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http://dx.doi.org/10.12989/acc.2020.10.3.247

Prediction of mechanical properties of limestone concrete after high temperature exposure with artificial neural networks  

Blumauer, Urska (Faculty of Civil and Geodetic Engineering, University of Ljubljana)
Hozjan, Tomaz (Faculty of Civil and Geodetic Engineering, University of Ljubljana)
Trtnik, Gregor (Building Materials Institute)
Publication Information
Advances in concrete construction / v.10, no.3, 2020 , pp. 247-256 More about this Journal
Abstract
In this paper the possibility of using different regression models to predict the mechanical properties of limestone concrete after exposure to high temperatures, based on the results of non-destructive techniques, that could be easily used in-situ, is discussed. Extensive experimental work was carried out on limestone concrete mixtures, that differed in the water to cement (w/c) ratio, the type of cement and the quantity of superplasticizer added. After standard curing, the specimens were exposed to various high temperature levels, i.e., 200℃, 400℃, 600℃ or 800℃. Before heating, the reference mechanical properties of the concrete were determined at ambient temperature. After the heating process, the specimens were cooled naturally to ambient temperature and tested using non-destructive techniques. Among the mechanical properties of the specimens after heating, known also as the residual mechanical properties, the residual modulus of elasticity, compressive and flexural strengths were determined. The results show that residual modulus of elasticity, compressive and flexural strengths can be reliably predicted using an artificial neural network approach based on ultrasonic pulse velocity, residual surface strength, some mixture parameters and maximal temperature reached in concrete during heating.
Keywords
residual mechanical properties; compressive strength; artificial neural network; non-destructive testing techniques; fire behavior; concrete;
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1 Abbas, H., Al-Salloum, Y.A., Elsanadedy, H.M. and Almusallam, T.H. (2019), "ANN models for prediction of residual strength of HSC after exposure to elevated temperatures", Fire Saf. J., 106, 13-28. https://doi.org/10.1016/j.firesaf.2019.03.011.   DOI
2 Ada, M., Sevim, B., Yuzer, N. and Ayvaz, Y. (2018), "Assessment of damages on a RC building after a big fire", Adv. Concrete Constr., 6(2), 177-197. https://doi.org/10.12989/acc.2018.6.2.177.   DOI
3 Arioz, O. (2007), "Effects of elevated temperatures on properties of concrete", Fire. Saf. J., 42(8), 516-522. https://doi.org/10.1016/j.firesaf.2007.01.003.   DOI
4 Arioz, O. (2009), "Retained properties of concrete exposed to high temperatures: Size effect", Fire Mater., 33, 211-222. https://doi.org/10.1002/fam.996.   DOI
5 Aslani, F. and Samali, B. (2013), "Predicting the bond between concrete and reinforcing steel at elevated temperatures", Struct. Eng. Mech., 48(5), 643-660. http://dx.doi.org/10.12989/sem.2013.48.5.643.   DOI
6 Cadorin, J.F. and Franssen, J.M. (2003), "A tool to design steel elements submitted to compartment fires - OZone V2. Part 1: pre- and post-flashover compartment fire model", Fire Saf. J., 38, 395-427. https://doi.org/10.1016/S0379-7112(03)00014-6.   DOI
7 Chaix, J.F., Garnier, V. and Corneloup, G. (2003), "Concrete damage evolution analysis by backscattered ultrasonic waves", NDT E Int., 36(7), 461-469. https://doi.org/10.1016/S0963-8695(03)00066-5.   DOI
8 Chan, Y.N., Jin, P., Anson, M. and Wang, J.S. (1998), "Fire resistance of concrete: prediction using artificial neural networks", Mag. Concrete Res., 50(4), 353-358. https://doi.org/10.1680/macr.1998.50.4.353.   DOI
9 Dolinar, U., Trtnik, G., Turk, G. and Hozjan, T. (2019), "The feasibility of estimation of mechanical properties of limestone concrete after fire using nondestructive methods", Constr. Build. Mater., 228, 116786. https://doi.org/10.1016/j.conbuildmat.2019.116786.   DOI
10 dos Santos, C.C. and Rodrigues, J.P.C. (2016), "Calcareous and granite aggregate concretes after fire", J. Build. Eng., 8, 231-242. https://doi.org/10.1016/j.jobe.2016.09.009.   DOI
11 EN 12390-3:2009, Testing Hardened Concrete - Part 3: Compressive Strength of Test Specimens.
12 EN 12390-5:2009, Testing Hardened Concrete - Part 5: Flexural Strength of Test Specimens.
13 EN 12504-2:2002, Testing Concrete in Structures - Part 2: Non-Destructive Testing - Determination of Rebound Number.
14 EN 12504-4: 2004, Testing Concrete - Part 4: Determination of Ultrasonic Pulse Velocity.
15 EN 1992-1-2:2004, Eurocode 2: Design of Concrete Structures - Part 1-2: General Rules - Structural Fire Design.
16 Gupta, T., Patel, K.A., Siddique, S., Sharma, R.K. and Chaudhary, S. (2019), "Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN", Measure., 147, 106870. https://doi.org/10.1016/j.measurement.2019.106870.
