarse grain marble publication-title: Applied mechanics and materials – volume: 55 start-page: 119 year: 1997 end-page: 139 ident: b0160 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J Comput Syst Sci – volume: 128 year: 2020 ident: b0105 article-title: Application of MLmethods for predicting new superhard materials publication-title: J Appl Phys – volume: 18 start-page: 19 year: 2018 end-page: 28 ident: b0050 article-title: De novo composite design based on MLalgorithm publication-title: Extreme Mech Lett – start-page: 532 year: 2009 end-page: 538 ident: b0120 publication-title: Encyclopedia of Database Systems – volume: 18 start-page: 279 year: 1982 end-page: 291 ident: b0020 article-title: Combined mode fracture via the cracked Brazilian disk test publication-title: Int J Fract – volume: 228 start-page: 106907 year: 2020 ident: b0085 article-title: Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network publication-title: Engng Fract Mech – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: b0135 article-title: Bagging predictors publication-title: Mach Learn – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b0155 article-title: Support-vector networks publication-title: Mach Learn – volume: 71 start-page: 1135 year: 2004 end-page: 1148 ident: b0025 article-title: Stress intensity factors for central cracked circular disk subjected to compression publication-title: Engng Fract Mech – volume: 118 year: 2021 ident: b0035 article-title: Knowledge extraction and transfer in data-driven fracture mechanics publication-title: Proc Natl Acad Sci – volume: 112 start-page: 102910 year: 2021 ident: b0090 article-title: Prediction of the mixed mode I/II fracture toughness of PMMA by an artificial intelligence approach publication-title: Theor Appl Fract Mech – volume: 107 year: 2020 ident: b0205 article-title: Effect of wetting–drying cycles on mode I and mode II fracture toughness of sandstone in natural (pH= 7) and acidic (pH= 3) environments publication-title: Theor Appl Fract Mech – volume: 264 start-page: 108334 year: 2022 ident: b0115 article-title: Prediction of Mode-I rock fracture toughness using support vector regression with metaheuristic optimization algorithms publication-title: Engng Fract Mech – reference: Ho TK. Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, IEEE; 1995;(1): p. 278-282. – volume: 153 start-page: 143 year: 2016 end-page: 150 ident: b0195 article-title: Effect of cyclic wetting and drying on the pure mode II fracture toughness of sandstone publication-title: Engng Fract Mech – volume: 41 start-page: 197 year: 2018 end-page: 211 ident: b0240 article-title: Experimental and numerical investigation of cracked chevron notched Brazilian disc specimen for fracture toughness testing of rock publication-title: Fatigue Fract Engng Mater Struct – volume: 102 start-page: 304 year: 2015 end-page: 313 ident: b0065 article-title: Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS publication-title: Comput Mater Sci – volume: 274 start-page: 106886 year: 2023 ident: b0165 article-title: Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction publication-title: Comput Struct – reference: Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction; New York: springer; 2009;(2): p. 1-758. – volume: 32 start-page: 57 year: 1995 end-page: 64 ident: b0180 article-title: Suggested method for determining mode I fracture toughness using Cracked Chevron Notched Brazilian Disc (CCNBD) specimens publication-title: Int J Rock Mech Min Sci Geomech Abstr – volume: 78 start-page: 331 year: 2015 end-page: 335 ident: b0190 article-title: The influence of cyclic wetting and drying on the fracture toughness of sandstone publication-title: Int J Rock Mech Min Sci – volume: 47 start-page: 871 year: 2010 end-page: 876 ident: b0235 article-title: A comparison of two ISRM suggested chevron notched specimens for testing mode-I rock fracture toughness publication-title: Int J Rock Mech Min Sci – reference: Nasrabadi NM. Book review: Pattern recognition and machine. Learning; 2007. – volume: 39 start-page: 587 year: 2020 end-page: 598 