Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete

The present study focuses on producing high-performance eco-efficient alternatives to conventional cement-based composites. The study is divided into two parts. The first part comprises of production of high-strength self-compacting alkali-activated slag concrete (SC-AASC) with GGBFS as a primary bi...

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Veröffentlicht in:Multiscale and Multidisciplinary Modeling, Experiments and Design Jg. 7; H. 3; S. 2901 - 2928
Hauptverfasser: Parhi, Suraj Kumar, Panda, Soumyaranjan, Dwibedy, Saswat, Panigrahi, Saubhagya Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.07.2024
Schlagworte:
Characterization and Evaluation of Materials
Engineering
Mathematical Applications in the Physical Sciences
Mechanical Engineering
Numerical and Computational Physics
Original Paper
Simulation
Solid Mechanics
High-strength
Self-compacting
Sensitivity analysis
GGBFS
XGBoost
Machine learning
Alkali-activated concrete
SHO
ISSN:2520-8160, 2520-8179
Online-Zugang:Volltext
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Abstract The present study focuses on producing high-performance eco-efficient alternatives to conventional cement-based composites. The study is divided into two parts. The first part comprises of production of high-strength self-compacting alkali-activated slag concrete (SC-AASC) with GGBFS as a primary binder. The second part deals with the development of a prediction model to estimate the mechanical strength of developed concrete. In this study, to achieve high-performance SC-AASC, the alkali activator solution content varied from 220 to 190 kg/m 3 , and the AAS/binder ratio varied between 0.47 and 0.36. The SP percentage fluctuated between 6 and 7%, while the additional water percentage was maintained between 21 and 24%. The approach used to obtain the high-performance SC-AASC was found to be competent as all the mix resulted in satisfactory performance for both fresh and hardened properties. For M45 graded SC-AASC, using 200 kg/m 3 of AAS with an AAS/binder ratio of 0.39 resulted in higher strength, while for M60 grade, 190 kg/m 3 of AAS with an AAS/binder ratio of 0.36 yielded stronger concrete. Additionally, a 6% SP and 24% extra water content enhanced workability for both M45 and M60 grade SC-AASC. A database of 135 observations was developed from the experimental study. The compressive strength and split tensile strength of SC-AASC were predicted using six machine-learning algorithms. The hyperparameters of all the models were optimized using the metaheuristic spotted hyena optimization technique. Optimized XGBoost outperformed other models scoring a higher R 2 of 0.97 and lower value of error parameters on both datasets. A comparison was drawn with previously published models to check the efficacy of the developed model. The Sobol and FAST global sensitivity analysis resulted in the AAS/binder ratio, AAS content, GGBFS content, and Curing days being most influential regarding the strength of SC-AASC.
AbstractList The present study focuses on producing high-performance eco-efficient alternatives to conventional cement-based composites. The study is divided into two parts. The first part comprises of production of high-strength self-compacting alkali-activated slag concrete (SC-AASC) with GGBFS as a primary binder. The second part deals with the development of a prediction model to estimate the mechanical strength of developed concrete. In this study, to achieve high-performance SC-AASC, the alkali activator solution content varied from 220 to 190 kg/m 3 , and the AAS/binder ratio varied between 0.47 and 0.36. The SP percentage fluctuated between 6 and 7%, while the additional water percentage was maintained between 21 and 24%. The approach used to obtain the high-performance SC-AASC was found to be competent as all the mix resulted in satisfactory performance for both fresh and hardened properties. For M45 graded SC-AASC, using 200 kg/m 3 of AAS with an AAS/binder ratio of 0.39 resulted in higher strength, while for M60 grade, 190 kg/m 3 of AAS with an AAS/binder ratio of 0.36 yielded stronger concrete. Additionally, a 6% SP and 24% extra water content enhanced workability for both M45 and M60 grade SC-AASC. A database of 135 observations was developed from the experimental study. The compressive strength and split tensile strength of SC-AASC were predicted using six machine-learning algorithms. The hyperparameters of all the models were optimized using the metaheuristic spotted hyena optimization technique. Optimized XGBoost outperformed other models scoring a higher R 2 of 0.97 and lower value of error parameters on both datasets. A comparison was drawn with previously published models to check the efficacy of the developed model. The Sobol and FAST global sensitivity analysis resulted in the AAS/binder ratio, AAS content, GGBFS content, and Curing days being most influential regarding the strength of SC-AASC.
Author Parhi, Suraj Kumar
Panda, Soumyaranjan
Dwibedy, Saswat
Panigrahi, Saubhagya Kumar
Author_xml – sequence: 1
  givenname: Suraj Kumar
  surname: Parhi
  fullname: Parhi, Suraj Kumar
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  givenname: Soumyaranjan
  surname: Panda
  fullname: Panda, Soumyaranjan
  organization: Department of Civil Engineering, VSSUT
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  givenname: Saswat
  surname: Dwibedy
  fullname: Dwibedy, Saswat
  organization: Department of Civil Engineering, VSSUT
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  givenname: Saubhagya Kumar
