[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/cac.2010.7.3.271

The use of neural networks in concrete compressive strength estimation

Bilgehan, M. (Harran University, Engineering Faculty, Civil Engineering Department)
Turgut, P. (Harran University, Engineering Faculty, Civil Engineering Department)

Publication Information

Computers and Concrete / v.7, no.3, 2010 , pp. 271-283 More about this Journal

Abstract

Testing of ultrasonic pulse velocity (UPV) is one of the most popular and actual non-destructive techniques used in the estimation of the concrete properties in structures. In this paper, artificial neural network (ANN) approach has been proposed for the evaluation of relationship between concrete compressive strength, UPV, and density values by using the experimental data obtained from many cores taken from different reinforced concrete structures with different ages and unknown ratios of concrete mixtures. The presented approach enables to find practically concrete strengths in the reinforced concrete structures, whose records of concrete mixture ratios are not yet available. Thus, researchers can easily evaluate the compressive strength of concrete specimens by using UPV values. The method can be used in conditions including too many numbers of the structures and examinations to be done in restricted time duration. This method also contributes to a remarkable reduction of the computational time without any significant loss of accuracy. Statistic measures are used to evaluate the performance of the models. The comparison of the results clearly shows that the ANN approach can be used effectively to predict the compressive strength of concrete by using UPV and density data. In addition, the model architecture can be used as a non-destructive procedure for health monitoring of structural elements.

Keywords

concrete; density; compressive strength; ultrasonic pulse velocity; non-destructive testing; artificial neural networks;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)
Times Cited By SCOPUS : 4

