To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models
- de-Prado-Gil, Jesús
- Palencia, Covadonga
- Silva-Monteiro, Neemias
- Martínez-García, Rebeca
ISSN: 2214-5095
Argitalpen urtea: 2022
Alea: 16
Orrialdeak: e01046
Mota: Artikulua
Beste argitalpen batzuk: Case Studies in Construction Materials
Erreferentzia bibliografikoak
- Liu, (2021), Constr. Build. Mater., 301, 10.1016/j.conbuildmat.2021.124382
- Señas, (2016), Constr. Build. Mater., 113, pp. 498, 10.1016/j.conbuildmat.2016.03.079
- Mohamad Ali Ridho, (2021), Infrastructures, 6, pp. 1
- Xie, (2018), J. Mater. Civ. Eng., 30, 10.1061/(ASCE)MT.1943-5533.0002304
- Martínez-García, (2021)
- Martínez-García, (2020), Mater. (Basel), 13, pp. 868, 10.3390/ma13040868
- Padmini, (2009), Constr. Build. Mater., 23, pp. 829, 10.1016/j.conbuildmat.2008.03.006
- Jagadesh, (2021), Appl. Sci., 11, 10.3390/app11136028
- Farooq, (2021), 14, pp. 4934
- Babajanzadeh, (2018), Civ. Eng. J., 4, pp. 1542, 10.28991/cej-0309193
- Feng, (2020), Constr. Build. Mater., 230, 10.1016/j.conbuildmat.2019.117000
- Sims, (2019), pp. 868
- Ahmad, (2021), Materials, 14
- Dinakar, (2013), Mater. Des., 43, pp. 161, 10.1016/j.matdes.2012.06.049
- Alyamaç, (2009), Constr. Build. Mater., 23, pp. 1201, 10.1016/j.conbuildmat.2008.08.012
- Nair, (2020), Int. J. Adv. Res., 8, pp. 836, 10.21474/IJAR01/11346
- Nalanth, (2014), Adv. Civ. Eng., 2014
- García, (2015)
- Silva, (2020), Rev. Int. Metod. Numer. Para. Calc. Y. Disen. En. Ing., 36, pp. 1
- Xu, (2021), Constr. Build. Mater., 301, pp. 141, 10.1016/j.conbuildmat.2021.124274
- Ahmad, (2021), Materials, pp. 5762, 10.3390/ma14195762
- Xie, (2020), J. Clean. Prod., 251, 10.1016/j.jclepro.2019.119752
- Xu, (2019), Constr. Build. Mater., 211, pp. 479, 10.1016/j.conbuildmat.2019.03.234
- Xu, (2019), Constr. Build. Mater., 226, pp. 534, 10.1016/j.conbuildmat.2019.07.155
- DeRousseau, (2019), Constr. Build. Mater., 228, 10.1016/j.conbuildmat.2019.08.042
- A. Huertas Mora, 2020. Algoritmos de aprendizaje supervisado utilizando datos de monitoreo de condiciones: Un estudio para el pronóstico de fallas en máquinas, (2020) 1–77. 〈https://repository.usta.edu.co/bitstream/handle/11634/29886/2020alexanderhuertas.pdf?sequence=1&isAllowed=y〉.
- A. Mendes, S. De Valeriola, S. Mahy, X. Maréchal, 2017. Machine Learning applications to non-life pricing Frequency modelling: An educational case study, (2017) 1–25. www.reacfin.com.
- EHE-08, 2008. Instrucción de Hormigón Estructural. Anejo 17. Recomendaciones para la utilización del hormigón autocompactante., (2008) 555–568. 〈https://www.mitma.gob.es/recursos_mfom/anejo17borde.pdf〉.
- Bermejo, (2009)
- Burón Maestro, (2006), Cem. Hormig., 887, pp. 52
- Neto, (2010), Montrteal
- Kushwaha, (2013), Int. J. Eng. Res. Appl., 3, pp. 539
- Kovačević, (2021), Materials, 14, 10.3390/ma14154346
- Gołaszewski, (2017), Czas. Tech., pp. 93
- Brouwers, (2005), Cem. Concr. Res., 35, pp. 2116, 10.1016/j.cemconres.2005.06.002
- Singh, (2018), Int. J. Civ. Eng. Technol., 9, pp. 77
- Katar, (2021), Recycling, 6, pp. 629, 10.3390/recycling6020023
- D. Nieto Alcolea, 2015. Estudio de hormigón autocompactante con árido reciclado, Universidad Politécnica de Madrid, 2015. 〈https://dialnet.unirioja.es/servlet/tesis?codigo=115881〉.