  surname: Panigrahi
  fullname: Panigrahi, Saubhagya Kumar
  email: skpanigrahi_ce@vssut.ac.in
  organization: Department of Civil Engineering, VSSUT
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Cites_doi 10.1109/TKDE.2005.31
10.1007/s42107-023-00799-8
10.1016/j.conbuildmat.2023.134129
10.1016/j.conbuildmat.2020.121117
10.3390/su13010135
10.1007/978-3-642-41136-6_5
10.1016/j.jobe.2023.107086
10.3390/ma15207098
10.1016/j.conbuildmat.2023.134092
10.1007/s42107-023-00826-8
10.1371/journal.pone.0265846
10.1016/j.rineng.2023.101595
10.1038/s41598-023-39349-2
10.1016/j.mtsust.2022.100240
10.1007/s00521-023-08378-3
10.1007/s10163-023-01851-0
10.1016/j.advengsoft.2017.05.014
10.1016/j.conbuildmat.2019.117000
10.1016/j.jobe.2022.105100
10.1016/j.jclepro.2019.119250
10.1016/j.ceramint.2021.02.009
10.1007/s10115-007-0114-2
10.1016/j.jobe.2023.107325
10.1016/j.commatsci.2019.109203
10.1007/s00366-021-01385-9
10.1016/j.jmrt.2023.07.034
10.51526/kbes.2022.3.1.1-16
10.1023/A:1024974810270
10.1007/BF00994018
10.1016/j.mtcomm.2023.107485
10.1111/j.1551-2916.2010.03611.x
10.1016/j.conbuildmat.2013.08.078
10.1007/s11356-022-20863-1
10.1016/j.jmrt.2020.06.008
10.1016/j.cscm.2022.e00994
10.1007/s41939-023-00145-0
10.1007/s10098-022-02318-w
10.1016/j.matpr.2019.06.288
10.1016/j.pisc.2016.06.040
10.1016/S0169-7161(04)24011-1
10.1016/j.matpr.2022.02.506
10.1080/24709360.2017.1396742
10.1007/s10904-023-02672-2
10.1061/JMCEE7.MTENG-16669
10.1214/aos/1176348768
10.1371/journal.pone.0253006
10.1016/j.cscm.2022.e01036
10.1016/j.engstruct.2018.01.008
10.1016/j.matpr.2022.03.337
10.4028/www.scientific.net/AMM.578-579.441
10.1016/j.autcon.2020.103155
10.1016/j.jclepro.2019.02.096
10.1063/1.1680571
10.1016/j.jobe.2020.101326
10.1007/s00521-022-07427-7
10.1080/00401706.1999.10485594
10.1016/j.conbuildmat.2023.132266
10.1109/ACCESS.2019.2932769
10.1007/978-981-13-0761-4_81
10.1016/j.cscm.2023.e02581
10.1016/j.conbuildmat.2022.128174
10.3390/su13137444
10.1016/j.jobe.2023.106942
10.1016/j.scitotenv.2019.01.221
10.1007/s41939-023-00154-z
10.1016/j.cscm.2020.e00352
10.1016/j.matpr.2023.06.338
10.1002/wics.2
10.3390/ma15051868
10.1023/B:AIRE.0000045502.10941.a9
10.1016/j.mtcomm.2023.107356
10.1016/j.cesys.2021.100047
10.1007/978-1-4302-5990-9_4
10.1016/j.jobe.2023.106521
10.1007/s41939-023-00150-3
10.1016/j.conbuildmat.2023.132814
10.1016/j.clema.2022.100111
10.1016/j.conbuildmat.2018.03.166
10.1007/s42107-023-00698-y
10.1016/j.jobe.2022.104062
10.1007/s00366-020-01003-0
10.1016/j.mattod.2023.01.017
10.1145/130385.130401
10.1109/ICTKE.2017.8259629
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Keywords High-strength
Self-compacting
Sensitivity analysis
GGBFS
XGBoost
Machine learning
Alkali-activated concrete
SHO
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References Saltelli, Tarantola, Chan (CR76) 1999; 41
Géron (CR38) 2019
Muraleedharan, Nadir (CR50) 2021; 47
Dwibedy, Panigrahi (CR32) 2023; 409
Pradhan, Panda, Kumar Parhi, Kumar Panigrahi (CR66) 2022
Ahmed, Mohammed, Faraj, Abdalla, Qaidi, Sor, Mohammed (CR9) 2023; 35
Parhi, Patro (CR57) 2023
Zhang, Bai, Zhang (CR88) 2023; 6
Dhiman, Kumar, Yadav, Yadav, Bansal, Deep, Kim (CR28) 2019
Nhat-Duc (CR51) 2023; 6
CR33
Pradhan, Panda, Kumar Parhi, Kumar Panigrahi (CR65) 2022; 344
Ahmed, Mohammed, Mohammed (CR6) 2022; 34
Parhi, Patro (CR58) 2023; 71
Cortes, Vapnik (CR22) 1995; 20
Chen (CR18) 2017; 1
Oliveira, Tutikian, Milanes, Silva (CR54) 2020; 248
Duan, Asteris, Nguyen, Bui, Moayedi (CR31) 2021; 37
Xiong, Cui, Liu, Zhao, Hu, Hu (CR87) 2020; 171
Parhi, Panigrahi (CR56) 2023
Pradhan, Panda, Kumar Mandal, Kumar Panigrahi (CR68) 2023
Dou, Yunus, Tien Bui, Merghadi, Sahana, Zhu, Chen, Khosravi, Yang, Pham (CR30) 2019; 662
Qaidi, Yahia, Tayeh, Unis, Faraj, Mohammed (CR72) 2022; 20
Chen, Amin, Iftikhar, Ahmad, Althoey, Alsharari (CR19) 2023; 76
Faraj, Mohammed, Mohammed, Omer, Ahmed (CR34) 2022; 38
Iftikhar, Alih, Vafaei, Javed, Rehman, Abdullaev, Tamam, Khan, Hassan (CR41) 2023; 13
Parhi, Dwibedy, Panda, Panigrahi (CR59) 2023
Singh, Patro, Parhi (CR79) 2023
Oliveira, Izquierdo, Querol, Lieberman, Saikia, Silva (CR53) 2019; 219
Patel, Shah (CR61) 2018; 171
Wang, Bah, Hammad (CR85) 2019; 7
Schapire, Schölkopf, Luo, Vovk (CR77) 2013
Awad, Khanna, Awad, Khanna (CR14) 2015
Kang, Yoo, Gupta (CR44) 2021; 266
Ahmed, Mohammed, Mohammed (CR4) 2022; 49
Shahmansouri, Bengar, Ghanbari (CR78) 2020; 31
Dong, Huang, Lehane, Ma (CR29) 2020; 114
Pradhan, Panda, Dwibedy, Pradhan, Panigrahi (CR67) 2023
Terrell, Scott (CR82) 1992; 20
Angiulli, Pizzuti (CR13) 2005; 17
Memon, Nuruddin, Demie, Shafiq (CR48) 2012; 8
Faraj, Mohammed, Omer, Ahmed (CR35) 2022; 24
Pradhan, Panda, Parhi, Pradhan, Panigrahi (CR69) 2024; 36
Unis Ahmed, Mahmood, Muhammad, Faraj, Qaidi, Hamah Sor, Mohammed, Mohammed (CR83) 2022; 5
Alsharari, Iftikhar, Uddin, Deifalla (CR12) 2023; 20
Kim, Park (CR45) 2014; 578–579
Ahmed, Mohammed, Mohammed (CR3) 2022; 17
Qaidi, Tayeh, Isleem, de Azevedo, Ahmed, Emad (CR70) 2022; 16
Hodge, Austin (CR39) 2004; 22
Qaidi, Najm, Abed, Ahmed, Al Dughaishi, Al Lawati, Sabri, Alkhatib, Milad (CR71) 2022; 15
Saini, Vattipalli (CR74) 2020; 12
Ahmed, Mohammed, Faraj, Qaidi, Mohammed (CR5) 2022; 16
CR17
Chou, Pham (CR21) 2013; 49
Cukier, Fortuin, Shuler, Petschek, Schaibly (CR23) 1973; 59
CR55
Kumar Dash, Kumar Parhi, Kumar Patro, Panigrahi (CR46) 2023; 400
Chen, Iftikhar, Ahmad, Dodo, Abuhussain, Althoey, Sufian (CR20) 2023; 37
Wu, Kumar, Ross Quinlan, Ghosh, Yang, Motoda, McLachlan, Ng, Liu, Yu, Zhou, Steinbach, Hand, Steinberg (CR86) 2008; 14
Faridmehr, Nehdi, Huseien, Baghban, Sam, Algaifi (CR36) 2021; 13
