Advances in Engineering Software 40 (2009) 334–340Contents lists available at ScienceDirect Advances in Engineering Software journal homepage: www.elsevier.com/locate/advengsoft Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network Cahit Bilim a, Cengiz D. Atisß b, Harun Tanyildizi c,*, Okan Karahan b a Civil Engineering Department, Mersin University, 33343 Mersin, Turkey Civil Engineering Department, Erciyes University, 38039 Kayseri, Turkey c Construction Education Department, Fırat University, 23100 Elazıg˘, Turkey b a r t i c l e i n f o Article history: Received 18 March 2008 Received in revised form 12 May 2008 Accepted 19 May 2008 Available online 16 July 2008 Keywords: Concrete Ground granulated blast furnace slag Compressive strength Modeling Prediction Artificial neural networks a b s t r a c t 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 450 kg/m3) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22 ± 2 °C) 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. Ó 2008 Elsevier Ltd. All rights reserved. 1. Introduction Concrete is the most important element of the construction industry. Since durability is one of the critical problems to construct reinforced concrete structures with long service life and develop construction technologies due to some environmental and economical reasons in recent years, it is important to produce well-designed concrete as a durable construction material. However, large amounts of natural sources such as gravel, sand, water and cement are used in concrete production. Also, 3 billion tons of raw materials are used in each year for cement production in the world [1,2] and, cement manufacturing is responsible for about 2.5% of total worldwide CO2 emissions from industrial sources [3,4]. High consumption of natural sources, high amount of production of industrial wastes and environmental pollution require obtaining new solutions for a sustainable development. One of the most effective ways to minimize the environmental effect is to use mineral admixtures such as ground granulated blast furnace slag, fly ash and silica fume, as a partial cement replacement. The use of mineral admixtures in concrete production improves the compressive strength, pore structure, and permeability of the mortars and concretes [3,5], this is attributed to the pozzolanic reaction [3,6]. This approach will have the potential to reduce costs, * Corresponding author. Tel.: +90 424 2370000x4268; fax: +90 424 2367064. E-mail address: htanyildizi@firat.edu.tr (H. Tanyildizi). 0965-9978/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.advengsoft.2008.05.005 conserve energy, and waste minimization [3,4,7] and the lower cement requirement also leads to a reduction for CO2 generated by the production of cement [8–12]. Blast furnace slag is a by-product obtained in the manufacture of pig iron in the blast furnace and, is formed by the combination of earthy constituents of iron ore with limestone flux. When the molten slag is quickly quenched with water in a pond, or cooled with powerful water jets, it forms into a fine, granular, almost fully non-crystalline, glassy form known as granulated slag, having latent hydraulic properties. Such granulated slag, when finely ground and combined with Portland cement, has been found to exhibit excellent cementitious properties [13]. Using ground granulated blast furnace slag as a supplementary cementitious material in Portland cement concrete has many advantages, including improved durability, workability and economic benefits [14]. When ground granulated blast furnace slag is used as cement replacement, one improvement is the compressive strength due in part to the fineness of the ground granulated blast furnace slag and to the chemical hydration [15–18]. Ground granulated blast furnace slag, which is latently hydraulic, undergoes hydration reactions in the presence of water and calcium hydroxide, Ca(OH)2. This secondary pozzolanic reaction yields a denser microstructure because the Ca(OH)2 is consumed and C– S–H paste is formed [15,19,20]. The partial replacement of slag by weight may decrease the early strength, but increase the later strength and