References
- Abuodeh, O.R., Abdalla, J.A. and Hawileh, R.A. (2020), "Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques", Appl. Soft Comput. J., 95,106552. https://doi.org/10.1016/j.asoc.2020.106552.
- Akyuncu, V., Uysal, M., Tanyildizi, H. and Sumer, M. (2019), "Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network", Revista de la Construccion, 17(3), 337-353. https://doi.org/10.7764/rdlc.17.3.337.
- Al-Shamiri, A.K., Kim, J.H., Yuan, T.F. and Yoon, Y.S. (2019), "Modeling the compressive strength of high-strength concrete:An extreme learning approach", Constr. Build. Mater., 208, 204-219. https://doi.org/10.1016/j.conbuildmat.2019.02.165.
- Altman, N.S. (1992), "An introduction to kernel and nearest-neighbor nonparametric regression", Am. Statist., 46(3), 175-185.
- Amorim Junior, N.S., Silva, G.A.O. and Ribeiro, D.V. (2018), "Effects of the incorporation of recycled aggregate in the durability of the concrete submitted to freeze-thaw cycles", Constr. Build. Mater., 161, 723-730. https://doi.org/10.1016/j.conbuildmat.2017.12.076.
- Astm C666/C666M (2003), Standard Test Method for Resistance of Concrete to Rapid Freezing and Thawing, ASTM International, West Conshohocken, PA.
- Azimi-Pour, M., Eskandari-Naddaf, H. and Pakzad, A. (2020), "Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete", Constr. Build. Mater., 230, 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021.
- Bagheri, A., Zanganeh, H., Alizadeh, H., Shakerinia, M. and Marian, M.A.S. (2013), "Comparing the performance of fine fly ash and silica fume in enhancing the properties of concretes containing fly ash", Constr. Build. Mater., 47, 1402-1408. https://doi.org/10.1016/j.conbuildmat.2013.06.037.
- Bao, J., Li, S., Zhang, P., Ding, X., Xue, S., Cui, Y. and Zhao, T. (2020), "Influence of the incorporation of recycled coarse aggregate on water absorption and chloride penetration into concrete", Constr. Build. Mater., 239, 117845. https://doi.org/10.1016/j.conbuildmat.2019.117845.
- Beckman, G.H., Polyzois, D. and Cha, Y.J. (2019), "Deep learning-based automatic volumetric damage quantification using depth camera", Auto. Constr., 99, 114-124. https://doi.org/10.1016/j.autcon.2018.12.006.
- Bravo, M., De Brito, J., Pontes, J. and Evangelista, L. (2015), "Durability performance of concrete with recycled aggregates from construction and demolition waste plants", Constr. Build. Mater., 77, 357-369. https://doi.org/10.1016/j.conbuildmat.2014.12.103.
- Busic, R., Bensic, M., Milicevic, I. and Strukar, K. (2020), "Prediction models for the mechanical properties of self-compacting concrete with recycled rubber and silica fume", Mater., 13(8), 1821. https://doi.org/10.3390/ma13081821.
- Ben Chaabene, W., Flah, M. and Nehdi, M.L. (2020), "Machine learning prediction of mechanical properties of concrete: Critical review", Constr. Build. Mater., 260, 119889. https://doi.org/10.1016/j.conbuildmat.2020.119889.
- Choudhary, R., Gupta, R., Nagar, R. and Jain, A. (2020), "Sorptivity characteristics of high strength self-consolidating concrete produced by marble waste powder, fly ash, and micro silica", Mater. Today: Proc., 32, 531-535. https://doi.org/10.1016/j.matpr.2020.01.287.
- Douma, O.B., Boukhatem, B. and Ghrici, M. (2014), "Prediction compressive strength of self-compacting concrete containing fly ash using fuzzy logic inference system", Int. J. Civil Environ. Struct. Constr. Arch. Eng., 8(12), 1285-1289.
- EFNARC (2002), Specification and Guidelines for Self-Compacting Concrete, Report from EFNARC.
- Elevado, K.J.T., Galupino, J.G. and Gallardo, R.S. (2018), "Compressive strength modelling of concrete mixed with fly ash and waste ceramics using K-nearest neighbor algorithm", Int. J. GEOMATE, 15(48), 169-174. https://doi.org/10.21660/2018.48.99305.
