Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water-cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450kg/m^3) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22+/-2^oC) were measured at 3, 7, 28, 90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.

[1]  T. Mingshu,et al.  Durability study of steel slag cement , 1997 .

[2]  C.M.L. Wu,et al.  Durability of concrete with high cement replacement , 2000 .

[3]  D. M. Roy,et al.  Effect of silica fume, metakaolin, and low-calcium fly ash on chemical resistance of concrete , 2001 .

[4]  Steve Millard,et al.  Strength development of mortars containing ground granulated blast-furnace slag: Effect of curing temperature and determination of apparent activation energies , 2006 .

[5]  S. Akyuz,et al.  An experimental study on optimum usage of GGBS for the compressive strength of concrete , 2007 .

[6]  Fevziye Aköz,et al.  Effect of curing conditions on the mortars with and without GGBFS , 2008 .

[7]  J. J. Brooks,et al.  Effect of silica fume on mechanical properties of high-strength concrete , 2004 .

[8]  P. K. Mehta,et al.  Greening of the Concrete Industry for Sustainable Development , 2002 .

[9]  Dale P. Bentz,et al.  Influence of Water-to-Cement Ratio on Hydration Kinetics: Simple Models Based on Spatial Considerations , 2006 .

[10]  J. Escalante,et al.  Reactivity of blast-furnace slag in Portland cement blends hydrated under different conditions , 2001 .

[11]  İsa Yüksel,et al.  Durability of concrete incorporating non-ground blast furnace slag and bottom ash as fine aggregate , 2007 .

[12]  Erick Ringot,et al.  Mineral Admixtures in Mortars Effect of Type, Amount and Fineness of Fine Constituents on Compressive Strength , 2005 .

[13]  Ľubomír Bágeľ.  Strength and pore structure of ternary blended cement mortars containing blast furnace slag and silica fume , 1998 .

[14]  Surendra P. Shah,et al.  Effects of curing conditions on properties of concrete using slag replacement , 2000 .

[15]  S. Tsivilis,et al.  EXPLOITATION OF POOR GREEK KAOLINS: STRENGTH DEVELOPMENT OF METAKAOLIN CONCRETE AND EVALUATION BY MEANS OF K-VALUE , 2004 .

[16]  Velu Saraswathy,et al.  Studies on the corrosion resistance of reinforced steel in concrete with ground granulated blast-furnace slag--An overview. , 2006, Journal of hazardous materials.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  Ramazan Demirboga,et al.  RELATIONSHIP BETWEEN ULTRASONIC VELOCITY AND COMPRESSIVE STRENGTH FOR HIGH-VOLUME MINERAL-ADMIXTURED CONCRETE , 2004 .

[19]  Chiara F. Ferraris,et al.  The influence of mineral admixtures on the rheology of cement paste and concrete , 2001 .

[20]  Erick Ringot,et al.  Efficiency of mineral admixtures in mortars: Quantification of the physical and chemical effects of fine admixtures in relation with compressive strength , 2006 .

[21]  S. E. Chidiac,et al.  Evolution of mechanical properties of concrete containing ground granulated blast furnace slag and effects on the scaling resistance test at 28 days , 2008 .

[22]  Davut Hanbay,et al.  An expert system based on wavelet decomposition and neural network for modeling Chua's circuit , 2008, Expert Syst. Appl..

[23]  Davut Hanbay,et al.  Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks , 2008, Expert Syst. Appl..

[24]  J. Sharp,et al.  The microstructure and mechanical properties of blended cements hydrated at various temperatures , 2001 .