improve microstructure and durability of hardened 5-80 450 360 270 180 90 450 360 270 180 90 450 360 270 180 90 – 90 180 270 360 – 90 180 270 360 – 90 180 270 360 135 135 135 135 135 180 180 180 180 180 225 225 225 225 225 18.40 – – – – 1950 1940 1935 1925 1915 1845 1835 1825 1820 1810 1735 1730 1720 1710 1705 450-0.2.5-40 400-0. For this purpose.91 32. Aggregate Dry and clean natural.1.3-20 450-0. The aim of this paper is to construct an ANN model to predict the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients and age.2.60 6.5 N/mm2) with a specific gravity of 3. Initial and final setting times of the cement were 2 h and 30 min and 3 h 30 min.60 2. The modification is made by replacing the cement with GGBFS for a given ratio on mass basis.4-20 450-0.4-80 350-0.00 9.25 100 60 36 21 12 7 3 100 76 56 42 32 20 8 100 88 74 62 49 35 18 100. Furthermore.3-80 400-0. Experimental study 2.4-40 400-0.335 C.96 – 0. river aggregate was used in concrete mixture.3-20 350-0. then.98 62.3-60 450-0. approximate concrete composition is given in Table 3. The gravel was 16 mm maximum nominal size with 1.4-60 350-0.99 0. Its chemical oxide composition is given in Table 1.3-00 400-0.3-00 450-0.25 4.00 5.5-20 400-0. a computer program was developed in MATLAB.3.4-60 400-0.70 5.5-00 450-0.00 4.3-20 400-0. GGBFS concrete was produced by modifying PC concrete.50 1. The grading of the mixed aggregate was presented in Table 2 with the standard limit [27]. 400.60 0. Cement The cement used was ASTM Type I normal Portland cement (PC 42.4-20 350-0.5-80 400 320 240 160 80 400 320 240 160 80 400 320 240 160 80 – 80 160 240 320 – 80 160 240 320 – 80 160 240 320 120 120 120 120 120 160 160 160 160 160 200 200 200 200 200 16. (kg/m3) 350-0. binder content (350.3-40 350-0.20 14. 0.67 g/cm3.40 11.60 5. 450 kg/m3) and water–cement ratio (0. According to ASTM C 989 [26] hydraulic activity index.42 0. Then.5 0. Table 2 shows the aggregate grading is suitable for concrete production. Measured .8% and its relative density at SSD condition was 2.1.70 – – – – 2030 2025 2015 2010 2000 1940 1930 1925 1915 1910 1845 1840 1830 1825 1815 400-0.10 4.80 0.4-80 450-0. the GGBFS used was classified as a category 80 slag. the volume of aggregate for each GGBFS concrete was compensated accordingly using absolute volume method. 0.5-60 350-0. The amount of hyperplasticizer was given in Table 3.76 Mixture PC (kg/m3) GGBFS (kg/m3) W (lt/m3) HP (kg/m3) Agg. The increase in the paste volume due to inclusion of GGBFS was considered. hydration of ground granulated blast furnace slag is much more sensitive to temperature than Portland cement and the strength development of ground granulated blast furnace slag concrete is considerably slower under standard 20 °C curing conditions than that of Portland cement concrete.4 12.1. Concrete mixture proportions For each concrete of a cubic meter. The specific gravity of GGBFS was 2.3-60 400-0. Mixture design is made with according to absolute volume method given by Turkish Standard TS 802 [28].1.12 2. the volume of aggregate was determined for each control PC concrete by assuming 2% air is trapped in fresh concrete Table 1 Chemical composition of cement and GGBFS (%) Oxide SiO2 Al2O3 Fe2O3 CaO MgO SO3 LOI Na2O K2O Cement GGBFS 19.25 0.40 3. However.25 1. The volume of aggregate was used to determine the aggregate weight.9 3.73 0.5-20 350-0. Its Blaine specific surface area was 3250 cm2/g and its chemical composition is given in Table 1. 2.4-00 450-0.1. / Advances in Engineering Software 40 (2009) 334–340 Portland cement and concrete very significantly [21.3% water absorption value and its relative density at saturated surface dry (SSD) condition was 2.70 9. the results obtained from the ANN model were compared with the average results of the experiments.00 4.5-00 400-0.5-60 400-0.0 74.72 0.5) were chosen as constant.4-80 400-0.71 36.1. The water absorption value of the sand used was 1.5-00 350-0.00 14.00 14.5-60 450-0.0 Table 3 Approximate concrete composition for a cubic meter 2.3-00 350-0.3-80 450-0.70 g/cm3. respectively. At the beginning of the mixture design. Bilim et al.7 41.4-00 350-0.3.25 11. 