Alsalman, Assi, Kareem, Carter, Ziehl (CR11) 2021; 3
Mangalathu, Jeon (CR47) 2018; 160
Dash, Parhi, Patro, Panigrahi (CR25) 2023; 37
Smirnova, Kazanskaya, Koplík, Tan, Gu (CR80) 2021; 13
Unis Ahmed, Mohammed, Mohammed (CR84) 2023; 394
Ahmed, Mohammed, Mohammed (CR10) 2023; 75
Hu (CR40) 2023; 6
Feng, Liu, Wang, Chen, Chang, Wei, Jiang (CR37) 2020; 230
Pradhan, Dwibedy, Pradhan, Panda, Panigrahi (CR63) 2022; 59
Qureshi, Alyami, Nawaz, Hakeem, Aslam, Iftikhar, Gamil (CR73) 2023; 19
Zou, Wang, Nasir Amin, Iftikhar, Khan, Ali, Althoey (CR89) 2023; 409
Nuruddin, Demie, Memon, Shafiq (CR52) 2011; 75
Basilio, Goliatt (CR16) 2022; 3
Petrovskiy (CR62) 2003; 29
Sakulich, Miller, Barsoum (CR75) 2010; 93
Awoyera, Kirgiz, Viloria, Ovallos-Gazabon (CR15) 2020; 9
Ahmed, Abdalla, Mohammed, Mohammed, Mosavi (CR2) 2022; 15
Dhiman, Kumar (CR27) 2017; 114
CR26
Iftikhar, Alih, Vafaei, Javed, Ali, Gamil, Rehman (CR42) 2023; 25
Das, Panda, Sahoo, Panigrahi (CR24) 2023; 62
Ahmed, Mohammed, Mohammed (CR7) 2022; 29
Parveen, Zaidi, Danish (CR60) 2016; 8
Sutton, Rao, Wegman, Solka (CR81) 2005
Ahmed, Mohammed, Mohammed, Faraj (CR1) 2021; 16
Ahmed, Mohammed, Mohammed (CR8) 2023
Pradhan, Panda, Kumar Parhi, Kumar Panigrahi (CR64) 2022
Jithendra, Elavenil (CR43) 2019; 18
Morgenthaler (CR49) 2009; 1
SK Parhi (349_CR56) 2023
I Faridmehr (349_CR36) 2021; 13
FA Memon (349_CR48) 2012; 8
HJ Qureshi (349_CR73) 2023; 19
349_CR17
SMA Qaidi (349_CR70) 2022; 16
PO Awoyera (349_CR15) 2020; 9
349_CR55
J Pradhan (349_CR67) 2023
C Cortes (349_CR22) 1995; 20
S Morgenthaler (349_CR49) 2009; 1
A Alsalman (349_CR11) 2021; 3
SK Parhi (349_CR59) 2023
HU Ahmed (349_CR9) 2023; 35
P Pradhan (349_CR66) 2022
F Alsharari (349_CR12) 2023; 20
P Pradhan (349_CR63) 2022; 59
RH Faraj (349_CR34) 2022; 38
HU Ahmed (349_CR10) 2023; 75
HU Ahmed (349_CR6) 2022; 34
Z Xiong (349_CR87) 2020; 171
M Muraleedharan (349_CR50) 2021; 47
A Géron (349_CR38) 2019
AA Shahmansouri (349_CR78) 2020; 31
H Unis Ahmed (349_CR84) 2023; 394
P Pradhan (349_CR64) 2022
SK Parhi (349_CR57) 2023
S Singh (349_CR79) 2023
GR Terrell (349_CR82) 1992; 20
S Qaidi (349_CR71) 2022; 15
N Parveen (349_CR60) 2016; 8
SK Parhi (349_CR58) 2023; 71
YJ Patel (349_CR61) 2018; 171
J-S Chou (349_CR21) 2013; 49
MLS Oliveira (349_CR54) 2020; 248
M Awad (349_CR14) 2015
HU Ahmed (349_CR5) 2022; 16
R Das (349_CR24) 2023; 62
RI Cukier (349_CR23) 1973; 59
SA Basilio (349_CR16) 2022; 3
W Dong (349_CR29) 2020; 114
J Pradhan (349_CR68) 2023
G Dhiman (349_CR27) 2017; 114
J Dou (349_CR30) 2019; 662
D-C Feng (349_CR37) 2020; 230
V Hodge (349_CR39) 2004; 22
J Pradhan (349_CR69) 2024; 36
J Duan (349_CR31) 2021; 37
S Dwibedy (349_CR32) 2023; 409
HU Ahmed (349_CR2) 2022; 15
Z Chen (349_CR19) 2023; 76
C Jithendra (349_CR43) 2019; 18
349_CR33
P Kumar Dash (349_CR46) 2023; 400
G Dhiman (349_CR28) 2019
M-C Kang (349_CR44) 2021; 266
JS Kim (349_CR45) 2014; 578–579
AR Sakulich (349_CR75) 2010; 93
H Wang (349_CR85) 2019; 7
HU Ahmed (349_CR8) 2023
F Angiulli (349_CR13) 2005; 17
HU Ahmed (349_CR7) 2022; 29
RH Faraj (349_CR35) 2022; 24
RE Schapire (349_CR77) 2013
Z Chen (349_CR20) 2023; 37
B Iftikhar (349_CR41) 2023; 13
Y Zhang (349_CR88) 2023; 6
MI Petrovskiy (349_CR62) 2003; 29
MLS Oliveira (349_CR53) 2019; 219
PK Dash (349_CR25) 2023; 37
H Unis Ahmed (349_CR83) 2022; 5
CD Sutton (349_CR81) 2005
349_CR26
HU Ahmed (349_CR3) 2022; 17
B Zou (349_CR89) 2023; 409
HU Ahmed (349_CR1) 2021; 16
HU Ahmed (349_CR4) 2022; 49
S Mangalathu (349_CR47) 2018; 160
H Nhat-Duc (349_CR51) 2023; 6
X Wu (349_CR86) 2008; 14
A Saltelli (349_CR76) 1999; 41
O Smirnova (349_CR80) 2021; 13
S Qaidi (349_CR72) 2022; 20
BC Iftikhar (349_CR42) 2023; 25
P Pradhan (349_CR65) 2022; 344
X Hu (349_CR40) 2023; 6
Y-C Chen (349_CR18) 2017; 1
F Nuruddin (349_CR52) 2011; 75
G Saini (349_CR74) 2020; 12
References_xml – volume: 17
  start-page: 203
  issue: 2
  year: 2005
  end-page: 215
  ident: CR13
  article-title: Outlier mining in large high-dimensional data sets
  publication-title: IEEE Trans Knowl Data Eng
  doi: 10.1109/TKDE.2005.31
– year: 2023
  ident: CR56
  article-title: Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00799-8
– volume: 409
  year: 2023
  ident: CR32
  article-title: Factors affecting the structural performance of geopolymer concrete beam composites
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.134129
– volume: 266
  year: 2021
  ident: CR44
  article-title: Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2020.121117
– volume: 13
  issue: 1
  year: 2021
  ident: CR80
  article-title: Concrete Based on clinker-free cement: selecting the functional unit for environmental assessment
  publication-title: Sustainability
  doi: 10.3390/su13010135
– start-page: 37
  year: 2013
  end-page: 52
  ident: CR77
  article-title: Explaining AdaBoost
  publication-title: Empirical inference: festschrift in honor of vladimir N. Vapnik
  doi: 10.1007/978-3-642-41136-6_5
– year: 2023
  ident: CR59
  article-title: A comprehensive study on controlled low strength material
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.107086
– volume: 15
  issue: 20
  year: 2022
  ident: CR71
  article-title: Fly ash-based geopolymer composites: a review of the compressive strength and microstructure analysis
  publication-title: Materials
  doi: 10.3390/ma15207098
– volume: 409
  year: 2023
  ident: CR89
  article-title: Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.134092
– year: 2023
  ident: CR57
  article-title: Compressive strength prediction of PET fiber-reinforced concrete using Dolphin echolocation optimized decision tree-based machine learning algorithms
