Prediction of concrete compressive strength using non-destructive test results |
Erdal, Hamit
(Institute of Social Sciences, Ataturk University)
Erdal, Mursel (Technology Faculty, Department of Civil Engineering, Gazi University) Simsek, Osman (Technology Faculty, Department of Civil Engineering, Gazi University) Erdal, Halil Ibrahim (Turkish Cooperation and Coordination Agency (TIKA)) |
1 | Portnoy, S. and Koenker, R. (1997), "The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators", Stat. Sci., 12(4), 279-300. DOI |
2 | Qasrawi, H.Y. (2000), "Concrete strength by combined nondestructive methods simply and reliable predicted", Cement Concrete Res., 30, 739-746. DOI |
3 | Rajasekaran, S. and Amalraj, R. (2002), "Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron", Comput. Struct., 80, 2495-2505. DOI |
4 | Rajasekaran, S. and Lavanya, S. (2007), "Hybridization of genetic algorithm with immune system for optimization problems in structural engineering", Struct. Multidisc. Optim., 34, 415-429. DOI |
5 | Rajasekaran, S., Suresh, D, and Pai, GAV. (2002), "Application of sequential learning neural networks to civil engineering modeling problems", Eng. Comput., 18, 138-147. DOI |
6 | Ramyar, K. and Kol, P. (1996), "Destructive and non-destructive test methods for estimating the strength of concrete", Cement Concrete World, 2, 46-54. |
7 | Saridemir, M., Topcu, I.B., Ozcan, F. and Severcan, M.H. (2009) "Prediction of longterm effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Constr. Build. Mater., 23(3), 1279-1286. DOI |
8 | Sobhani, J., Najimi, M., Pourkhorshidi, A.R. and Parhizkar, T. (2010), "Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models", Constr. Build. Mater., 24(5), 709-718. DOI |
9 | Altun, F., Kisi, O. and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Comput. Mater. Sci., 42(2), 259-265. DOI |
10 | Antonaci, P., Bruno, C.L.E., Gliozzi, A.S. and Scalerandi, M. (2010), "Monitoring evolution of compressive damage in concrete with linear and nonlinear ultrasonic methods", Cement Concrete Res., 40, 1106-1113. DOI |
11 | Arif, M., Ishihara, T. and Inooka, H. (2001), "Incorporation of experience in iterative learning controllers using locally weighted learning", Autom, 37, 881-888. DOI |
12 | ASTM C39 (2001), Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens, Annual Book of ASTM Standards, Philadelphia, USA. |
13 | Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonic., 49, 53-60. DOI |
14 | Solis-Carcano, R. and Moreno, EI. (2008), "Evaluation of concrete made with crushed limestone aggregate based on ultrasonic pulse velocity", Constr. Build. Mater., 22, 1225-1231. DOI |
15 | Subasi, S. (2009), "Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique", Sci. Res. Essay., 4(4), 289-297. |
16 | Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. DOI |
17 | Tufekci, P. (2014), "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning method", Int. J. Elect. Power Energy Syst., 60, 126-140. DOI |
18 | Turkan, Y.S., Aydogmus, H.Y. and Erdal, H. (2016), "The prediction of the wind speed at different heights by machine learning methods", IJOCTA, 6(2), 179-187. |
19 | Wang, W. and Xu, Z. (2004), "A heuristic training for support vector regression", Neurocomput., 61, 259-275. DOI |
20 | Wang, Y. and Witten, I. (1997), "Inducing model trees for continuous classes", Ninth European Conference on Machine Learning, Prague, Czech Republic. |
21 | Windsor Probe Test System Inc. (1994), WPS 500 Windsor Probe Test System Operating Instructions. |
22 | Witten, I.H. and Frank, E. (2005), Data Mining: Practical Machine Learning Tools And Technique, Morgan Kaufmann Publishers. |
