Browse > Article
http://dx.doi.org/10.12989/cac.2015.15.1.089

Analyzing the compressive strength of clinker mortars using approximate reasoning approaches - ANN vs MLR  

Beycioglu, Ahmet (Civil Engineering Department, Technology Faculty, Duzce University)
Emiroglu, Mehmet (Civil Engineering Department, Technology Faculty, Duzce University)
Kocak, Yilmaz (Department of Construction, Vocational School of Technical Sciences, Dumlupinar University)
Subasi, Serkan (Civil Engineering Department, Technology Faculty, Duzce University)
Publication Information
Computers and Concrete / v.15, no.1, 2015 , pp. 89-101 More about this Journal
Abstract
In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.
Keywords
clinker; prediction; compressive strength; artificial neural networks; multi linear regression;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Alexandridis, A. Chondrodima, E. and Sarimveis, H. (2013), "Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization", IEEE T. Neur. Networ., 24(2), 219-230.
2 Alexandridis, A. Triantis, D. Stavrakas, I. and Stergiopoulos, C. (2012), "A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals", Construct. Build. Mater., 30(0), 294-300.   DOI
3 Amani, J. and Moeini, R. (2012), "Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network", Scientia Iranica, 19(2), 242-248.   DOI   ScienceOn
4 Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst.Appl., 38(8), 9609-9618.   DOI   ScienceOn
5 Bagheri, M., Mirbagheri, S.A., Ehteshami, M. and Bagheri, Z. (2014), "Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks", Process Safety and Environmental Protection (In Press).
6 Bal, L. and Buyle-Bodin, F. (2013), "Artificial neural network for predicting drying shrinkage of concrete", Construct. Build. Mater., 38(0), 248-254.   DOI   ScienceOn
7 Basyigit, C., Akkurt, I., Kilincarslan, S. and Beycioglu, A. (2010), "Prediction of compressive strength of heavyweight concrete by ANN and FL models", Neural Comput., 19(4), 507-513.   DOI
8 Benardos, P.G. and Vosniakos, G.C. (2007), "Optimizing feedforward artificial neural network architecture", Eng. Appl. Artif. Intel., 20(3), 365-382.   DOI
9 Bilgehan, M. (2011), "A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches", Nondestructive Testing Evaluation, 26(1), 35-55.   DOI
10 Bilhan, O., Emiroglu, M.E. and Kisi, O. (2011), "Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels", Adv. Eng. Softw., 42(4), 208-214.   DOI
11 Bilim C. (2011), "Properties of cement mortars containing clinoptilolite as a supplementary cementitious material", Construct. Build. Mater., 25(8), 3175-3180.   DOI
12 Boga, A.R., O zturk, M. and Topcu, I.B. (2013), "Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI", Compos. Part B: Eng., 45(1), 688-696.   DOI
13 Duan, Z.H., Kou, S.C. and Poon, C.S. (2013), "Prediction of compressive strength of recycled aggregate concrete using artificial neural networks", Construct.Build. Mater., 40(0), 1200-1206.   DOI   ScienceOn
14 Brant, R. (2007) Lecture notes http://stat.ubc.ca/rollin/teach/BiostatW07/reading/MLR.pdf
15 Celik, I.B., Oner, M. and Can, N.M. (2007), "The influence of grinding technique on the liberation of clinker minerals and cement properties", Cement Concrete Res., 37(9), 1334-1340.   DOI
16 Dantas, ATA., Batista Leite, M. and de Jesus Nagahama, K. (2013), "Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks", Construct.Build. Mater., 38(0), 717-722.   DOI
17 Dunstetter, F., de Noirfontaine, M.N. and Courtial, M. (2006), ""Polymorphism of tricalcium silicate, the major compound of Portland cement clinker: 1. Structural data: review and unified analysis", Cement Concrete Res., 36(1), 39-53.   DOI
18 Erdem, H. (2010), "Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks", Adv. Eng. Softw., 41(2), 270-276.   DOI   ScienceOn
19 Gencel, O., Kocabas, F., Gok, M.S. and Koksal, F. (2011), "Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete", Construct. Build. Mater., 25(8), 3486-3494.   DOI
20 Hou, T-H., Su, C-H. and Chang, H-Z. (2008), "Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process", Exp. Syst. Appl., 34(1), 427-436.   DOI
21 Khan, M.I. (2012), "Predicting properties of high performance concrete containing composite cementitious materials using artificial neural networks", Auto. Construct., 22(0), 516-524.   DOI
22 Kalayci, S. (2006), Multi varied statistical techniques and SPSS applications, Asil Publishing, Ankara (In Turkish).
23 Karakurt, C. and Topcu, I.B. (2011), "Effect of blended cements produced with natural zeolite and industrial by-products on alkali-silica reaction and sulfate resistance of concrete", Construct. Build. Mater., 25(4), 1789-1795.   DOI
24 Khan, M.I. (2012), "Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks", Construct. Build. Mater., 28(1), 14-20.   DOI
25 Khayet, M. and Cojocaru, C. (2012), "Artificial neural network modeling and optimization of desalination by air gap membrane distillation", Sep. Purif. Technol., 86(0), 171-182.   DOI
26 Krishnamoorthy, C.S. and Rajeev, S. (1996), Artificial Intelligence and Expert Systems for Engineers, Taylor and Francis.