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00826-8
– volume: 17
  issue: 5
  year: 2022
  ident: CR3
  article-title: Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0265846
– volume: 20
  year: 2023
  ident: CR12
  article-title: Data-driven strategy for evaluating the response of eco-friendly concrete at elevated temperatures for fire resistance construction
  publication-title: Results Eng
  doi: 10.1016/j.rineng.2023.101595
– volume: 13
  issue: 1
  year: 2023
  ident: CR41
  article-title: Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
  publication-title: Sci Rep
  doi: 10.1038/s41598-023-39349-2
– volume: 20
  year: 2022
  ident: CR72
  article-title: 3D printed geopolymer composites: a review
  publication-title: Mater Today Sustain
  doi: 10.1016/j.mtsust.2022.100240
– volume: 35
  start-page: 12453
  issue: 17
  year: 2023
  end-page: 12479
  ident: CR9
  article-title: Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-023-08378-3
– year: 2023
  ident: CR67
  article-title: Production of durable high-strength self-compacting geopolymer concrete with GGBFS as a precursor
  publication-title: J Mater Cycles Waste Manag
  doi: 10.1007/s10163-023-01851-0
– volume: 114
  start-page: 48
  year: 2017
  end-page: 70
  ident: CR27
  article-title: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications
  publication-title: Adv Eng Softw
  doi: 10.1016/j.advengsoft.2017.05.014
– volume: 230
  year: 2020
  ident: CR37
  article-title: Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2019.117000
– volume: 59
  year: 2022
  ident: CR63
  article-title: Durability characteristics of geopolymer concrete—progress and perspectives
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2022.105100
– volume: 248
  year: 2020
  ident: CR54
  article-title: Atmospheric contaminations and bad conservation effects in Roman mosaics and mortars of Italica
  publication-title: J Clean Prod
  doi: 10.1016/j.jclepro.2019.119250
– volume: 47
  start-page: 13257
  issue: 10
  year: 2021
  end-page: 13279
  ident: CR50
  article-title: Factors affecting the mechanical properties and microstructure of geopolymers from red mud and granite waste powder: a review
  publication-title: Ceram Int
  doi: 10.1016/j.ceramint.2021.02.009
– volume: 14
  start-page: 1
  issue: 1
  year: 2008
  end-page: 37
  ident: CR86
  article-title: Top 10 algorithms in data mining
  publication-title: Knowl Inf Syst
  doi: 10.1007/s10115-007-0114-2
– volume: 76
  year: 2023
  ident: CR19
  article-title: Predictive modelling for the acid resistance of cement-based composites modified with eggshell and glass waste for sustainable and resilient building materials
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.107325
– volume: 171
  year: 2020
  ident: CR87
  article-title: Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation
  publication-title: Comput Mater Sci
  doi: 10.1016/j.commatsci.2019.109203
– volume: 38
  start-page: 2365
  issue: 3
  year: 2022
  end-page: 2388
  ident: CR34
  article-title: Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages
  publication-title: Eng Comput
  doi: 10.1007/s00366-021-01385-9
– volume: 25
  start-page: 5705
  year: 2023
  end-page: 5719
  ident: CR42
  article-title: A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks
  publication-title: J Mater Res Technol
  doi: 10.1016/j.jmrt.2023.07.034
– volume: 3
  start-page: 1
  issue: 1
  year: 2022
  ident: CR16
  article-title: Gradient boosting hybridized with exponential natural evolution strategies for estimating the strength of geopolymer self-compacting concrete
  publication-title: Knowl Based Eng Sci
  doi: 10.51526/kbes.2022.3.1.1-16
– volume: 29
  start-page: 228
  issue: 4
  year: 2003
  end-page: 237
  ident: CR62
  article-title: Outlier detection algorithms in data mining systems
  publication-title: Program Comput Softw
  doi: 10.1023/A:1024974810270
– volume: 20
  start-page: 273
  issue: 3
  year: 1995
  end-page: 297
  ident: CR22
  article-title: Support-vector networks
  publication-title: Mach Learn
  doi: 10.1007/BF00994018
– volume: 37
  year: 2023
  ident: CR25
  article-title: Influence of chemical constituents of binder and activator in predicting compressive strength of fly ash-based geopolymer concrete using firefly-optimized hybrid ensemble machine learning model
  publication-title: Mater Today Commun
  doi: 10.1016/j.mtcomm.2023.107485
– volume: 93
  start-page: 1741
  issue: 6
  year: 2010
  end-page: 1748
  ident: CR75
  article-title: Chemical and microstructural characterization of 20-month-old alkali-activated slag cements
  publication-title: J Am Ceram Soc
  doi: 10.1111/j.1551-2916.2010.03611.x
– volume: 49
  start-page: 554
  year: 2013
  end-page: 563
  ident: CR21
  article-title: Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2013.08.078
– year: 2019
  ident: CR38
  publication-title: Hands-on machine learning with scikit-learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems
– volume: 29
  start-page: 71232
  issue: 47
  year: 2022
  end-page: 71256
  ident: CR7
  article-title: Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica
  publication-title: Environ Sci Pollut Res
  doi: 10.1007/s11356-022-20863-1
– volume: 9
  start-page: 9016
  issue: 4
  year: 2020
  end-page: 9028
  ident: CR15
  article-title: Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
  publication-title: J Mark Res
  doi: 10.1016/j.jmrt.2020.06.008
– volume: 16
  year: 2022
  ident: CR70
  article-title: Sustainable utilization of red mud waste (bauxite residue) and slag for the production of geopolymer composites: a review
