Evaluation of neural network model for compressive strength of the steel fiber reinforced concrete using break-off method

Document Type : Original Article


1 Civil engineering Dept, technical faculty, university of giulan

2 Ph.D Candidate in Civil Engineering(Structural Engineering) University of Guilan

3 Assistant Professor in Civil Engineering(Structural Engineering) University of Guilan


In the present paper break-off test as a partially-destructive method is used for durability evaluation of steel fiber reinforced concrete. In recent years, utilizations of steel fibers have been known as an appropriate solution method for sudden fracture of concrete. In order to provide a comprehensive statistical database, 24 mixtures are designed with various cement content (400, 450, and 500 Kg/m3), maximum aggregate size (12.5, 25 mm), steel fibre volume fractions (0, 0.33, 0.67, 1 %), and the constant water/cement ratio of 0.4 for all mixtures. Hence, influencing factors of steel fiber reinforced concrete characteristics and break-off test results are evaluated. The investigations show that the volume fraction of steel fibers and its features significantly affect the results of break-off test. Furthermore, in this study conventional numerical neural networks are developed for predicting the compressive strength of concrete with various mixtures and ages. ANN is sophisticatedly capable of being trained from the existent data and extending their behavior on a new dataset. This ability introduced ANN as an apt tool for modeling the complex mechanisms and systems in engineering applications. Statistical indices are used to compare the efficiency and accuracy of models. The result of this study has confirmed the accuracy of artificial neural network models in determination of the compressive strength of concrete.