ident: b0100 article-title: MLpredictions on fracture toughness of multiscale bio-nano-composites publication-title: J Reinf Plast Compos – volume: 190 start-page: 105 year: 2020 end-page: 112 ident: b0030 article-title: A MLapproach to fracture mechanics problems publication-title: Acta Mater – volume: 116 start-page: 26414 year: 2019 end-page: 26420 ident: b0055 article-title: Deep learning predicts path-dependent plasticity publication-title: Proc Natl Acad Sci – volume: 67 start-page: 1 year: 2014 end-page: 8 ident: b0185 article-title: Effects of temperature and confining pressure on mixed-mode (I–II) and mode II fracture toughness of Kimachi sandstone publication-title: Int J Rock Mech Min Sci – volume: 81 start-page: 270 year: 2017 end-page: 293 ident: b0060 article-title: Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review publication-title: Engng Fail Anal – volume: 129 start-page: 210 year: 2017 end-page: 218 ident: b0075 article-title: Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb-Si alloys publication-title: Mater Des – volume: 253 year: 2021 ident: b0110 article-title: MLapproaches to rock fracture mechanics problems: Mode-I fracture toughness determination publication-title: Engng Fract Mech – volume: 106 start-page: 102448 year: 2020 ident: b0210 article-title: Effect of wetting–drying cycles on mode I and mode II fracture toughness of cement mortar and concrete publication-title: Theor Appl Fract Mech – year: 2017 ident: b0005 article-title: Fracture mechanics: fundamentals and applications – year: 2000 ident: b0015 article-title: Engineering rock mechanics: an introduction to the principles – volume: 26 start-page: 04019036 year: 2020 ident: b0140 article-title: Data analytics in asset management: Cost-effective prediction of the pavement condition index publication-title: J Infrastruct Syst – volume: 117 year: 2022 ident: b0220 article-title: Experimental study of effect of number of heating–cooling cycles on mode I and mode II fracture toughness of travertine publication-title: Theor Appl Fract Mech – volume: 274 start-page: 114953 year: 2023 ident: b0170 article-title: Predicting seismic response of SMRFs founded on different soil types using machine learning techniques publication-title: Engng Struct – volume: 15 start-page: 20170844 year: 2018 ident: b0045 article-title: A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis publication-title: J R Soc Interface – volume: 93 start-page: 242 year: 2017 end-page: 249 ident: b0200 article-title: Experimental investigation on the effect of wetting-drying cycles on mixed mode fracture toughness of sandstone publication-title: Int J Rock Mech Min Sci – volume: 48 start-page: 121 year: 2015 end-page: 136 ident: b0095 article-title: A new expression to evaluate the critical fracture load for bainitic functionally graded steels under mixed mode (I+ II) loading publication-title: Engng Fail Anal – volume: 109 year: 2020 ident: b0215 article-title: Effect of polypropylene fibers on the mode I, mode II, and mixed-mode fracture toughness and crack propagation in fiber-reinforced concrete publication-title: Theor Appl Fract Mech – volume: 48 start-page: 1123 year: 2011 end-page: 1134 ident: b0225 article-title: Measuring fracture toughness of crystalline marbles under modes I and II and mixed mode I-II loading conditions using CCNBD and HCCD specimens publication-title: Int J Rock Mech Min Sci – volume: 126 start-page: 231 year: 2018 end-page: 241 ident: b0080 article-title: Predicting mode-I fracture toughness of rocks using soft computing and multiple regression publication-title: Measurement – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 10.1016/j.engfracmech.2022.108914_b0155 article-title: Support-vector networks publication-title: Mach Learn doi: 10.1023/A:1022627411411 – volume: 47 start-page: 871 issue: 5 year: 2010 ident: 10.1016/j.engfracmech.2022.108914_b0235 article-title: A comparison of two ISRM suggested chevron notched specimens for testing mode-I rock fracture toughness publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2009.12.015 – volume: 41 start-page: 197 issue: 1 year: 2018 ident: 10.1016/j.engfracmech.2022.108914_b0240 article-title: Experimental and numerical investigation of cracked chevron notched Brazilian disc specimen for fracture toughness testing of rock publication-title: Fatigue Fract Engng Mater Struct doi: 10.1111/ffe.12672 – volume: 190 start-page: 105 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0030 article-title: A MLapproach to fracture mechanics problems publication-title: Acta Mater doi: 10.1016/j.actamat.2020.03.016 – volume: 129 start-page: 210 year: 2017 ident: 10.1016/j.engfracmech.2022.108914_b0075 article-title: Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb-Si alloys publication-title: Mater Des doi: 10.1016/j.matdes.2017.05.027 – ident: 10.1016/j.engfracmech.2022.108914_b0125 doi: 10.1109/ICDAR.1995.598994 – volume: 118 issue: 23 year: 2021 ident: 10.1016/j.engfracmech.2022.108914_b0035 article-title: Knowledge extraction and transfer in data-driven fracture mechanics publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.2104765118 – volume: 78 start-page: 331 year: 2015 ident: 10.1016/j.engfracmech.2022.108914_b0190 article-title: The influence of cyclic wetting and drying on the fracture toughness of sandstone publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2015.06.010 – start-page: 532 year: 2009 ident: 10.1016/j.engfracmech.2022.108914_b0120 – volume: 93 start-page: 242 year: 2017 ident: 10.1016/j.engfracmech.2022.108914_b0200 article-title: Experimental investigation on the effect of wetting-drying cycles on mixed mode fracture toughness of sandstone publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2017.01.017 – volume: 274 start-page: 106886 year: 2023 ident: 10.1016/j.engfracmech.2022.108914_b0165 article-title: Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction publication-title: Comput Struct doi: 10.1016/j.compstruc.2022.106886 – volume: 39 start-page: 587 issue: 15–16 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0100 article-title: MLpredictions on fracture toughness of multiscale bio-nano-composites publication-title: J Reinf Plast Compos doi: 10.1177/0731684420915984 – volume: Vol. 5 start-page: 181 year: 2006 ident: 10.1016/j.engfracmech.2022.108914_b0230 article-title: Mode I and mode II fracture toughness testing for a coarse grain marble – ident: 10.1016/j.engfracmech.2022.108914_b0040 – volume: 153 start-page: 143 year: 2016 ident: 10.1016/j.engfracmech.2022.108914_b0195 article-title: Effect of cyclic wetting and drying on the pure mode II fracture toughness of sandstone publication-title: Engng Fract Mech doi: 10.1016/j.engfracmech.2015.11.020 – year: 2017 ident: 10.1016/j.engfracmech.2022.108914_b0005 – volume: 48 start-page: 121 year: 2015 ident: 10.1016/j.engfracmech.2022.108914_b0095 article-title: A new expression to evaluate the critical fracture load for bainitic functionally graded steels under mixed mode (I+ II) loading publication-title: Engng Fail Anal doi: 10.1016/j.engfailanal.2014.11.005 – volume: 116 start-page: 26414 issue: 52 year: 2019 ident: 10.1016/j.engfracmech.2022.108914_b0055 article-title: Deep learning predicts path-dependent plasticity publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1911815116 – volume: 106 start-page: 102448 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0210 article-title: Effect of wetting–drying cycles on mode I and mode II fracture toughness of cement mortar and concrete publication-title: Theor Appl Fract Mech doi: 10.1016/j.tafmec.2019.102448 – volume: 126 start-page: 231 year: 2018 ident: 10.1016/j.engfracmech.2022.108914_b0080 article-title: Predicting mode-I fracture toughness of rocks using soft computing and multiple regression publication-title: Measurement doi: 10.1016/j.measurement.2018.05.069 – volume: 20 start-page: 832 issue: 8 year: 1998 ident: 10.1016/j.engfracmech.2022.108914_b0130 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.709601 – volume: 107 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0205 article-title: Effect of wetting–drying cycles on mode I and mode II fracture