1	ACI 318-95 (1995), Building code requirements for structural concrete, (ACI 318-95) and commentary-ACI 318R-95, ACI, USA.
2	Ashour, A.F. and Alqedra, M.A. (2005), "Concrete breakout strength of single anchors in tension using neural networks", Adv. Eng. Softw., 36, 87-97. DOI ScienceOn
3	ASTM C 42-90 (1992), Standard test method for obtaining and testing drilled cores and sawed beams of concrete, ASTM, USA.
4	ASTM C 597-83 (1991), Test for pulse velocity through concrete, ASTM, USA.
5	Bilgehan, M. and Turgut, P. (2010), "Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity", Res. Nondestruct. Eval., 21, 1-17. DOI ScienceOn
6	BS 1881: Part 120 (1983), Method for determination of compressive strength of concrete cores, BSI, UK.
7	BS 1881-203 (1986), Recommendations for measurement of velocity of ultrasonic pulses in concrete, BSI, UK.
8	Bungey, J.H. and Millard S.G. (2004), Testing of concretes in structures, Taylor&Francis e-Library.
9	Bungey, J.H. and Soutsos, M.N. (2001), "Reliability of partially-destructive tests to assess the strength of concrete on site", Constr. Build. Mater., 15, 81-92. DOI ScienceOn
10	Castro, P.F. (2000), "Assessing concrete strength by Insitu Tests", 15th World Conference on Non Destructive Testing, Rome, Italy.
11	Davis, S.G. (1977), "The effect of variations in the aggregate content of concrete columns upon the estimation of strength by the pulse-velocity method", Mag. Concrete Res., 29(98), 7-12. DOI ScienceOn
12	Del Rio, L.M., Jimenez, A., Lopez, F., Rosa, F.J., Rufo, M.M. and Paniagua, J.M. (2004), "Characterization and hardening of concrete with ultrasonic testing", Ultrasonics, 42, 527-530. DOI ScienceOn
13	Mehta, P.K. and Monteiro, P.J.M. (2006), Concrete microstructure, properties, and materials, McGraw-Hill, New York.
14	Neville, A.M. (1995), Properties of concrete, Addison-Wesley Longman, Essex, UK.
15	Nilsen, A. and Aitcin, P. (1992), "Static modulus of elasticity of high strength concrete from pulse velocity tests", Cement Concrete Aggr., 14(1), 64-66. DOI
16	Ohdaira, E. and Masuzawa, N. (2000), "Water content and its effect on ultrasound propagation in concrete-the possibility of NDE", Ultrasonics, 38, 546-552. DOI ScienceOn
17	Philleo, R. (1995), "Comparison of results of three methods for determining Young's modulus of elasticity of concrete", J. Am. Concr. Inst., 51, 461-469.
18	Popovics, S. (1998), Strength and related properties of concrete: a quantitative approach, New York, John Wiley Sons Inc.
19	Popovics, S. (2001), "Analysis of the concrete strength versus ultrasonic pulse velocity relationship", Mater. Eval., 59(2), 123-130.
20	Popovics, S. and Popovics, J.S. (1992), "A critique of the ultrasonic pulse velocity method for testing concrete", In: Ansari, F. and Sture, S. (Eds.), Nondestructive Testing of Concrete Elements and Structures, Symp. on Nondestructive Testing of Concrete Elements and Structures/Structures Congress, Engineering Division of ASCE, April 13-15, San Antonio, Texas, 94-103.
21	Rafiq, M.Y., Bugmann, G. and Easterbrook D.J. (2001), "Neural network design for engineering application", Comput. Struct., 79, 1541-1552. DOI ScienceOn
22	Rajagopalan, P.R., Prakash, J. and Naramimhan, V. (1973), "Correlation between ultrasonic pulse velocity and strength of concrete", Indian Concrete J., 47(11), 416-418.
23	Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Learning internal representation by error propagation", Inf: Parallel Distributed Processing, ed. by Rumelhart, D.E. and McClelland, J.L., vol. 1: Foundations, MIT Press, Cambridge, Massachusetts, USA.
24	Sharma, M. and Gupta, B. (1960), "Sonic modulus as related to strength and static modulus of high strength concrete", Indian Concrete. J., 34(4), 139-141.
25	Hola, J. and Schabowicz, K. (2005a), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11(1), 23-32.
26	Ferreira, A.P. and Castro, P.F. (2000), "NDT for assessing concrete strength", Non Destructive Testing In Civil Engineering 2000-Seiken Symposium No. 26, April, 25-27, Tokyo, Japan.
27	Flood, I. and Kartam, N. (1994a), "Neural network in civil engineering I: principles and understandings", J. Comput. Civil Eng. - ASCE, 8(2), 131-148. DOI ScienceOn
28	Flood, I. and Kartam, N. (1994b), "Neural network in civil engineering II: systems and applications", J. Comput. Civil Eng. - ASCE, 8(2), 149-162. DOI ScienceOn
29	Hola, J. and Schabowicz, K. (2005b), "New technique of nondestructive assessment of concrete strength using artificial intelligence", NDT&E Inter., 38(4), 251-259. DOI ScienceOn
30	Kartam, N., Flood, I. and Garrett, J.H. (1997), Artificial neural networks for civil engineers: fundamentals and applications, ASCE, New York.
31	Kewalramani, M.A. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Automat. Constr., 15, 374-379. DOI ScienceOn
32	Kisi, O. (2005), "Suspended sediment estimation using neuro-fuzzy and neural network approaches", Hydrolog. Sci. J., 50(4), 683-695.
33	Leshchinsky A.M. (1991), "Non-destructive methods instead of specimens and cores", In: Quality Control of Concrete Structures, Proceedings of the International Symposium Held by RILEM, CEB, RUG, BBG/GBB and NFWO/FNRS and organized by the Magnel Laboratory for Reinforced Concrete, State University of Ghent, ed. by L. Taerwe and H. Lambotte, Belgium, June 12-14, 377-386.
34	Lippman, R. (1987), "An introduction to computing with neural nets", IEEE ASSP Mag., 4, 4-22. DOI
35	Malhotra, V.M. (1976), Testing hardened concrete: non-destructive methods, ACI, Monograph no. 9, Detroit, USA.
36	MathWorks Inc. (1999), MatLab the Language of Technical Computing, Version 6, Natick, M.A., USA.
37	Todd, C.P.D. and Challis, R.E. (1999), "Quantitative classification of adhesive bondlines using Lamb waves and artificial neural networks", IEEE Trans. UFFC, 46(1), 167-181. DOI
38	Tapkn, S., Cevik, A. and Uar, U. (2010), "Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks", Expert Systems with Applications, doi:10.1016/j.eswa. 2009. 12.042.
39	Tapkin, S., Tuncan, M., Arioz, O., Tuncan, A. and Ramyar, K. (2006), "Estimation of concrete compressive strength by using ultrasonic pulse velocities and artificial neural networks", Conference for Computer Aided Engineering and System Modeling, 11th FIGES User's Conference, Turkey.
40	Tharmaratnam, K. and Tan, B.S. (1990), "Attenuation of ultrasonic pulse in cement mortar", Cement Concrete Res., 20, 335-345. DOI ScienceOn
41	Trtnik, G., Franci, K. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonics, 49, 53-60. DOI ScienceOn
42	Turgut, P. (2004), "Research into the correlation between concrete strength and UPV values", J. Nondestruct. Test., 12(12).
43	Whitehurst, E. (1951), "Soniscope tests concrete structures", J. Am. Concrete Inst., 47, 433-444.
44	Yaman, I.O., Akbay, Z. and Aktan, H. (2006), "Numerical modelling and finite element analysis of stress wave propagation for ultrasonic pulse velocity testing of concrete", Comput. Concrete, 3(6), 423-437. DOI