Main Subjects

[1] Bungey, J. H. and Millard S. G. and Grantham M. G. (2006). Testing of concrete in structures, 4th ed. Taylor & Francis
Groups, London and New York, 352.
[2] Long A. and Murray A. (1984). The pull-off partially destructive test for concrete. In: Malhotra VM, editor. Proc. Int.
Conf. on In-SiturNon-Destructive Testing of Concrete. Ottawa, Canada, October. ACI SP-82, :327-350.
[3] Neville A. M. (2011). Properties of concrete, 4th ed. London: Pitman Publishing, 687, 331.
[4] Johansen R. (1976). A new method for determination of in-place concrete strength of form removal. 1st Eur. Colloq. on
Construction Quality Control. Madrid, Spain, 12 pages.
[5] Dahl-Jorgensen E. and Johansen R. (1984). General and specialized use of the break-off concrete strength testing
method. Special Publication. 82, 293-308.
[6] Johansen R. (1979). In Situ strength Evaluation of Concrete the Break-off Method. Concrete International. Vol. 1, No.9,
[7] Byfors, J. (1980). Plain concrete at early ages. Swedish Cement and Concrete Research Institute, Report No. Facks-
10044, Stockholm Sweden, 19 pages.
[8] Dahl-Jorgensen E. (1982). Break-Off and Pull-Off Methods for Testing Epoxy-Concrete Bonding Strength. Project No.
160382, The Foundation of Scientific and Industrial Research of the Norwegian Institute of Technology, Trondheim,
[9] Carlsson M. and Eeg I. R. and Jahren P. (1984). Field experience in the use of the “break-off tester”. Special Publication,
82. 277-292.
[10]Barker, M. G. and Ramirez, J. A. (1988). Determination of concrete strengths with break-off tester. Materials Journal,
85(4), 221-228.
[11] Naik, T. and Salameh Z. and Hassaballah A. (1988). Evaluation of In-Place Strength of Concrete By The Break-Off
Method. Proceedings of the NDT&E for Manufacturing and Construction Conference, University of Illinois, Urbana-
Champaign, IL.
[12] Lin Y. And Lin Y.F. and Hsiao C. (2010). Evaluation of bond quality at the interface between steel bar and concrete
using the small-dimension break-off test. Materials and Structures. 1;43(5): 583-595.
[13] Li V. C. (2002). Large volume, high-performance applications of fibres in civil engineering. Journal of Applied
Polymer Science.;83(3): 660-686.
[14] Aydin A. (2007). Self compactability of high volume hybrid fibre reinforced concrete. Construction and Building
Materials. 21(6): 1149-1154.
[15] Xu Z. and Hao H. and Li H. N. (2012). Mesoscale modelling of fibre reinforced concrete material under compressive
impact loading. Construction and Building Materials. 26(1): 274-288.
[16]Khalaj G. and Nazari A. (2012). Modeling split tensile strength of high strength self compacting concrete incorporating
randomly oriented steel fibres and SiO2 nanoparticles. Composites Part B: Engineering. 43(4): 1887-1892.
[17] Luccioni B. and Ruano G. and Isla F. and Zerbino R. and Giaccio G. (2012). A simple approach to model SFRC. Construction and Building Materials. 37: 111-124.
[18] Xu Z. and Hao H. and Li H.N. (2012). Mesoscale modelling of dynamic tensile behaviour of fibre reinforced concrete with spiral fibres. Cement and Concrete Research. 42(11): 1475-1493.
[19] ASTM C 150, (2004). Standard Specification for Portland Cement, American Standards for Testing and Materials.
[20] BS 882:(1992), Specification for aggregates from natural sources for concrete, London: BSI..
[21] EN, BS. 12390-3. (2009). Testing hardened concrete. Compressive strength of test specimens 19.
[22] ASTM C 1150 (1992) Standard test method for the break-off number of concrete, vol 04.02. Annual Book of ASTM Standards.
[23] Yiching, L. and Yu-Feng, L. and Chiamen, H. (2010). Evaluation of bond quality at the interface between steel bar and concrete using the small-dimension break-off test, Materials and Structures 43: 583–595.
[24] Adhikary, B. and Mutsuyoshi, H. (2006). Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater, 801–811.
[25] Mukherjee, A. and Biswas, S. (1997). Artificial neural networks prediction of mechanical behavior of concrete at high temperature. Nucl Eng Design, 1-11.
[26] Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks, Eng Fract Mech, 2143–59.
[27] Nikbin, I. M. and Beygi, M. H. A. and Kazemi, M. T. and Vaseghi Amiri, J. and E Rahmani, and Rabbanifar,S. and Eslami, M. (2014). A comprehensive investigation into the effect of aging and coarse aggregate size and volume on mechanical properties of self-compacting concrete. Materials & Design 59: 199-210.
[28] Xuan Hong, V. and Daudeville, L. and Malecot, Y. (2011). Effect of coarse aggregate size and cement paste volume on concrete behaviour under high triaxial compression loading. Construction and Building Materials 25.10: 3941-3949.
[29] El-Dieb, A. and Reda Taha, S. (2012). Flow characteristics and acceptance criteria of fiber- reinforced self-compacted concrete (FR-SCC). Construction and Building Materials 27.1 585-596.
[30] Khayat, K. and Schutter, D. (2013). Mechanical Properties of Self-Compacting Concrete. State-or-the-art report of RILEM Technical Committee 228-MPS Vol. 14, springer.
[31] Madandoust, R. and Ranjbar, M. and Ghavidel, R. and Shahabi, F. (2015). Assessment of factors influencing mechanical properties of steel fiber reinforced self-compacting concrete. Materials & Design 83: 284-294.
[32] Aslani, F. and Nejadi, S. (2013). Self-compacting concrete incorporating steel and polypropylene fibers: Compressive and tensile strengths, moduli of elasticity and rupture, compressive stress–strain curve, and energy dissipated under compression. Composites Part B: Engineering 53: 121-133.
[33] Güneyisi, E. and Geso─člu, M. and Akoi, A. O. M. and Mermerda┼č, K. (2014). Combined effect of steel fiber and metakaolin incorporation on mechanical properties of concrete. Composites Part B: Engineering 56: 83-91.
[34] AL-Ameeri, A. (2013). The effect of steel fiber on some mechanical properties of self compacting concrete. American Journal of civil engineering 1.3: 102-110.
[35] Ghavidel, R. and Madandoust, R. and Ranjbar, M. (2015). Reliability of pull-of test for steel fiber reinforced self-compacting concrete. Measurement 73: 628-639.
[36] Martinie, L. and Roussel, N. (2011). Simple tools for fiber orientation prediction in industrial practice. Cement and Concrete research 41.10: 993-1000.
[37] Zerbino, R. and Tobes, J. M. and Bossio, M. E. and Giaccio, G. (2012). On the orientation of fibres in structural members fabricated with self compacting fibre reinforced concrete. Cement and Concrete Composites 34.2: 191-200.
[38] Madandoust, R. and Ghavidel, R. and Nariman-Zadeh, N. (2010). Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV. Computational Materials Science 49.3: 556-567.
[39] Madandoust, R. and Bungey, H. and Ghavidel, R. (2012). Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Computational Materials Science 51.1: 261-272.
[40] Hudson Beale, M. and HaganHagan, M. T. and Demuth, H. B. (2012). Neural Network Toolbox™ User’s Guide, MathWorks, Inc.