toughness of sandstone in natural (pH= 7) and acidic (pH= 3) environments publication-title: Theor Appl Fract Mech doi: 10.1016/j.tafmec.2020.102512 – volume: 102 start-page: 304 year: 2015 ident: 10.1016/j.engfracmech.2022.108914_b0065 article-title: Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2015.02.045 – ident: 10.1016/j.engfracmech.2022.108914_b0150 doi: 10.1007/b94608_8 – volume: 67 start-page: 1 year: 2014 ident: 10.1016/j.engfracmech.2022.108914_b0185 article-title: Effects of temperature and confining pressure on mixed-mode (I–II) and mode II fracture toughness of Kimachi sandstone publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2013.12.009 – year: 1982 ident: 10.1016/j.engfracmech.2022.108914_b0010 – volume: 200 start-page: 108202 year: 2021 ident: 10.1016/j.engfracmech.2022.108914_b0070 article-title: Characterizing fracture toughness using machine learning publication-title: J Petrol Sci Engng doi: 10.1016/j.petrol.2020.108202 – year: 2000 ident: 10.1016/j.engfracmech.2022.108914_b0015 – volume: 15 start-page: 20170844 issue: 138 year: 2018 ident: 10.1016/j.engfracmech.2022.108914_b0045 article-title: A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis publication-title: J R Soc Interface doi: 10.1098/rsif.2017.0844 – volume: 128 issue: 7 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0105 article-title: Application of MLmethods for predicting new superhard materials publication-title: J Appl Phys doi: 10.1063/5.0012055 – start-page: 1189 year: 2001 ident: 10.1016/j.engfracmech.2022.108914_b0145 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat – volume: 264 start-page: 108334 year: 2022 ident: 10.1016/j.engfracmech.2022.108914_b0115 article-title: Prediction of Mode-I rock fracture toughness using support vector regression with metaheuristic optimization algorithms publication-title: Engng Fract Mech doi: 10.1016/j.engfracmech.2022.108334 – volume: 253 year: 2021 ident: 10.1016/j.engfracmech.2022.108914_b0110 article-title: MLapproaches to rock fracture mechanics problems: Mode-I fracture toughness determination publication-title: Engng Fract Mech doi: 10.1016/j.engfracmech.2021.107890 – volume: 71 start-page: 1135 issue: 7–8 year: 2004 ident: 10.1016/j.engfracmech.2022.108914_b0025 article-title: Stress intensity factors for central cracked circular disk subjected to compression publication-title: Engng Fract Mech doi: 10.1016/S0013-7944(03)00120-6 – volume: 112 start-page: 102910 year: 2021 ident: 10.1016/j.engfracmech.2022.108914_b0090 article-title: Prediction of the mixed mode I/II fracture toughness of PMMA by an artificial intelligence approach publication-title: Theor Appl Fract Mech doi: 10.1016/j.tafmec.2021.102910 – volume: 274 start-page: 114953 year: 2023 ident: 10.1016/j.engfracmech.2022.108914_b0170 article-title: Predicting seismic response of SMRFs founded on different soil types using machine learning techniques publication-title: Engng Struct doi: 10.1016/j.engstruct.2022.114953 – volume: 109 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0215 article-title: Effect of polypropylene fibers on the mode I, mode II, and mixed-mode fracture toughness and crack propagation in fiber-reinforced concrete publication-title: Theor Appl Fract Mech – volume: 18 start-page: 19 year: 2018 ident: 10.1016/j.engfracmech.2022.108914_b0050 article-title: De novo composite design based on MLalgorithm publication-title: Extreme Mech Lett doi: 10.1016/j.eml.2017.10.001 – volume: 81 start-page: 270 year: 2017 ident: 10.1016/j.engfracmech.2022.108914_b0060 article-title: Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review publication-title: Engng Fail Anal doi: 10.1016/j.engfailanal.2017.07.011 – volume: 18 start-page: 279 issue: 4 year: 1982 ident: 10.1016/j.engfracmech.2022.108914_b0020 article-title: Combined mode fracture via the cracked Brazilian disk test publication-title: Int J Fract doi: 10.1007/BF00015688 – volume: 26 start-page: 04019036 issue: 1 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0140 article-title: Data analytics in asset management: Cost-effective prediction