Advanced SearchSearch Tips
Application of expert systems in prediction of flexural strength of cement mortars
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Computers and Concrete
  • Volume 18, Issue 1,  2016, pp.1-16
  • Publisher : Techno-Press
  • DOI : 10.12989/cac.2016.18.1.001
 Title & Authors
Application of expert systems in prediction of flexural strength of cement mortars
Gulbandilar, Eyyup; Kocak, Yilmaz;
In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of , RMS and MAPE. For the testing of dataset, the , RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the , RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.
ANN;ANFIS;blast furnace slag;waste tire rubber powder;flexural strength;
 Cited by
Aali, K.A., Parsinejad, M. and Rahmani, B. (2009), "Estimation of saturation percentage of soil using multiple regression, ANN, and ANFIS techniques", Comput. Inform. Sci., 2(3), 127-136.

Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build. Mater., 20(9), 801-811. crossref(new window)

Al-Akhras, N.M. and Smadi, M.M. (2004), "Properties of tire rubber ash mortar", Cement Concrete Compos., 26, 821-826. crossref(new window)

Atiş, C.D. and Bilim, C. (2007), "Wet and dry cured compressive strength of concrete containing ground granulated blast-furnace slag", Build. Envir., 42(8), 3060-3065. crossref(new window)

Behnood, A., Verian, K.P. and Gharehveran, M.M. (2015), "Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength", Constr. Build. Mater., 98, 519-529. crossref(new window)

Beycioglu, A., Emiroglu, M., Kocak, Y. and Subasi, S. (2015), "Analyzing the compressive strength of clinker mortars using approximate reasoning approaches-ANN vs MLR", Comput. Concrete, 15(1), 89-101. crossref(new window)

Crossin, E. (2015), "The greenhouse gas implications of using ground granulated blast furnace slag as a cement substitute", J.Clean. Product., 95, 101-108. crossref(new window)

Deb, P.S., Nath, P. and Sarker, P.K. (2014), "The effects of ground granulated blast-furnace slag blending with fly ash and activator content on the workability and strength properties of geopolymer concrete cured at ambient temperature", Mater. Des., 62, 32-39. crossref(new window)

Dellinghausen L.M., Gastaldini A.L.G., Vanzin F.J. and Veiga K.K. (2012), "Total shrinkage, oxygen permeability, and chloride ion penetration in concrete made with white Portland cement and blast-furnace slag", Constr. Build. Mater., 37, 652-659. crossref(new window)

Demir, F. (2008), "Prediction of elastic modulus of normal and high strength concrete by artificial neural network", Constr. Build. Mater., 22(7), 1428-1435. crossref(new window)

Eiras, J.N., Segovia, F., Borrachero, M.V., Monzo, J., Bonilla, M. and Paya, J. (2014), "Physical and mechanical properties of foamed Portland cement composite containing crumb rubber from worn tires", Mater. Des., 59, 550-557. crossref(new window)

Gulbandilar, E. and Kocak, Y. (2013), "Prediction the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic", Neur. Comput. Appl., 22, 1485-1491. crossref(new window)

Jang, J.S.R. (1996), "Input selection for ANFIS learning, Fuzzy Systems", Proceedings of the Fifth IEEE International Conference on, New Orleans, September.

Komleh, H.E and Maghsoudi, A.A. (2015), "Prediction of curvature ductility factor for FRP strengthened RHSC beams using ANFIS and regression models", Comput. Concrete, 16(3), 399-414. crossref(new window)

Mansouri, I. and Kisi, O. (2015), "Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches", Compos.: Part B, 70, 247-255. crossref(new window)

Motamedi, S., Shamshirband, S., Petkovic, D. and Hashim, R. (2015), "Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture", Powder Tech., 278, 278-285. crossref(new window)

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, 856-863. crossref(new window)

Parichatprecha, R. and Nimityongskul, P. (2009), "Analysis of durability of high performance concrete using artificial neural Networks", Constr. Build. Mater., 23, 910-917. crossref(new window)

Sakthivel, P.B., Ravichandran, A. and Alagumurthi, N. (2016), "Modeling and pediction of flexural strength of hybrid mesh and fiber reinforced cement-based composites using Artificial Neural Network (ANN)", Int. J. Geomate, 10(1), 1623-1635.

Siddiquea, R. and Bennacer, R. (2012), "Use of iron and steel industry by-product (GGBS) in cement paste and mortar. Resources", Conserv. Recy., 69, 29-34. crossref(new window)

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.

Teng, S., Lim, T.Y.D. and Divsholi, B.S. (2013), "Durability and mechanical properties of high strength concrete incorporating ultra fine ground granulated blast-furnace slag", Constr. Build. Mater., 40, 875-881. crossref(new window)

Topcu, İ.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41, 305-311. crossref(new window)

Topcu, İ.B., Karakurt, C. and Saridemir, M. (2008), "Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic", Mater. Des., 29(10), 1986-1991. crossref(new window)

Topcu, İ.B., Saridemir, M., 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", Constr. Build. Mater., 23, 1279-1286. crossref(new window)

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. crossref(new window)

TS EN 196-1 (2009), "Methods of testing cement-Part 1: Determination of strength", Turkish Standards, Ankara, Turkey.

TS EN 197-1 (2012), "Cement- Part 1: Compositions and conformity criteria for common cements", Turkish Standards, Ankara, Turkey.

Uygunoglu, T. and Topcu, I.B. (2010), "The role of scrap rubber particles on the drying shrinkage and mechanical properties of self-consolidating mortars", Constr. Build. Mater., 24(7), 1141-1150. crossref(new window)

Wang, B., Man, T. and Jin, H. (2015), "Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems", Constr. Build. Mater., 84, 184-191. crossref(new window)

Yaprak, H., Karaci, A. and Demir, I. (2013), "Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks", Neur. Comput. Appl., 22, 133-141.

Yilmaz, A. and Degirmenci, N. (2009), "Possibility of using waste tire rubber and fly ash with Portland cement as construction materials", Waste Manage., 29, 1541-1546. crossref(new window)

Yung, W.H., Yung, L.C. and Hua, L.H. (2013), "A study of the durability properties of waste tire rubber applied to self-compacting concrete", Constr. Build. Mater., 41, 665-672. crossref(new window)

Zhu, J., Zhong, Q., Chen, G. and Li, D. (2012), "Effect of particlesize of blast furnace slag on properties of portland cement", Procedia Eng., 27, 231-236.