- Pérez-Benedicto, (2012), Mater. Constr., 62, pp. 25, 10.3989/mc.2011.62110
- Carro-López, (2018), Hormig. Y. Acero, 69, pp. 213, 10.1016/j.hya.2017.04.023
- Bradu, (2016), Bul. Ina. Politeh. Din. Iasi., 62, pp. 59
- Robas, (2008)
- Zhang, (2012)
- Rouhiainen, (2008), Alienta Ed., pp. 22
- Song, (2021), Constr. Build. Mater., 308, 10.1016/j.conbuildmat.2021.125021
- A. Nafees, M.F. Javed, S. Khan, K. Nazir, F. Farooq, F. Aslam, M.A. Musarat, N.I. Vatin, 2021. Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP, Mater. 2021, Vol. 14, Page 7531. 14 (2021) 7531. https://doi.org/https://doi.org/10.3390/ma14247531.
- Yang, (2018), Big Geospatial Data Data Sci., 1, pp. 1, 10.23977/bgdds.2018.11001
- Murphy, (2012)
- Al Daoud, (2019), Int. J. Comput. Inf. Eng., 13, pp. 6
- Liu, (2017), IEEE Access, 5, pp. 24417, 10.1109/ACCESS.2017.2766203
- F. Li, J. Wu, F. Dong, J. Lin, G. Sun, H. Chen, J. Shen, 2018. Ensemble Machine Learning Systems for the Estimation of Steel Quality Control, Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018. (2019) 2245–2252. https://doi.org/10.1109/BigData.2018.8622583.
- Marani, (2020), Constr. Build. Mater., 265, pp. 1, 10.1016/j.conbuildmat.2020.120286
- Ben Jabeur, (2021), Technol. Forecast. Soc. Change, 166
- Olu-Ajayi, (2022), J. Build. Eng., 45
- Altman, (1992), Am. Stat., 46, pp. 175
- Breiman, (2001), Mach. Learn., 45, pp. 5, 10.1023/A:1010933404324
- Geurts, (2006), Mach. Learn., 63, pp. 3, 10.1007/s10994-006-6226-1
- Friedman, (2001), Ann. Stat., 29, pp. 1189, 10.1214/aos/1013203451
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.Y. Liu, 2017. LightGBM: A highly efficient gradient boosting decision tree, NIPS’17 Prpoceedings 31st Int. Conf. Neural Inf. Process. Syst. 2017-Decem (2017) 3147–3155. 〈https://doi.org/10.5555/3294996.3295074〉.
- T. Chen, C. Guestrin, 2016. XGBoostr: A Scalable Tree Boosting System, KDD’16 Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining. August 201 (2016) 785–794. 〈https://doi.org/10.1145/2939672.2939785〉.
- L. Prokhorenkova, G. Gusev, A. Vorobev, A.V. Dorogush, A. Gulin, 2018. Catboost: Unbiased boosting with categorical features, NIPS’18 Proc. 32nd Int. Conf. Neural Inf. Process. Syst. 2018-Decem (2018) 6638–6648. 〈https://doi.org/10.5555/3327757.3327770〉.
- Wood, (2017)
- Servén, (2018), Zenodo
- Ali, (2012), Constr. Build. Mater., 35, pp. 785, 10.1016/j.conbuildmat.2012.04.117
- Nili, (2019), Materials, 12, pp. 1120, 10.3390/ma12071120
- Aslani, (2018), J. Clean. Prod., 182, pp. 553, 10.1016/j.jclepro.2018.02.074
- Pan, (2019), Constr. Build. Mater., 200, pp. 570, 10.1016/j.conbuildmat.2018.12.150
- Babalola, (2020), J. Mater. Res. Technol., 9, pp. 6521, 10.1016/j.jmrt.2020.04.038
- Pereira-De-Oliveira, (2014), Constr. Build. Mater., 51, pp. 113, 10.1016/j.conbuildmat.2013.10.061
- Bahrami, (2020), J. Build. Eng., 31
- Poongodi, (2021), Silicon, 13, pp. 2727, 10.1007/s12633-020-00635-7
- Barroqueiro, (2020), Buildings, 10, pp. 1, 10.3390/buildings10060113
- Revathi, (2013), J. Inst. Eng. Ser. A., 94, pp. 179, 10.1007/s40030-014-0051-5
- Behera, (2019), Constr. Build. Mater., 228, 10.1016/j.conbuildmat.2019.116819
- Revilla-Cuesta, (2020), Constr. Build. Mater., 263, 10.1016/j.conbuildmat.2020.120671
- Bidabadi, (2020), J. Build. Eng., 32
- Sadeghi-Nik, (2019), J. Sci. Technol. - Trans. Civ. Eng., 45, pp. 503, 10.1007/s40996-018-0182-4
- Chakkamalayath, (2020), Asian J. Civ. Eng., 21, 10.1007/s42107-020-00242-2