of the pavement condition index publication-title: J Infrastruct Syst doi: 10.1061/(ASCE)IS.1943-555X.0000512 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.engfracmech.2022.108914_b0135 article-title: Bagging predictors publication-title: Mach Learn doi: 10.1023/A:1018054314350 – volume: 32 start-page: 57 issue: 1 year: 1995 ident: 10.1016/j.engfracmech.2022.108914_b0180 article-title: Suggested method for determining mode I fracture toughness using Cracked Chevron Notched Brazilian Disc (CCNBD) specimens publication-title: Int J Rock Mech Min Sci Geomech Abstr doi: 10.1016/0148-9062(94)00015-U – volume: 55 start-page: 119 issue: 1 year: 1997 ident: 10.1016/j.engfracmech.2022.108914_b0160 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J Comput Syst Sci doi: 10.1006/jcss.1997.1504 – volume: 228 start-page: 106907 year: 2020 ident: 10.1016/j.engfracmech.2022.108914_b0085 article-title: Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network publication-title: Engng Fract Mech doi: 10.1016/j.engfracmech.2020.106907 – volume: 48 start-page: 1123 issue: 7 year: 2011 ident: 10.1016/j.engfracmech.2022.108914_b0225 article-title: Measuring fracture toughness of crystalline marbles under modes I and II and mixed mode I-II loading conditions using CCNBD and HCCD specimens publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2011.06.015 – volume: 69 start-page: 17 year: 2014 ident: 10.1016/j.engfracmech.2022.108914_b0175 article-title: Rock fracture toughness study using cracked chevron notched Brazilian disc specimen under pure modes I and II loading–A statistical approach publication-title: Theor Appl Fract Mech doi: 10.1016/j.tafmec.2013.11.008 – volume: 117 year: 2022 ident: 10.1016/j.engfracmech.2022.108914_b0220 article-title: Experimental study of effect of number of heating–cooling cycles on mode I and mode II fracture toughness of travertine publication-title: Theor Appl Fract Mech doi: 10.1016/j.tafmec.2021.103185 |
| SSID | ssj0007680 |
| Score | 2.56179 |
| Snippet | •Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108914 |
| SubjectTerms | Data-driven techniques Fracture load Fracture toughness Machine learning algorithm Prediction model Supervised learning |
| Title | Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques |
| URI | https://dx.doi.org/10.1016/j.engfracmech.2022.108914 |
| Volume | 276 |
| WOSCitedRecordID | wos000898742300001&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: 1873-7315 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007680 issn: 0013-7944 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9swEBdZO8b6MPZV1u4DDfaWysS2bEuwl7C1bGMLZXQjb8Yfp5K2cUvihtI_Z3_pTrIlu91Gu8FeTDiQbPl-ufudfLoj5E0cKxGrkjOJ_obxKASWoyNiUZ5kyP6zojQV-L5_TiYTMZ3K_cHghz0LszpJqkpcXMiz_6pqlKGy9dHZv1C3mxQF-BuVjldUO15vpfj9hf72Yomg0qeg9EeCWrfjMXZtVg0VBsnAFmDKpuoMAIyKkT42rfLmmpG7tE50cMfL4bnZUlhhYK1TZucmAxNsy4nDoasEu7yy09_VOuyeYw76rHEvx_496JZdTWup4dhzHiC7hLmR7TnZOC-N5KuTTGb1saG_X7z-_kUQ9HJBWpvshwytAu_b5CDpW1V_JGRz1PQXg9_sPRx5UB3qheg1ePouXjfmapHta87PpSTabLejtDdVqqdKm6nukPUgiSRazvXxx93pJ-fvMWAb2T4Zeh33yOsui_APz_V7FtRjNgcPyYM2JKHjBkqPyACqx2Sjp7wn5LIDFT1V1CqTOlDRWUWvg4paUO3QBlI7FAFFDaCoARRtAUVbQFELKNoB6in5trd78O4Da7t2sCIM_JqJSEW5giJRCcS5zAX3A1BcAJdxFuI7KGIdVsgCBB-FEEiuoiKXJY9AhaUS4SZZq04reEaoEHEGJbJQ9MG8zKTIkG5xXxdMUsBVvkWEfYtp0Za0151VTtIbtblFAjf0rKnrcptBb62q0pagNsQzRTjePHz7X-75nNzv_jUvyFq9OIeX5G6xqmfLxasWiz8Bqsu8xw |
| 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=Prediction+of+fracture+toughness+in+fibre-reinforced+concrete%2C+mortar%2C+and+rocks+using+various+machine+learning+techniques&rft.jtitle=Engineering+fracture+mechanics&rft.au=Dehestani%2C+A.&rft.au=Kazemi%2C+F.&rft.au=Abdi%2C+R.&rft.au=Nitka%2C+M.&rft.date=2022-12-01&rft.issn=0013-7944&rft.volume=276&rft.spage=108914&rft_id=info:doi/10.1016%2Fj.engfracmech.2022.108914&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_engfracmech_2022_108914 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0013-7944&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0013-7944&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0013-7944&client=summon |