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2022.e00994
– volume: 6
  start-page: 389
  issue: 3
  year: 2023
  end-page: 400
  ident: CR88
  article-title: Compressive strength estimation of ultra-great workability concrete using hybrid algorithms
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00145-0
– volume: 24
  start-page: 2253
  issue: 7
  year: 2022
  end-page: 2281
  ident: CR35
  article-title: Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes
  publication-title: Clean Technol Environ Policy
  doi: 10.1007/s10098-022-02318-w
– volume: 18
  start-page: 148
  year: 2019
  end-page: 154
  ident: CR43
  article-title: Role of superplasticizer on GGBS based geopolymer concrete under ambient curing
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2019.06.288
– volume: 8
  start-page: 629
  year: 2016
  end-page: 631
  ident: CR60
  article-title: Support vector regression model for predicting the sorption capacity of lead (II)
  publication-title: Perspect Sci
  doi: 10.1016/j.pisc.2016.06.040
– start-page: 303
  year: 2005
  end-page: 329
  ident: CR81
  article-title: 11—Classification and regression trees, bagging, and boosting
  publication-title: Handbook of statistics
  doi: 10.1016/S0169-7161(04)24011-1
– year: 2022
  ident: CR64
  article-title: Effect of critical parameters on the fresh properties of Self Compacting geopolymer concrete
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2022.02.506
– volume: 1
  start-page: 161
  issue: 1
  year: 2017
  end-page: 187
  ident: CR18
  article-title: A tutorial on kernel density estimation and recent advances
  publication-title: Biostat Epidemiol
  doi: 10.1080/24709360.2017.1396742
– year: 2023
  ident: CR8
  article-title: Effectiveness of silicon dioxide nanoparticles (Nano SiO ) on the internal structures, electrical conductivity, and elevated temperature behaviors of geopolymer concrete composites
  publication-title: J Inorg Organomet Polym Mater
  doi: 10.1007/s10904-023-02672-2
– volume: 36
  start-page: 04023578
  issue: 2
  year: 2024
  ident: CR69
  article-title: GGBFS-based self-compacting geopolymer concrete with optimized mix parameters established on fresh, mechanical, and durability characteristics
  publication-title: J Mater Civ Eng
  doi: 10.1061/JMCEE7.MTENG-16669
– ident: CR26
– volume: 20
  start-page: 1236
  issue: 3
  year: 1992
  end-page: 1265
  ident: CR82
  article-title: Variable kernel density estimation
  publication-title: Ann Stat
  doi: 10.1214/aos/1176348768
– volume: 16
  issue: 6
  year: 2021
  ident: CR1
  article-title: Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0253006
– volume: 16
  year: 2022
  ident: CR5
  article-title: Compressive strength of geopolymer concrete modified with nano-silica: experimental and modeling investigations
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2022.e01036
– volume: 160
  start-page: 85
  year: 2018
  end-page: 94
  ident: CR47
  article-title: Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques
  publication-title: Eng Struct
  doi: 10.1016/j.engstruct.2018.01.008
– year: 2022
  ident: CR66
  article-title: Variation in fresh and mechanical properties of GGBFS based self-compacting geopolymer concrete in the presence of NCA and RCA
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2022.03.337
– volume: 578–579
  start-page: 441
  year: 2014
  end-page: 444
  ident: CR45
  article-title: An experimental evaluation of development length of reinforcements embedded in geopolymer concrete
  publication-title: Appl Mech Mater
  doi: 10.4028/www.scientific.net/AMM.578-579.441
– volume: 114
  year: 2020
  ident: CR29
  article-title: XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring
  publication-title: Autom Constr
  doi: 10.1016/j.autcon.2020.103155
– volume: 219
  start-page: 236
  year: 2019
  end-page: 243
  ident: CR53
  article-title: Nanoparticles from construction wastes: a problem to health and the environment
  publication-title: J Clean Prod
  doi: 10.1016/j.jclepro.2019.02.096
– volume: 59
  start-page: 3873
  issue: 8
  year: 1973
  end-page: 3878
  ident: CR23
  article-title: Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory
  publication-title: J Chem Phys
  doi: 10.1063/1.1680571
– volume: 31
  year: 2020
  ident: CR78
  article-title: Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2020.101326
– volume: 34
  start-page: 17853
  issue: 20
  year: 2022
  end-page: 17876
  ident: CR6
  article-title: Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-022-07427-7
– ident: CR33
– volume: 41
  start-page: 39
  issue: 1
  year: 1999
  end-page: 56
  ident: CR76
  article-title: A Quantitative model-independent method for global sensitivity analysis of model output
  publication-title: Technometrics
  doi: 10.1080/00401706.1999.10485594
– volume: 394
  year: 2023
  ident: CR84
  article-title: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.132266
– volume: 7
  start-page: 107964
  year: 2019
  end-page: 108000
  ident: CR85
  article-title: Progress in outlier detection techniques: a survey
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2932769
– start-page: 857
  year: 2019
  end-page: 867
  ident: CR28
  article-title: Spotted hyena optimizer for solving complex and non-linear constrained engineering problems
  publication-title: Harmony search and nature inspired optimization algorithms
  doi: 10.1007/978-981-13-0761-4_81
– volume: 19
  year: 2023
  ident: CR73
  article-title: Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2023.e02581