27 Kyriazopoulos, A., Anastasiadis, C., Triantis, D. and Brown, C.J. (2011), "Non-destructive evaluation of cement-based materials from pressure-stimulated electrical emission-Preliminary results", Construct. Build. Mater., 25(4), 1980-1990.   DOI
28 Liang, X.B. and Wang, J. (2000), "A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints", IEEE T. Neur. Networ., 11(6), 1251-1262.   DOI
29 Lim, S.P. and Haron, H. (2013), "Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data", IEEE T. Neur. Networ., 24(9), 1414-1424.
30 Madandoust, R., Bungey, J.H. and Ghavidel, R. (2012), "Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models", Comput. Mat. Sci., 51(1), 261-272.   DOI
31 Mashrei, M.A., Seracino, R. and Rahman, M.S. (2013), "Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints", Construct. Build. Mater., 40(0), 812-821.   DOI
32 Mohanraj, M., Jayaraj, S. and Muraleedharan, C. (2012), "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems-A review", Renew. Sust. Energ. Rev., 16(2), 1340-1358.   DOI
33 Molero, M., Segura, I., Izquierdo, M.A., Fuente, J.V. and Anaya, J.J. (2009), "Sand/cement ratio evaluation on mortar using neural networks and ultrasonic transmission inspection", Ultrasonics, 49(2), 231-237.   DOI
34 Nazari, A. and Riahi, S. (2011), "Prediction split tensile strength and water permeability of high strength concrete containing $TiO_2$ nanoparticles by artificial neural network and genetic programming", Compos.Part B: Eng., 42(3), 473-488.   DOI
35 Neville, A.M. (2006), Properties of concrete: Pearson Education limited, England.
36 Onal, O. and Ozturk, A.U. (2010), "Artificial neural network application on microstructure-compressive strength relationship of cement mortar", Adv. Eng. Softw., 41(2), 165-169.   DOI   ScienceOn
37 Ozturk, A.U. and Turan, M.E. (2012), "Prediction of effects of microstructural phases using generalized regression neural network", Construct. Build. Mater., 29(0), 279-283.   DOI
38 Sahoo, A.K., Zuo, M.J. and Tiwari, M.K. (2012), "A data clustering algorithm for stratified data partitioning in artificial neural network", Exp. Syst. Appl., 39(8), 7004-7014.   DOI
39 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.   DOI
40 Pinter, J.D. (2012), "Calibrating artificial neural networks by global optimization", Exp. Syst.Appl., 39(1), 25-32.   DOI
41 Saridemir, M. (2009), "Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic", Adv. Eng. Softw., 40(9), 920-927.   DOI
42 Saridemir, M., Topcu I.B., Ozcan, F. and Severcan, M.H. (2009), "Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Construct. Buildi. Mater., 23(3), 1279-1286.   DOI
43 Schneider, M., Romer, M., Tschudin, M. and Bolio, H. (2011), "Sustainable cement production-present and future", Cement Concrete Res., 41(7), 642-650.   DOI
44 Shah A.A., Alsayed S.H., Abbas, H. and Al-Salloum, Y.A. (2012), "Predicting residual strength of nonlinear ultrasonically evaluated damaged concrete using artificial neural network", Construct. Build. Mater., 29(0), 42-50.   DOI   ScienceOn
45 Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng.Softw., 42(10), 780-786.   DOI   ScienceOn
46 Topcu, I.B. and Saridemir, M. (2008), "Prediction of rubberized mortar properties using artificial neural network and fuzzy logic", J.Mater. Process. Technol., 199(1-3), 108-118.   DOI
47 Slonski, M. (2010), "A comparison of model selection methods for compressive strength prediction of highperformance concrete using neural networks", Comput. Struct., 88(21-22), 1248-1253.   DOI   ScienceOn
48 Subasi, S. (2009), "Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique", Scientific Research Essays, 4(4) 289:297.
49 Terzi, S. (2007), "Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks", Construct. Build. Mater., 21(3), 590-593.   DOI
50 Triantis, D., Stavrakas, I., Kyriazopoulos, A., Hloupis, G. and Agioutantis, Z. (2012), "Pressure stimulated electrical emissions from cement mortar used as failure predictors", Int. J. Fracture, 175(1), 53-61.   DOI
51 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", Construct. Build.Mater., 27(1), 404-414.   DOI