- Fisher, G.L., Prentice, B.A., Sllberman, D., Ondov, J.M., Biermann, A.H., Ragainl, R.C. and McFarl, A.R. (1978), "Physical and morphological studies of size-classified coal fly ash", Environ. Sci. Technol., 12(4), 447-451. https://doi.org/10.1021/es60140a008.
- Flah, M., Suleiman, A.R. and Nehdi, M.L. (2020), "Classification and quantification of cracks in concrete structures using deep learning image-based techniques", Cement Concrete Compos., 114, 103781. https://doi.org/10.1016/j.cemconcomp.2020.103781.
- Gao, P.W., Wu, S.X., Lin, P.H., Wu, Z.R. and Tang, M.S. (2006), "The characteristics of air void and frost resistance of RCC with fly ash and expansive agent", Constr. Build. Mater., 20(8), 586-590. https://doi.org/10.1016/j.conbuildmat.2005.01.039.
- Gers, F.A., Schmidhuber, J. and Cummins, F. (2000), "Learning to forget: Continual prediction with LSTM", Neur. Comput., 12(10), 2451-2471. https://doi.org/10.1162/089976600300015015.
- Gers, F.A., Schraudolph, N.N. and Schmidhuber, J. (2003), "Learning precise timing with LSTM recurrent networks", J. Mach. Learn. Res., 3(1), 115-143.
- Gesoglu, M. and Ozbay, E. (2007), "Effects of mineral admixtures on fresh and hardened properties of self-compacting concretes: Binary, ternary and quaternary systems", Mater. Struct./Materiaux et Constr., 40(9), 923-937. https://doi.org/10.1617/s11527-007-9242-0.
- Guler, S., Yavuz, D., Korkut, F. and Ashour, A. (2019), "Strength prediction models for steel, synthetic, and hybrid fiber reinforced concretes", Struct. Concrete, 20(1), 428-445. https://doi.org/10.1002/suco.201800088.
- Hashmpour, M. and Heidari, A. (2018), "Investigation of mechanical properties of self compacting polymeric concrete with backpropagation network", Int. J. Eng., 31(6), 903-909. https://doi.org/10.5829/IJE.2018.31.06C.06.
- Hatungimana, D., Taskopru, C., Ichedef, M., Sac, M.M. and Yazici, S. (2019), "Compressive strength, water absorption, water sorptivity and surface radon exhalation rate of silica fume and fly ash based mortar", J. Build. Eng., 23, 369-376. https://doi.org/10.1016/j.jobe.2019.01.011.
- Cheng-Yi, H. and Feldman, R.F. (1985), "Dependence of frost resistance on the pore structure of mortar containing silica fume", J. Proc., 82(5), 740-743.
- Jang, K., Kim, N. and An, Y.K. (2019), "Deep learning-based autonomous concrete crack evaluation through hybrid image scanning", Struct. Hlth. Monit., 18(5-6), 1722-1737. https://doi.org/10.1177/1475921718821719.
- Jepsen, M.T. (2002), "Predicting concrete durability by using artificial neural network", Durability of Exposed Concrete containing Secondary Cementitious Materials, 1-12.
- Karahan, O., Tanyildizi, H. and Atis, C.D. (2008), "An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash", J. Zhejiang Univ.: Sci. A, 9(11), 1514-1523. https://doi.org/10.1631/jzus.A0720136.
- Khandelwal, M. and Singh, T.N. (2007), "Evaluation of blast-induced ground vibration predictors", Soil Dyn. Earthq. Eng., 27(2), 116-125. https://doi.org/10.1016/j.soildyn.2006.06.004.
- Kina, C., Turk, K., Atalay, E., Donmez, I. and Tanyildizi, H. (2021), "Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC", Neur. Comput. Appl., 33(18), 11641-11659. https://doi.org/10.1007/s00521-021-05836-8.
- Kina, C., Turk, K. and Tanyildizi, H. (2022a), "Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models ", Struct. Concrete, https://doi.org/10.1002/suco.202100622.
- Kina, C., Turk, K. and Tanyildizi, H. (2022b), "Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete", Struct. Concrete, https://doi.org/10.1002/suco.202100756.
- Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017), "ImageNet classification with deep convolutional neural networks", Communications of the ACM, 84-90.
- Kumar, S., Rai, B., Biswas, R., Samui, P. and Kim, D. (2020), "Prediction of rapid chloride permeability of self-compacting concrete using multivariate adaptive regression spline and minimax probability machine regression", J. Build. Eng., 32, 101490. https://doi.org/10.1016/j.jobe.2020.101490.