17 Hagan, M.T., Demuth, H.B., Beale, M.H. and De Jesus, O. (2014), Neural Network Design, 2nd Edition, Self Published.
18 Haykin, S. (2009), Neural Networks and Learning Machines, Pearson Education, Inc., Upper Saddle River, New Jersy.
19 Hertz, K.D. (2005), "Concrete strength for fire safety design", Mag. Concrete Res., 57(8), 445-453. https://doi.org/10.1680/macr.2005.57.8.445.   DOI
20 Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neur. Network., 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8.   DOI
21 ISO 1920-10:2010, Testing of Concrete - Part 10: Determination of Static Modulus of Elasticity in Compression.
22 Krzemien, K. and Hager, I. (2015), "Post-fire assessment of mechanical properties of concrete with the use of the impact-echo method", Constr. Build. Mater., 96, 155-163. https://doi.org/10.1016/j.conbuildmat.2015.08.007.   DOI
23 Ma, Q.M., Guo, R.X., Zhao, Z.M., Lin, Z.W. and He, K.C. (2015), "Mechanical properties of concrete at high temperature - a review", Constr. Build. Mater., 93, 371-383. http://dx.doi.org/10.1016/j.conbuildmat.2015.05.131.   DOI
24 Matlab (1999) The Language of Technical Computing, The Mathworks Inc.
25 Molkens, T., Van Coile, R. and Gernay, T. (2017), "Assessment of damage and residual load bearing capacity of concrete slab after fire: Applied reliability-based methodology", Eng. Struct., 150, 969-985. http://dx.doi.org/10.1016/j.engstruct.2017.07.078.   DOI
26 Park, G.K. and Yim, H.J. (2017), "Evaluation of fire-damaged concrete: An experimental analysis based on destructive and nondestructive methods", Int. J. Concrete Struct. Mater., 11(3), 447-457. https://doi.org/10.1007/s40069-017-0211-x.   DOI
27 Park, S.J. and Yim, H.J. (2016), "Evaluation of residual mechanical properties of concrete after exposure to high temperatures using impact resonance method", Constr. Build. Mater., 129, 89-97. https://doi.org/10.1016/j.conbuildmat.2016.10.116.   DOI
28 Park, S.J., Park, G.K., Yim, H.J. and Kwak, H.G. (2015), "Evaluation of residual tensile strength of fire-damaged concrete using a non-linear resonance vibration method", Mag. Concrete Res., 67(5), 235-246. https://doi.org/10.1680/macr.14.00259.   DOI
29 Payan, C., Garnier, V., Moysan, J. and Johnson, P.A. (2007), "Applying nonlinear resonant ultrasound spectroscopy to improving thermal damage assessment in concrete", J. Acoust. Soc. Am., 121(4), EL125-EL130. https://doi.org/10.1121/1.2710745.   DOI
30 Park, S.J., Yim, H.J. and Kwak, H.G. (2014), "Nonlinear resonance vibration method to estimate the damage level on heat-exposed concrete", Fire Saf. J., 69, 36-42. https://doi.org/10.1016/j.firesaf.2014.07.003.   DOI
31 Savva, A., Manita, P. and Sideris, K.K. (2005), "Influence of elevated temperatures on the mechanical properties of blended cement concretes prepared with limestone and siliceous aggregates", Cement Concrete Compos., 27(2), 239-248. https://doi.org/10.1016/j.cemconcomp.2004.02.013.   DOI
32 Shah, A.A., Alsayed, S.H., Abbas, H. and Al-Salloum, Y.A. (2012), "Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network", Constr. Build. Mater., 29, 42-50. https://doi.org/10.1016/j.conbuildmat.2011.10.038.   DOI
33 Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonics, 49(1), 53-60. https://doi.org/10.1016/j.ultras.2008.05.001.   DOI
34 Turkmen, I., Bingol, A.F., Tortum, A., Demirboga, R. and Gul, R. (2017), "Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models", Fire Mater., 41, 142-153. https://doi.org/10.1002/fam.2374.   DOI
35 Vakharia, V. and Gujar, R. (2019), "Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques", Constr. Build. Mater., 225, 292-301. https://doi.org/10.1016/j.conbuildmat.2019.07.224.   DOI
36 Yaqub, M. and Bailey, C.G. (2016), "Non-destructive evaluation of residual compressive strength of post-heated reinforced concrete columns", Constr. Build. Mater., 120, 482-493. https://doi.org/10.1016/j.conbuildmat.2016.05.022.   DOI
37 Varona, F.B., Baeza, F.J., Bru, D. and Ivorra, S. (2018), "Evolution of the bond strength between reinforcing steel and fibre reinforced concrete after high temperature exposure", Constr. Build. Mater., 176, 359-370. https://doi.org/10.1016/j.conbuildmat.2018.05.065.   DOI
38 Varona, F.B., Baeza, F.J., Bru, D. and Ivorra, S. (2020), "Non-linear multivariable model for predicting the steel to concrete bond after high temperature exposure", Constr. Build. Mater., 249, 118713. https://doi.org/10.1016/j.conbuildmat.2020.118713.   DOI
39 Yang, O., Zhang, B., Yan, G. and Chen, J. (2018), "Bond performance between slightly corroded steel bar and concrete after exposure to high temperature", J. Struct. Eng., 144(11), 04018209. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002217.   DOI
40 Yonaba, H., Anctil, F. and Fortin, V. (2010), "Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting", J. Hydrol. Eng., 15(4), 275-283. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000188.   DOI