- Salesa, (2017), Constr. Build. Mater., 153, pp. 364, 10.1016/j.conbuildmat.2017.07.087
- Duan, (2020), Constr. Build. Mater., 10.1016/j.conbuildmat.2020.119323
- Sasanipour, (2019), Constr. Build. Mater., 228, 10.1016/j.conbuildmat.2019.117054
- Fiol, (2018), Constr. Build. Mater., 182, pp. 309, 10.1016/j.conbuildmat.2018.06.132
- Sasanipour, (2020), Constr. Build. Mater., 236, 10.1016/j.conbuildmat.2019.117540
- Gesoglu, (2015), Constr. Build. Mater., 98, pp. 334, 10.1016/j.conbuildmat.2015.08.036
- Grdic, (2010), Constr. Build. Mater., 24, pp. 1129, 10.1016/j.conbuildmat.2009.12.029
- Sharifi, (2013), Front. Struct. Civ. Eng., Vol. 7, pp. 419, 10.1007/s11709-013-0224-8
- Güneyisi, (2014), Constr. Build. Mater., 64, pp. 172, 10.1016/j.conbuildmat.2014.04.090
- Silva, (2017), Eur. J. Environ. Civ. Eng., 21, pp. 430, 10.1080/19648189.2015.1131200
- Guo, (2020), Constr. Build. Mater., 231, 10.1016/j.conbuildmat.2019.117115
- Singh, (2015), J. Water Resour. Hydraul. Eng., 4, pp. 398, 10.5963/JWRHE0404011
- Kapoor, (2016), Int. J. Civ. Eng., 16, pp. 47, 10.1007/s40999-016-0062-x
- Singh, (2019), J. Hazard., Toxic. Radioact. Waste, 23, 10.1061/(ASCE)HZ.2153-5515.0000456
- Sua-Iam, (2013), Constr. Build. Mater., 47, pp. 701, 10.1016/j.conbuildmat.2013.05.065
- Khafaga, (2014), World Appl. Sci. J., 29, pp. 465
- Sun, (2020), Resour. Conserv. Recycl., 161, 10.1016/j.resconrec.2020.104930
- Khodair, (2017), J. Build. Eng., 12, pp. 282, 10.1016/j.jobe.2017.06.007
- Surendar, (2021), Mater. Today Proc., 44, pp. 1723, 10.1016/j.matpr.2020.11.896
- Kou, (2009), Cem. Concr. Compos., 31, pp. 622, 10.1016/j.cemconcomp.2009.06.005
- Tang, (2016), Adv. Mater. Sci. Eng., 3, pp. 1
- Krishna, (2018), Int. J. Civ. Eng. Technol., 9, pp. 1672
- Thomas, (2016), Constr. Build. Mater., 114, pp. 536, 10.1016/j.conbuildmat.2016.03.203
- Vinay Kumar, (2017), J. Build. Eng., 9, pp. 100, 10.1016/j.jobe.2016.11.013
- Tuyan, (2014), Mater. Des., 53, pp. 983, 10.1016/j.matdes.2013.07.100
- Li, (2019), J. Clean. Prod., 236, 10.1016/j.jclepro.2019.117707
- Uygunoğlu, (2014), J. Clean. Prod., 84, pp. 691, 10.1016/j.jclepro.2014.06.019
- W. Long, J. Shi, W. Wang, X. Fang, 2016. Shrinkage of Hybrid Fiber Reinforced Self- Consolidating Concrete with Recycled Aggregate, en: K.H. Khayat (Ed.), SCC-2016. 8th Int. RILEM Symp. Self-Compacting Concr. Flow. Towar. Sustain., Washington, D.C., USA, 2016: pp. 751–762. 〈https://cies.mst.edu/media/research/cies/documents/SCC2016NPRConferenceProceedings.pdf〉.
- Wang, (2020), J. Clean. Prod., 277, 10.1016/j.jclepro.2020.123180
- Mahakavi, (2020), Aust. J. Struct. Eng., 21, pp. 33, 10.1080/13287982.2019.1636519
- Yu, (2014), Appl. Mech. Mater., 638, pp. 1494, 10.4028/www.scientific.net/AMM.638-640.1494
- Manzi, (2017), Constr. Build. Mater., 157, pp. 582, 10.1016/j.conbuildmat.2017.09.129
- Yu, (2020), Front. Struct. Civ. Eng., 14, pp. 760, 10.1007/s11709-020-0618-3
- Yu, (2021), J. Civ. Eng. Manag., 27, pp. 188, 10.3846/jcem.2021.14117
- Mo, (2021), Waste Biomass. Valoriz., 12, pp. 1133, 10.1007/s12649-020-01045-x
- Zhou, (2013), Adv. Mater. Res., 639–640, pp. 399, 10.4028/www.scientific.net/AMR.639-640.399
- Nieto, (2019), J. Mater. Civ. Eng., 31, 10.1061/(ASCE)MT.1943-5533.0002566
- Hassan, (2020), Energies, 1735
- Ahmad, (2021), Materials, 14, pp. 794, 10.3390/ma14040794
- Montaño Moreno, (2013), Psicothema, 25, pp. 500
- Vivas, (2020), Entropy, 22, pp. 1412, 10.3390/e22121412
- Kang, (2021), Constr. Build. Mater., 266, 10.1016/j.conbuildmat.2020.121117