23 | Aydogmus, H.Y., Erdal, H.I., Karakurt, O., Namli, E., Turkan, Y.S. and Erdal, H. (2015), "A comparative assessment of bagging ensemble models for modeling concrete slump flow", Comput. Concrete, 16(5), 741-757. DOI |
24 | ASTM C597 (1998), Standard Test Method for Pulse Velocity Through Concrete, Annual Book of ASTM Standards, Philadelphia, USA. |
25 | ASTM C803 (1999), Standard Test Method for Penetration Resistance of Hardened Concrete, Annual Book of ASTM Standards, Philadelphia, USA. |
26 | Yaprakli, T.S. and Erdal, H. (2015), "Bankacilik sektorunde pazarlama karmasi elemanlarinin onceliklerinin belirlenmesi: Erzurum ili ornegi", JASSS, 38, 481-500. |
27 | ASTM C805 (1997), Standard Test Method for Rebound Number of Hardened Concrete, Annual Book of ASTM Standards, Philadelphia, USA. |
28 | Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38, 9609-9618. DOI |
29 | Aydin, F. and Saribiyik, M. (2010), "Correlation between Schmidt hammer and destructive compressions testing for concretes in existing buildings", Sci. Res. Essay., 5, 1644-1648. |
30 | Aydogmus, H.Y., Ekinci, A., Erdal, H.I. and Erdal, H. (2015), "Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models", J. Econ. Int. Finance, 7(5), 127-136. DOI |
31 | Baykan, U.N., Erdal, M. and Ugur, L.O. (2017), "A fuzzy logic model for prediction of compressive strength of concrete by use of non-destructive test results", Rev. Rom. Mater., 47(1), 54-59. |
32 | Betrie, G.D., Tesfamariam, S., Morin, K.A. and Sadiq, R. (2013), "Predicting copper concentrations in acid mine drainage: A comparative analysis of five machine learning techniques", Environ. Monit. Assess., 185(5), 4171-4182. DOI |
33 | ASTM C42 (1999), Standard Test Method for Obtaining and Testing Drilled Cores and Sawed Beams of Concrete, Annual Book of ASTM Standards, Philadelphia, USA. |
34 | Zarandi, M.F., Turksen, I.B., Sobhani, J. and Ramezanianpour, A.A. (2008), "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete", Appl. Soft. Comput., 8(1), 488-498. DOI |
35 | Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28, 1797-1808. DOI |
36 | Yeh, I.C. (2007), "Modeling slump flow of concrete using secondorder regressions and artificial neural networks", Cement Concrete Compos., 29, 474-480. DOI |
37 | Yeh, I.C. and Lien, L.C. (2009), "Knowledge discovery of concrete material using genetic operation trees", Exp. Syst. Appl., 36(3), 5807-5812. DOI |
38 | Cheng, M.Y., Chou, J.S., Roy, A.F.V. and Wu, Y.W. (2012), "Highperformance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model", Automat. Constr., 28, 106-115. DOI |
39 | Breysse, D. (2012), "Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods", Constr. Build. Mater., 33, 139-163. DOI |
40 | Cheng, M.Y. and Wu, Y.W. (2009), "Evolutionary support vector machine inference system for construction management", Automat. Constr., 18, 597-604. DOI |
41 | Cleary, J.G. and Trigg, L.E. (1995), "K*: an instance-based learner using an entropic distance measure", Proceedings of the 12th International Conference on Machine Learning, 108-114. |
42 | Csepe, Z., Makra, L., Voukantsis, D., Matyasovszky, I., Tusnady, G., Karatzas, K. and Thibaudon, M. (2014), "Predicting daily ragweed pollen concentrations using computational intelligence techniques over two heavily polluted areas in Europe", Sci. Total Environ., 476, 542-52. |
43 | Demirdogen, O., Erdal, H. and Akbaba, A.I. (2017), "Comparing various machine learning methods for prediction of patient revisit intention: A case study", SUJEST, 5(4), 386-401. |
44 | Dias, W. and Pooliyadda, S. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15(7), 371-379. DOI |
