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http://dx.doi.org/10.12989/cac.2022.30.2.099

Predicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighbor  

Turk, Kazim (Department of Civil Engineering, Engineering Faculty, Inonu University)
Kina, Ceren (Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University)
Tanyildizi, Harun (Department of Civil Engineering, Technology Faculty, Firat University)
Publication Information
Computers and Concrete / v.30, no.2, 2022 , pp. 99-111 More about this Journal
Abstract
In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.
Keywords
deep learning; durability factor; k-nearest neighbor; prediction; self-compacting mortar; sorptivity coefficient;
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1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 Astm C666/C666M (2003), Standard Test Method for Resistance of Concrete to Rapid Freezing and Thawing, ASTM International, West Conshohocken, PA.
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.   DOI
13 Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017), "ImageNet classification with deep convolutional neural networks", Communications of the ACM, 84-90.
14 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.   DOI
15 Jepsen, M.T. (2002), "Predicting concrete durability by using artificial neural network", Durability of Exposed Concrete containing Secondary Cementitious Materials, 1-12.
16 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.   DOI
17 Altman, N.S. (1992), "An introduction to kernel and nearest-neighbor nonparametric regression", Am. Statist., 46(3), 175-185.
18 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.
19 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.   DOI
20 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.   DOI
21 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.   DOI
22 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.   DOI
23 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.   DOI
24 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.   DOI
25 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.   DOI
26 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.
27 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.   DOI
28 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.   DOI
29 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.   DOI
30 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.   DOI
31 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.   DOI
32 Ross, T.J. (2010), Fuzzy Logic with Engineering Applications: Third Edition, John Wiley & Sons.
33 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.   DOI
34 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.   DOI
35 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.   DOI
36 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.   DOI
37 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.   DOI
38 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.   DOI
39 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.   DOI
40 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.   DOI
41 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.   DOI
42 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.   DOI
43 Shakhnarovich, G., Darrell, T. and Indyk, P. (2018), "Nearest-neighbor methods in learning and vision", IEEE Trans. Neur. Network., 19(2), 377.
44 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.   DOI
45 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.   DOI
46 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.
47 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.   DOI
48 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.   DOI
49 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.   DOI
50 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.   DOI
51 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.   DOI
52 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.   DOI
53 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.   DOI
54 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.   DOI
55 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.   DOI
56 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.   DOI
57 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.   DOI
58 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.   DOI
59 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.
60 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.   DOI
61 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.   DOI
62 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.
63 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.   DOI
64 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.   DOI
65 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.   DOI
66 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.   DOI
67 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.   DOI
68 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.   DOI
69 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.   DOI
70 EFNARC (2002), Specification and Guidelines for Self-Compacting Concrete, Report from EFNARC.
71 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.
72 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.   DOI
73 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.   DOI