– volume: 344
  year: 2022
  ident: CR65
  article-title: Factors affecting production and properties of self-compacting geopolymer concrete—a review
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2022.128174
– volume: 13
  issue: 13
  year: 2021
  ident: CR36
  article-title: Experimental and informational modeling study of sustainable self-compacting geopolymer concrete
  publication-title: Sustainability
  doi: 10.3390/su13137444
– volume: 75
  start-page: 187
  year: 2011
  end-page: 194
  ident: CR52
  article-title: Effect of superplasticizer and NaOH molarity on workability, compressive strength and microstructure properties of self-compacting geopolymer concrete
  publication-title: World Acad Sci Eng Technol
– volume: 75
  year: 2023
  ident: CR10
  article-title: Engineering properties of geopolymer concrete composites incorporated recycled plastic aggregates modified with nano-silica
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.106942
– volume: 662
  start-page: 332
  year: 2019
  end-page: 346
  ident: CR30
  article-title: Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
  publication-title: Sci Total Environ
  doi: 10.1016/j.scitotenv.2019.01.221
– volume: 6
  start-page: 415
  issue: 3
  year: 2023
  end-page: 430
  ident: CR51
  article-title: Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using a novel regularized deep learning approach
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00154-z
– volume: 12
  year: 2020
  ident: CR74
  article-title: Assessing properties of alkali activated GGBS based self-compacting geopolymer concrete using nano-silica
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2020.e00352
– year: 2023
  ident: CR68
  article-title: Influence of GGBFS-based blended precursor on fresh properties of self-compacting geopolymer concrete under ambient temperature
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2023.06.338
– volume: 1
  start-page: 33
  issue: 1
  year: 2009
  end-page: 44
  ident: CR49
  article-title: Exploratory data analysis
  publication-title: Wires Comput Stat
  doi: 10.1002/wics.2
– volume: 15
  issue: 5
  year: 2022
  ident: CR2
  article-title: Statistical methods for modeling the compressive strength of geopolymer mortar
  publication-title: Materials
  doi: 10.3390/ma15051868
– volume: 22
  start-page: 85
  issue: 2
  year: 2004
  end-page: 126
  ident: CR39
  article-title: A survey of outlier detection methodologies
  publication-title: Artif Intell Rev
  doi: 10.1023/B:AIRE.0000045502.10941.a9
– ident: CR17
– volume: 37
  year: 2023
  ident: CR20
  article-title: Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models
  publication-title: Mater Today Commun
  doi: 10.1016/j.mtcomm.2023.107356
– volume: 3
  year: 2021
  ident: CR11
  article-title: Energy and CO emission assessments of alkali-activated concrete and ordinary Portland cement concrete: a comparative analysis of different grades of concrete
  publication-title: Clean Environ Syst
  doi: 10.1016/j.cesys.2021.100047
– start-page: 67
  year: 2015
  end-page: 80
  ident: CR14
  article-title: Support vector regression
  publication-title: Efficient learning machines: theories, concepts, and applications for engineers and system designers
  doi: 10.1007/978-1-4302-5990-9_4
– volume: 71
  year: 2023
  ident: CR58
  article-title: Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.106521
– volume: 6
  start-page: 357
  issue: 3
  year: 2023
  end-page: 370
  ident: CR40
  article-title: Evaluation of compressive strength of the HPC produced with admixtures by a novel hybrid SVR model
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00150-3
– volume: 400
  year: 2023
  ident: CR46
  article-title: Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.132814
– ident: CR55
– volume: 5
  year: 2022
  ident: CR83
  article-title: Geopolymer concrete as a cleaner construction material: an overview on materials and structural performances
  publication-title: Clean Mater
  doi: 10.1016/j.clema.2022.100111
– volume: 62
  start-page: 1
  year: 2023
  end-page: 11
  ident: CR24
  article-title: Effect of superplasticizer types and dosage on the flow characteristics of GGBFS based self-compacting geopolymer concrete
  publication-title: Mater Today Proc
– volume: 171
  start-page: 654
  year: 2018
  end-page: 662
  ident: CR61
  article-title: Enhancement of the properties of ground granulated blast furnace slag based self compacting geopolymer concrete by incorporating rice husk ash
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2018.03.166
– year: 2023
  ident: CR79
  article-title: Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00698-y
– volume: 49
  year: 2022
  ident: CR4
  article-title: The role of nanomaterials in geopolymer concrete composites: a state-of-the-art review
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2022.104062
– volume: 37
  start-page: 3329
  issue: 4
  year: 2021
  end-page: 3346
  ident: CR31
  article-title: A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
  publication-title: Eng Comput
  doi: 10.1007/s00366-020-01003-0
– volume: 8
  start-page: 407
  year: 2012
  end-page: 414
  ident: CR48
  article-title: Effect of superplasticizer and extra water on workability and compressive strength of self-compacting geopolymer concrete
  publication-title: Res J Appl Sci Eng Technol
– volume: 47
  start-page: 13257
  issue: 10
  year: 2021
  ident: 349_CR50
  publication-title: Ceram Int
  doi: 10.1016/j.ceramint.2021.02.009
– volume: 171
  start-page: 654
  year: 2018
  ident: 349_CR61
