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...
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| Vydáno v: | Engineering fracture mechanics Ročník 276; s. 108914 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.12.2022
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| ISSN: | 0013-7944, 1873-7315 |
| On-line přístup: | Získat plný text |
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| 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. |
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| 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 |
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| 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 |
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| Keywords | Data-driven techniques Fracture load Supervised learning Machine learning algorithm Prediction model Fracture toughness |
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| 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 |
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