2.5-20 450-0.50 3.3-40 400-0. Plasticizer A carboxilic-type hyperplasticizing (HP) admixture was used at various amounts to maintain the workability of fresh concrete.90 0.80 6.4-40 350-0.54 10.22].3-80 350-0.75 2.61 2.3-40 450-0.0 23.00 4.4.55 8.4-20 400-0.00 2. Properties of materials 2.25 2.5-40 350-0. Table 2 Mixed aggregate grading with standard limit Sieve size (mm) % Passed TS 706 lower limit TS 706 medium limit TS 706 upper limit Mixed aggregate 16 8 4 2 1 0. The GGBFS was ground granulated in Iskenderun Cement Factory to have a Blaine specific surface area about 4250 cm2/g.4. Ground granulated blast furnace slag (GGBFS) GGBFS was supplied from Iskenderun Iron–Steel Factory in Turkey.81 g/cm3.3-60 350-0.4-00 400-0.21 3.75 7.4-60 450-0.4-40 450-0.00 8.5-80 350 280 210 140 70 350 280 210 140 70 350 280 210 140 70 – 70 140 210 280 – 70 140 210 280 – 70 140 210 280 105 105 105 105 105 140 140 140 140 140 175 175 175 175 175 12. 2.5-40 450-0. although the ultimate strength is higher for the same water–binder ratio [23–25].7 17.16 g/cm3.20 3.35 – – – – – 1865 1860 1850 1840 1830 1750 1740 1730 1720 1710 1630 1620 1610 1600 1590 suggested by TS 802. 2. 50) show that.9 34.9 0.4 81.3 73.4 71. This secondary pozzolanic reaction yields a denser microstructure because the Ca(OH)2 is consumed and C–S–H paste is formed. 0.5 66.8 61.1 46.0 64.50 0 20 40 60 80 26.40 0 20 40 60 80 53. 90 and 360 days.3 Age (days) up to 7 days of age.1 44.4 86.8 35.2 28.3 78.4 34. the strength losses at 3 days are 15% for 20% slag replacement ratio.8 87.4 68.1 57.7 72.2 57. Samples (with 150 mm a side) produced from fresh concrete were demoulded after a day.4 14.6 80.3 MPa/s.3 39. This decrease observed at the beginning is due to the relatively slower rate of pozzolanic hydration process. But at later ages slag. For instance.5 65.7 0.1 35. slag replacement by weight decreases the strength of concretes at early ages when compared to control Portland cement concrete.9 54.8 81.5 48.2 56.0 77.1 48.50 water–binder ratios.4 0.6 66.2 61.1 92.9 72. Compressive strength of slag concrete is found to be equivalent to that of control normal Portland cement concrete for 60% replacement ratio at 28 days and beyond.5 73.7 51. However.4 52.3 74.9 96. three months and one year.4 49. when the concrete made with 350 kg/ m3 cement dosage and 0.8 75.3 20.9 86. Ca(OH)2.1 66. however. 27% and 43%.7 83.6 64. Tables 4–6.9 65.0 58.8 95. compressive strength of slag is found to be satisfactory when compared to control normal Portland cement concrete for 80% replacement ratio.8 81.0 55.30 0 20 40 60 80 61.40 0 20 40 60 80 49.1 53.3 56.8 83.3 16.6 45.3 81.8 71.8 53.4 57.7 78.1 29.7 13.7 65.0 34.9 39.4 77.1 65.8 18.2 66. Furthermore.5 51.5 27. 3.5 50.6 63.2 79.2 41.6 36.6 40.4 65.6 23.1 88.4 54. Compressive strength of each specimen was determined using TS-EN 12390-1.0 49.4 39.7 21.8 67.7 50. the strength loss of concrete with increasing the slag replacement level is more marked at early ages up to 7 days.4 79.0 82.2 53.8 17.9 28. in comparison with control normal Portland cement concrete.2 50. for all slag replacement ratio.6 76.8 68. then.6 38. As a result.9 37. However. Experimental results The compressive strength of the concrete studied were presented in Tables 4–6. 400. .1 91.7 74.2 70.4 48.6 0.6 31.6 84.8 71. concrete containing slag exhibits an equivalent or a greater final strength than that of control normal Portland cement concrete.9 0.0 36.4 92.40 and 0.6 59. the loading rate was 0.9 72.0 69.9 58.8 85.336 C.3 16. 2.0 66.8 80. undergoes hydration reactions in the presence of water with calcium hydroxide. 0. respectively.3.9 45. Workability value of fresh concrete obtained from flow table was in the order of 40–50 cm.0 82.6 57.6 2. 3. and 30% for 40% slag replacement ratio.40 water–binder ratio is examined.0 60.8 76.9 26. Table 6 Compressive strength of GGBFS concrete for 450 kg/m3 (MPa) w/c ratio GGBFS (%) 3 7 28 90 360 0.6 33.5 80.2 90. which is latently hydraulic.5 32.8 58.3 85.3 38.9 22.7 76.8 36. while the compressive strength of slag concrete at 28 days for 20% slag replacement ratio is 3% more than that of control normal Portland cement concrete. / Advances in Engineering Software 40 (2009) 334–340 unit weight of fresh concrete was in the range of between 2350 and 2550 kg/m3.1 56.2 53.0 69.7 90.6 58.40 0 20 40 60 80 45.3 64. 