- Lee, B. and Lee, J.S. (2018), "Freeze-thaw resistance estimation of concrete using surface roughness and image analysis", J. Korea Inst. Struct. Mainten. Inspec., 22(3), 1-7. https://doi.org/10.11112/jksmi.2018.22.3.001.
- Leung, H.Y., Kim, J., Nadeem, A., Jaganathan, J. and Anwar, M.P. (2016), "Sorptivity of self-compacting concrete containing fly ash and silica fume", Constr. Build. Mater., 113, 369-375. https://doi.org/10.1016/j.conbuildmat.2016.03.071.
- Liu, G., Bao, H. and Han, B. (2018), "A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis", Math. Prob. Eng., 2018, Article ID 5105709. https://doi.org/10.1155/2018/5105709.
- Liu, M. (2010), "Self-compacting concrete with different levels of pulverized fuel ash", Constr. Build. Mater., 24(7), 1245-1252. https://doi.org/10.1016/j.conbuildmat.2009.12.012.
- Mardani-Aghabaglou, A. andic-Cakir, O. and Ramyar, K. (2013), "Freeze-thaw resistance and transport properties of high-volume fly ash roller compacted concrete designed by maximum density method", Cement Concrete Compos., 37(1), 259-266. https://doi.org/10.1016/j.cemconcomp.2013.01.009.
- Mardani-Aghabaglou, A., Inan Sezer, G. and Ramyar, K. (2014), "Comparison of fly ash, silica fume and metakaolin from mechanical properties and durability performance of mortar mixtures view point", Constr. Build. Mater., 70, 17-25. https://doi.org/10.1016/j.conbuildmat.2014.07.089.
- Martins, F.F. and Camoes, A. (2019), "Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques", Revista Materia, 24(4), 1. https://doi.org/10.1590/S1517-707620190004.0852.
- Mirgozar Langaroudi, M.A. and Mohammadi, Y. (2022), "Effect of nano-clay on the freeze-thaw resistance of self-compacting concrete containing mineral admixtures", Eur. J. Environ. Civil Eng., 26(2), 481-500. https://doi.org/10.1080/19648189.2019.1665107.
- Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020), "Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581.
- Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005.
- Pitchaipillai, N. and Paramasivam, S.K. (2019), "Deep neural network-based mechanical behavior analysis for various masonry infill walls with hybrid fiber mortar", Struct. Concrete, 20(6), 1974-1985. https://doi.org/10.1002/suco.201900064.
- Pospichal, O., Kucharczykova, B., Misak, P. and Vymazal, T. (2010), "Freeze-thaw resistance of concrete with porous aggregate", Procedia Eng., 2(1), 521-529. https://doi.org/10.1016/j.proeng.2010.03.056.
- Ross, T.J. (2010), Fuzzy Logic with Engineering Applications: Third Edition, John Wiley & Sons.
- Hochreiter, S. and Schmidhuber, J. (1997), "Long short-term memory", Neur. Comput., 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
- Scrivener, K.L., Crumbie, A.K. and Laugesen, P. (2004), "The interfacial transition zone (ITZ) between cement paste and aggregate in concrete", Interf. Sci., 12(4), 411-421. https://doi.org/10.1023/B:INTS.0000042339.92990.4c.
- Shakhnarovich, G., Darrell, T. and Indyk, P. (2018), "Nearest-neighbor methods in learning and vision", IEEE Trans. Neur. Network., 19(2), 377.
- Shehata, M.H. and Thomas, M.D.A. (2002), "Use of ternary blends containing silica fume and fly ash to suppress expansion due to alkali-silica reaction in concrete", Cement Concrete Res., 32(3), 341-349. https://doi.org/10.1016/S0008-8846(01)00680-9.
- Shon, C.S., Abdigaliyev, A., Bagitova, S., Chung, C.W. and Kim, D. (2018), "Determination of air-void system and modified frost resistance number for freeze-thaw resistance evaluation of ternary blended concrete made of ordinary Portland cement/silica fume/class F fly ash", Cold Reg. Sci. Technol., 155, 127-136. https://doi.org/10.1016/j.coldregions.2018.08.003.
- Sun, Y., Li, G. and Zhang, J. (2020), "Developing hybrid machine learning models for estimating the unconfined compressive strength of jet grouting composite: A comparative study", Appl. Sci. (Switzerland), 10(5), 1612. https://doi.org/10.3390/app10051612.