45 | Domone, P. and Soutsos, M. (1994), "An approach to the proportioning of high-strength concrete mixes", Concrete Int., 16, 26-31. |
46 | Ekinci, S., Celebi, U.B., Bala, M., Amasyali, M.F. and Boyaci, U.K. (2011), "Predictions of oil/chemical tanker main design parameters using computational intelligence techniques", Appl. Soft. Comput., 11, 2356-2366. DOI |
47 | Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 25-32. |
48 | Erdal, H.I. (2013), "Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction", Eng. Appl. Artif. Intell., 26(7), 1689-1697. DOI |
49 | Erdal, H. (2015), "Makine ogrenmesi yontemlerinin insaat sektorune katkisi: Basinc dayanimi tahminlemesi", Pamukkale Univ Muh Bilim Derg, 21(3), 109-114. |
50 | Erdal, H. and Karahanoglu, I. (2016), "Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks", Appl. Soft. Comput., 49, 861-867. DOI |
51 | Erdal, H.I. and Karakurt, O. (2013), "Advancing monthly stream flow prediction accuracy of CART models using ensemble learning paradigms", J. Hydrol., 477, 119-128. DOI |
52 | Erdal, H.I., Karakurt, O. and Namli, E. (2013), "High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform", Eng. Appl. Artif. Intell., 26(4), 1246-1254. DOI |
53 | Erdal, M. (2002), "Determination of compressive strength of concrete by some non-destructive test methods", M.Sc. Thesis, Gazi University, Ankara. |
54 | Gupta, R., Kewalramani, M.A. and Goel, A. (2006), "Prediction of concrete strength using neural-expert system", J. Mater. Civil Eng., 18(3), 462-466. DOI |
55 | Erdal, M. (2009), "Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression techniques", Sci. Res. Essay., 4(10), 1057-1065. |
56 | Erdal, M. and Simsek, O. (2006), "Investigation of the performance of some non-destructive tests on the determination of compressive strength of vacuum-processed concrete", J. Fac. Eng. Arch. Gazi Univ., 21(1), 65-73. |
57 | Galan, A. (1967), "Estimate of concrete strength by ultrasonic pulse velocity and damping constant", ACI J. Proc., 64(10), 678-684. |
58 | Iba W.L. (1992), "Induction of one-level decision trees", Ninth International Conference on Machine Learning, Aberdeen. |
59 | Haykin, S. (1999), Neural Networks, A Comprehensive Foundation, 2nd Edition, Prentice Hall. |
60 | Hola, J. and Schabowicz, K. (2005), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11, 23-32. |
61 | Jiawei, H. and Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann. |
62 | 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 |
63 | Mielentz, F. (2008), "Phased arrays for ultrasonic investigations in concrete components", J. Nondestruct. Eval., 27, 23-33. DOI |
64 | Kheder, G.F. (1998), "A two stage procedure for assessment of in-situ concrete strength using combined non-destructive testing", Mater. Struct., 32, 410-417. |
65 | Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. DOI |
66 | Mehta, P.K. and Monterio, P.J.M. (2006), Concrete Structure, Properties and Materials, 3th Edition, Mc Graw-Hill Companies. |
67 | Mousavi, S.M., Gandomi, A.H., Alavi, A.H. and Vesalimahmood, M. (2010), "Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares", Struct. Eng. Mech., 36, 225-241. DOI |
68 | Namli, E., Erdal, H.I. and Erdal, H. (2016), "Dalgacik donusumu ile beton basinc dayanim tahmininin iyilestirilmesi", Politeknik Dergisi, 19(4), 471-480. |
69 | Neville, A.M. (1993), Properties of Concrete, 3th Edition, Longman Scientific & Technical. |
70 | Painuli, S., Elangovan, M. and Sugumaran, V. (2014), "Tool condition monitoring using K-star algorithm", Exp. Syst. Appl., 41, 2638-2643. DOI |