	Lyes Bal. (2013) Construction and Building Materials Artificial neural network for predicting drying shrinkage of concrete / 38 , 248
4	Mahmut Bilgehan. (2011) Applied Soft Computing Comparison of ANFIS and NN models—With a study in critical buckling load estimation / 11 (4) , 3779
2	Jose R. Marti-Vargas. (2013) Computers and Concrete Prediction of the transfer length of prestressing strands with neural networks / 12 (2) , 187
	Tanja Kalman Šipoš. (2017) Construction and Building Materials Model for mix design of brick aggregate concrete based on neural network modelling / 148 , 757
6	S. M. A. Boukli Hacene. (2014) Arabian Journal for Science and Engineering Probabilistic Modelling of Compressive Strength of Concrete Using Response Surface Methodology and Neural Networks / 39 (6) , 4451
8	Osman Gencel. (2011) Construction and Building Materials Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete / 25 (8) , 3486
6	V.G. Papadakis. (2013) Computers and Concrete Predictive modeling of concrete compressive strength based on cement strength class / 11 (6) , 587
4	Ali Demir. (2015) Computers and Concrete Prediction of Hybrid fibre-added concrete strength using artificial neural networks / 15 (4) , 503
4	A. A. Shah. (2011) Latin American Journal of Solids and Structures Estimation of RC slab-column joints effective strength using neural networks / 8 (4) , 393
6	Nain Y. Sheen. (2013) Computers and Concrete Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network / 12 (6) , 785
2	Yousef A. Al-Salloum. (2012) Computers & concrete Prediction of compressive strength of concrete using neural networks / 10 (2) , 197
	Emilia Vasanelli. (2017) Construction and Building Materials Combining non-invasive techniques for reliable prediction of soft stone strength in historic masonries / 146 , 744
6	Lyes Bal. (2014) Neural Computing and Applications Artificial neural network for predicting creep of concrete / 25 (6) , 1359
3	Mohammad Mohammadhassani. (2013) Computers & concrete Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams / 11 (3) , 237
3-4	Osman Gencel. (2014) Neural Computing and Applications A comparative modeling study to estimate wear of concrete / 24 (3-4) , 649
	Behnam Vakhshouri. (2018) Neurocomputing Prediction of compressive strength of self-compacting concrete by ANFIS models / 280 , 13
4	Mahmut Bilgehan. (2012) Journal of Civil Engineering and Management Buckling load estimation of cracked columns using artificial neural network modeling technique / 18 (4) , 568
4	Mahmut Bilgehan. (2016) Neural Computing and Applications ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire / 27 (4) , 869
1	P. K. Singh. (2017) Engineering with Computers Indirect estimation of compressive and shear strength from simple index tests / 33 (1) , 1
4	O. Gencel. (2013) Experimental Heat Transfer Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches / 26 (4) , 360
6	Hsing-Chih Tsai. (2013) Computers and Concrete Polynomial modeling of confined compressive strength and strain of circular concrete columns / 11 (6) , 603
5	Zhen-Hua Duan. (2014) Computers and Concrete Factors affecting the properties of recycled concrete by using neural networks / 14 (5) , 547
1	(2010) Advances in civil engineering materials An Artificial Intelligence Model for Computing Optimum Fly Ash Content for Structural-Grade Concrete / 8 (1) , 20180079
1687-8094	(2018) Advances in Civil Engineering Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges / 2018 (1687-8094) , 1
3	(2018) Applied Sciences Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network / 8 (3) , 405
2116-7214	(2021) European Journal of Environmental and Civil Engineering Influence of steel reinforcement on ultrasonic pulse velocity as a non-destructive evaluation of high-performance concrete strength / (2116-7214) , 1
3	(2018) Materials and Structures A graphical method for assessing the concrete strength class in existing RC structures / 51 (3)
1	(2017) Computers & concrete Artificial neural network model using ultrasonic test results to predict compressive stress in concrete / 19 (1) , 59
2	(2018) Computers & concrete Bond strength prediction of steel bars in low strength concrete by using ANN / 22 (2) , 249
4	(2010) Computers & concrete Concrete compressive strength prediction using the imperialist competitive algorithm / 22 (4) , 355
4	(2010) Computers & concrete Knowledge-based learning for modeling concrete compressive strength using genetic programming / 23 (4) , 255
4	(2010) Computers & concrete Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars / 24 (4) , 329
8	(2020) Smart materials & structures Actuating and sensing mechanism of embedded piezoelectric transducers in concrete / 29 (8) , 085020
1	(2010) Network : computation in neural systems Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey / 31 (1) , 1
1	(2010) Proceedings of the Institution of Civil Engineers, Municipal engineer Evaluation of concrete made with stone and brick aggregate using non-destructive testing / 174 (1) , 43
5	(2021) AIP advances CompoNet with SFEL: A convolutional neural network for identifying low-emissivity coating damage / 11 (5) , 055211
1	(2010) Computers & concrete Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites / 28 (1) , 55
8	(2021) AIP advances Emissivity measurement based on deep learning and surface roughness / 11 (8) , 085305
None	(2021) Construction & building materials Mechanical strength, mass loss and volumetric changes of drying adobe matrices combined with kaolin and fine soil particles / 312 (None) , 125246