- Tanyildizi, H. (2017), "Prediction of compressive strength of lightweight mortar exposed to sulfate attack", Comput. Concrete, 19(2), 217-226. https://doi.org/10.12989/cac.2017.19.2.217.
- Turk, K. (2012), "Viscosity and hardened properties of self-compacting mortars with binary and ternary cementitious blends of fly ash and silica fume", Constr. Build. Mater., 37, 326-334. https://doi.org/10.1617/s11527-007-9345-7.
- Turk, K., Caliskan, S. and Yazicioglu, S. (2007), "Capillary water absorption of self-compacting concrete under different curing conditions", Ind. J. Eng. Mater. Sci., 14(5), 365-372.
- Turk, K., Karatas, M. and Gonen, T. (2013), "Effect of fly ash and silica fume on compressive strength, sorptivity and carbonation of SCC", KSCE J. Civil Eng., 17(1), 202-209. https://doi.org/10.1007/s12205-013-1680-3.
- Turk, K. and Kina, C. (2018), "Freeze-thaw resistance and sorptivity of self-compacting mortar with ternary blends", Comput. Concrete, 21(2), 149-156. https://doi.org/10.12989/cac.2018.21.2.149.
- Kandil, U., Erdogdu, S. and Kurbetci, S. (2017), "Permeation properties of concretes incorporating fly ash and silica fume", Comput. Concrete, 19(4), 357-363. https://doi.org/10.12989/cac.2017.19.4.357.
- Uysal, M. and Tanyildizi, H. (2012), "Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network", Constr. Build. Mater., 27(1), 404-414. https://doi.org/10.1016/j.conbuildmat.2011.07.028.
- Veda Samhitha, K., Srinivasa Reddy, V., Seshagiri Rao, M.V. and Shrihari, S. (2019), "Performance evaluation of high-strength high-volume fly ash concrete", Int. J. Recent Technol. Eng., 8(3), 5990-5994.
- Wang, D., Zhou, X., Meng, Y. and Chen, Z. (2017), "Durability of concrete containing fly ash and silica fume against combined freezing-thawing and sulfate attack", Constr. Build. Mater., 147, 398-406. https://doi.org/10.1016/j.conbuildmat.2017.04.172.
- Wang, Q., Yan, P. and Feng, J. (2012), "The influence of mineral admixtures on bending strength of mortar on the premise of equal compressive strength", J. Wuhan Univ. Technol., Materi. Sci. Ed., 27(3), 586-589. https://doi.org/10.1007/s11595-012-0510-7.
- Wang, Y., Gong, F., Zhang, D. and Ueda, T. (2016), "Estimation of ice content in mortar based on electrical measurements under freeze-thaw cycle", J. Adv. Concrete Technol., 14(2), 35-46. https://doi.org/10.3151/jact.14.35.
- Wawrzenczyk, J. and Klak, A. (2015), "Prediction of freeze-thaw resistance of GGBFS concrete based on ANN models", Arch. Civil Eng. Environ., 8(4), 61-66.
- Wu, W., Wang, R., Zhu, C. and Meng, Q. (2018), "The effect of fly ash and silica fume on mechanical properties and durability of coral aggregate concrete", Constr. Build. Mater., 185, 69-78. https://doi.org/10.1016/j.conbuildmat.2018.06.097.
- Xie, Y., Yu, B., Wu, X. and Fan, Y. (2011), "Influence of mineral admixture on concrete abrasion resistance", Adv. Mater. Res., 168-170, 78-81. https://doi.org/10.4028/www.scientific.net/AMR.168-170.78.
- Xu, J. and Yu, X. (2020), "Detection of concrete structural defects using impact echo based on deep networks", J. Test. Eval., 49(1), 109-120. https://doi.org/10.1520/JTE20190801.
- Yang, L. and An, X. (2020), "Estimating the workability of self-compacting concrete in different mixing conditions based on deep learning", Comput. Concrete, 25(5), 433-445. https://doi.org/10.12989/cac.2020.25.5.433.
- Yang, Y. and Zhang, Q. (1997), "A hierarchical analysis for rock engineering using artificial neural networks", Rock Mech. Rock Eng., 30(4), 207-222. https://doi.org/10.1007/BF01045717.
- Zhang, J., Huang, Y., Aslani, F., Ma, G. and Nener, B. (2020), "A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete", J. Clean. Prod., 273, 122922. https://doi.org/10.1016/j.jclepro.2020.122922.