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2018.03.166
– ident: 349_CR26
– volume-title: Hands-on machine learning with scikit-learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems
  year: 2019
  ident: 349_CR38
– volume: 8
  start-page: 407
  year: 2012
  ident: 349_CR48
  publication-title: Res J Appl Sci Eng Technol
– volume: 41
  start-page: 39
  issue: 1
  year: 1999
  ident: 349_CR76
  publication-title: Technometrics
  doi: 10.1080/00401706.1999.10485594
– volume: 20
  year: 2022
  ident: 349_CR72
  publication-title: Mater Today Sustain
  doi: 10.1016/j.mtsust.2022.100240
– volume: 12
  year: 2020
  ident: 349_CR74
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2020.e00352
– volume: 20
  year: 2023
  ident: 349_CR12
  publication-title: Results Eng
  doi: 10.1016/j.rineng.2023.101595
– volume: 409
  year: 2023
  ident: 349_CR32
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.134129
– volume: 171
  year: 2020
  ident: 349_CR87
  publication-title: Comput Mater Sci
  doi: 10.1016/j.commatsci.2019.109203
– volume: 6
  start-page: 389
  issue: 3
  year: 2023
  ident: 349_CR88
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00145-0
– volume: 38
  start-page: 2365
  issue: 3
  year: 2022
  ident: 349_CR34
  publication-title: Eng Comput
  doi: 10.1007/s00366-021-01385-9
– volume: 1
  start-page: 161
  issue: 1
  year: 2017
  ident: 349_CR18
  publication-title: Biostat Epidemiol
  doi: 10.1080/24709360.2017.1396742
– year: 2023
  ident: 349_CR59
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.107086
– volume: 75
  year: 2023
  ident: 349_CR10
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.106942
– volume: 114
  year: 2020
  ident: 349_CR29
  publication-title: Autom Constr
  doi: 10.1016/j.autcon.2020.103155
– year: 2023
  ident: 349_CR57
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00826-8
– volume: 36
  start-page: 04023578
  issue: 2
  year: 2024
  ident: 349_CR69
  publication-title: J Mater Civ Eng
  doi: 10.1061/JMCEE7.MTENG-16669
– volume: 19
  year: 2023
  ident: 349_CR73
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2023.e02581
– volume: 62
  start-page: 1
  year: 2023
  ident: 349_CR24
  publication-title: Mater Today Proc
  doi: 10.1016/j.mattod.2023.01.017
– volume: 17
  start-page: 203
  issue: 2
  year: 2005
  ident: 349_CR13
  publication-title: IEEE Trans Knowl Data Eng
  doi: 10.1109/TKDE.2005.31
– volume: 266
  year: 2021
  ident: 349_CR44
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2020.121117
– volume: 16
  year: 2022
  ident: 349_CR5
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2022.e01036
– volume: 49
  year: 2022
  ident: 349_CR4
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2022.104062
– volume: 34
  start-page: 17853
  issue: 20
  year: 2022
  ident: 349_CR6
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-022-07427-7
– volume: 76
  year: 2023
  ident: 349_CR19
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.107325
– volume: 6
  start-page: 415
  issue: 3
  year: 2023
  ident: 349_CR51
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00154-z
– volume: 16
  year: 2022
  ident: 349_CR70
  publication-title: Case Stud Constr Mater
  doi: 10.1016/j.cscm.2022.e00994
– volume: 1
  start-page: 33
  issue: 1
  year: 2009
  ident: 349_CR49
  publication-title: Wires Comput Stat
  doi: 10.1002/wics.2
– volume: 71
  year: 2023
  ident: 349_CR58
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2023.106521
– start-page: 303
  volume-title: Handbook of statistics
  year: 2005
  ident: 349_CR81
  doi: 10.1016/S0169-7161(04)24011-1
– volume: 409
  year: 2023
  ident: 349_CR89
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.134092
– volume: 93
  start-page: 1741
  issue: 6
  year: 2010
  ident: 349_CR75
  publication-title: J Am Ceram Soc
  doi: 10.1111/j.1551-2916.2010.03611.x
– year: 2023
  ident: 349_CR56
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00799-8
– volume: 17
  issue: 5
  year: 2022
  ident: 349_CR3
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0265846
– volume: 37
  year: 2023
  ident: 349_CR20
  publication-title: Mater Today Commun
  doi: 10.1016/j.mtcomm.2023.107356
– volume: 25
  start-page: 5705
  year: 2023
  ident: 349_CR42
  publication-title: J Mater Res Technol
  doi: 10.1016/j.jmrt.2023.07.034
– volume: 13
  issue: 1
  year: 2021
  ident: 349_CR80
  publication-title: Sustainability
  doi: 10.3390/su13010135
– volume: 3
  year: 2021
  ident: 349_CR11
  publication-title: Clean Environ Syst
  doi: 10.1016/j.cesys.2021.100047
– volume: 14
  start-page: 1
  issue: 1
  year: 2008
  ident: 349_CR86
  publication-title: Knowl Inf Syst
  doi: 10.1007/s10115-007-0114-2
– volume: 13
  issue: 1
  year: 2023
  ident: 349_CR41
  publication-title: Sci Rep
  doi: 10.1038/s41598-023-39349-2
– volume: 7
  start-page: 107964
  year: 2019
  ident: 349_CR85
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2932769
– ident: 349_CR17
  doi: 10.1145/130385.130401
– volume: 24
  start-page: 2253
  issue: 7
  year: 2022
  ident: 349_CR35
  publication-title: Clean Technol Environ Policy
  doi: 10.1007/s10098-022-02318-w
– volume: 219
  start-page: 236
  year: 2019
  ident: 349_CR53
  publication-title: J Clean Prod
  doi: 10.1016/j.jclepro.2019.02.096
– volume: 8
  start-page: 629
  year: 2016
  ident: 349_CR60
  publication-title: Perspect Sci
  doi: 10.1016/j.pisc.2016.06.040
– volume: 400
  year: 2023
  ident: 349_CR46
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.132814
– year: 2023
  ident: 349_CR68
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2023.06.338