1	Prediction of compressive strength of concrete using neural networks / [Al-Salloum, Yousef A.;Shah, Abid A.;Abbas, H.;Alsayed, Saleh H.;Almusallam, Tarek H.;Al-Haddad, M.S.;] / Computers and Concrete
2	Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams / [Mohammadhassani, Mohammad;Nezamabadi-pour, Hossein;Jumaat, Mohd Zamin;Jameel, Mohammed;Arumugam, Arul M.S.;] / Computers and Concrete
3	Polynomial modeling of confined compressive strength and strain of circular concrete columns / [Tsai, Hsing-Chih;] / Computers and Concrete
4	Predictive modeling of concrete compressive strength based on cement strength class / [Papadakis, V.G.;Demis, S.;] / Computers and Concrete
5	Prediction of the transfer length of prestressing strands with neural networks / [Marti-Vargas, Jose R.;Ferri, Francesc J.;Yepes, Victor;] / Computers and Concrete
6	Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network / [Sheen, Nain Y.;Huang, Jeng L.;Le, Hien D.;] / Computers and Concrete
7	Factors affecting the properties of recycled concrete by using neural networks / [Duan, Zhen-Hua;Poon, Chi-Sun;] / Computers and Concrete

1	/ Applied Soft Computing Journal
2	/ Construction and Building Materials
3	/ Latin American Journal of Solids and Structures
4	/ International Journal of Physical Sciences