– volume: 15
  issue: 5
  year: 2022
  ident: 349_CR2
  publication-title: Materials
  doi: 10.3390/ma15051868
– year: 2022
  ident: 349_CR66
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2022.03.337
– ident: 349_CR55
  doi: 10.1109/ICTKE.2017.8259629
– start-page: 37
  volume-title: Empirical inference: festschrift in honor of vladimir N. Vapnik
  year: 2013
  ident: 349_CR77
  doi: 10.1007/978-3-642-41136-6_5
– volume: 13
  issue: 13
  year: 2021
  ident: 349_CR36
  publication-title: Sustainability
  doi: 10.3390/su13137444
– volume: 20
  start-page: 273
  issue: 3
  year: 1995
  ident: 349_CR22
  publication-title: Mach Learn
  doi: 10.1007/BF00994018
– volume: 18
  start-page: 148
  year: 2019
  ident: 349_CR43
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2019.06.288
– year: 2023
  ident: 349_CR67
  publication-title: J Mater Cycles Waste Manag
  doi: 10.1007/s10163-023-01851-0
– ident: 349_CR33
– volume: 6
  start-page: 357
  issue: 3
  year: 2023
  ident: 349_CR40
  publication-title: Multiscale Multidiscip Model Exp Des
  doi: 10.1007/s41939-023-00150-3
– volume: 15
  issue: 20
  year: 2022
  ident: 349_CR71
  publication-title: Materials
  doi: 10.3390/ma15207098
– volume: 662
  start-page: 332
  year: 2019
  ident: 349_CR30
  publication-title: Sci Total Environ
  doi: 10.1016/j.scitotenv.2019.01.221
– year: 2023
  ident: 349_CR79
  publication-title: Asian J Civ Eng
  doi: 10.1007/s42107-023-00698-y
– volume: 35
  start-page: 12453
  issue: 17
  year: 2023
  ident: 349_CR9
  publication-title: Neural Comput Appl
  doi: 10.1007/s00521-023-08378-3
– volume: 248
  year: 2020
  ident: 349_CR54
  publication-title: J Clean Prod
  doi: 10.1016/j.jclepro.2019.119250
– volume: 29
  start-page: 228
  issue: 4
  year: 2003
  ident: 349_CR62
  publication-title: Program Comput Softw
  doi: 10.1023/A:1024974810270
– year: 2023
  ident: 349_CR8
  publication-title: J Inorg Organomet Polym Mater
  doi: 10.1007/s10904-023-02672-2
– volume: 9
  start-page: 9016
  issue: 4
  year: 2020
  ident: 349_CR15
  publication-title: J Mark Res
  doi: 10.1016/j.jmrt.2020.06.008
– volume: 22
  start-page: 85
  issue: 2
  year: 2004
  ident: 349_CR39
  publication-title: Artif Intell Rev
  doi: 10.1023/B:AIRE.0000045502.10941.a9
– volume: 3
  start-page: 1
  issue: 1
  year: 2022
  ident: 349_CR16
  publication-title: Knowl Based Eng Sci
  doi: 10.51526/kbes.2022.3.1.1-16
– volume: 37
  start-page: 3329
  issue: 4
  year: 2021
  ident: 349_CR31
  publication-title: Eng Comput
  doi: 10.1007/s00366-020-01003-0
– volume: 578–579
  start-page: 441
  year: 2014
  ident: 349_CR45
  publication-title: Appl Mech Mater
  doi: 10.4028/www.scientific.net/AMM.578-579.441
– volume: 29
  start-page: 71232
  issue: 47
  year: 2022
  ident: 349_CR7
  publication-title: Environ Sci Pollut Res
  doi: 10.1007/s11356-022-20863-1
– volume: 59
  year: 2022
  ident: 349_CR63
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2022.105100
– start-page: 67
  volume-title: Efficient learning machines: theories, concepts, and applications for engineers and system designers
  year: 2015
  ident: 349_CR14
  doi: 10.1007/978-1-4302-5990-9_4
– volume: 59
  start-page: 3873
  issue: 8
  year: 1973
  ident: 349_CR23
  publication-title: J Chem Phys
  doi: 10.1063/1.1680571
– volume: 5
  year: 2022
  ident: 349_CR83
  publication-title: Clean Mater
  doi: 10.1016/j.clema.2022.100111
– volume: 230
  year: 2020
  ident: 349_CR37
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2019.117000
– volume: 20
  start-page: 1236
  issue: 3
  year: 1992
  ident: 349_CR82
  publication-title: Ann Stat
  doi: 10.1214/aos/1176348768
– volume: 394
  year: 2023
  ident: 349_CR84
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2023.132266
– volume: 160
  start-page: 85
  year: 2018
  ident: 349_CR47
  publication-title: Eng Struct
  doi: 10.1016/j.engstruct.2018.01.008
– volume: 344
  year: 2022
  ident: 349_CR65
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2022.128174
– volume: 16
  issue: 6
  year: 2021
  ident: 349_CR1
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0253006
– volume: 31
  year: 2020
  ident: 349_CR78
  publication-title: J Build Eng
  doi: 10.1016/j.jobe.2020.101326
– start-page: 857
  volume-title: Harmony search and nature inspired optimization algorithms
  year: 2019
  ident: 349_CR28
  doi: 10.1007/978-981-13-0761-4_81
– year: 2022
  ident: 349_CR64
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2022.02.506
– volume: 114
  start-page: 48
  year: 2017
  ident: 349_CR27
  publication-title: Adv Eng Softw
  doi: 10.1016/j.advengsoft.2017.05.014
– volume: 75
  start-page: 187
  year: 2011
  ident: 349_CR52
  publication-title: World Acad Sci Eng Technol
– volume: 49
  start-page: 554
  year: 2013
  ident: 349_CR21
  publication-title: Constr Build Mater
  doi: 10.1016/j.conbuildmat.2013.08.078
– volume: 37
  year: 2023
  ident: 349_CR25
  publication-title: Mater Today Commun
  doi: 10.1016/j.mtcomm.2023.107485
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Snippet The present study focuses on producing high-performance eco-efficient alternatives to conventional cement-based composites. The study is divided into two...
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SubjectTerms Characterization and Evaluation of Materials
Engineering
Mathematical Applications in the Physical Sciences
Mechanical Engineering
Numerical and Computational Physics
Original Paper
Simulation
Solid Mechanics
Title Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete
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