4 [29–32].8 46.3 75.7 51.9 91.4 8.8 24.0 9.0 86.2 36. For example. theoretical fresh unit weight determined from mixture proportions was in the range of between 2270 and 2500 kg/m3.30 0 20 40 60 80 63.8 21. 0.5 58.1 25. the increase in the water–cementitious material ratio decreases more the strength of concrete having particularly high percentages of slag.3 81.6 90.8 69.8 101. compressive strength of concrete containing slag concrete is higher than that of control normal Portland cement concrete for 20% and 40% replacement ratios at 28 days. the accrual for concrete containing 40% slag replacement ratio is 5% approximately.8 67. On the other hand.0 76.0 31. Test procedure Three cubic samples were used for each age.6 79.2 80.3 63.3 56.9 27. when compared to control normal Portland cement concrete. However.0 29.0 73.0 0.1 66.0 33.30.2 28. 28.9 49.3 48.4 63.8 65. this negative effect disappears at later ages (28 days and beyond) and concrete containing slag exhibits an equivalent or a greater final strength than that of control normal Portland cement concrete. In general.3 31.1 62.3 24.0 73. samples were cured at 22 ± 2 °C with 100% RH until the samples were used for compressive strength measurement at 3. The results obtained in this laboratory research which conducted to the concretes made with three different cement dosages (350.9 17. Bilim et al.9 61.3 79.7 85.9 39.50 0 20 40 60 80 25.6 51.0 22.40.4 26.4 82.8 26.4 89.9 49.50 0 20 40 60 80 38.8 12.3 73.5 56.30 0 20 40 60 80 62.2 82. the strength losses at 28 days for concrete containing 450 kg/m3 cement dosage and 80% slag replacement ratio are increasing 17%.9 66.9 22.9 75.4 61.3 38.3 62.7 50.2 22. 7.1 67.9 61.7 81.0 49.2 60. In addition.3 45. show that. strength contribution of slag to concrete is low Table 4 Compressive strength of GGBFS concrete for 350 kg/m3 (MPa) w/c ratio GGBFS (%) Age (days) 3 7 28 90 360 0. Compressive strength measurements were carried out using ELE International ADR 3000 hydraulic press with a capacity of 3000 kN.4 49.7 64.8 70.2 89. for 0.8 50.4 92. 450 kg/m3) and three different water–binder ratios (0.0 45.30.3 18.1 Table 5 Compressive strength of GGBFS concrete for 400 kg/m3 (MPa) w/c ratio GGBFS (%) Age (days) 3 7 28 90 360 0.5 41.5 77.7 65.9 97.8 50. 1.) V Fig. In this study. step are used. the problem is proposed to network models by means of six inputs and one output parameter. These neurons are connected with connection link. amount of aggregate. The computer program was performed under MATLAB software using the neural network toolbox. a number of considerations must be taken into account. A schematic diagram for an artificial neuron model is given in Fig. amount of blast furnace slag. A data set including 225 data samples obtained from experimental studies were used for artificial neural networks. the activation function need to be determined. The model output variable was the compressive strength of the concrete. amount of water and age of samples were selected as input variables.C. Hidden Layer Σ Output Layer Σ Σ Inputs Σ Σ Σ Σ bias bias Fig. 4.Xn) represent the n input applied to the neuron. Each neuron has an activation function to determine the output. 3. Multilayer feed-forward neural network structure. Where Wj represents the weight for input Xj and b is a bias. Usually nonlinear activation functions such as sigmoid. . / Advances in Engineering Software 40 (2009) 334–340 of activation functions. Input layer Cement Blast furnace slag xj wj b and V ¼ f ðuÞ Hidden layer Output layer 1 2 Hyperplasticizing Compressive Strength Aggregate Water Age ð1Þ Artifical neural networks are systems that are deliberately constructed to make use of some organizational principles resembling those of the human brain [33–35]. 2. Then. There are many kind of ANN structure. then the output of the neuron is given by Eq. They represent the promising new generation of information processing systems. There are many kinds Xm Σ W3 u f (. Each link has a weight that is multiplied by transmitted signal in network. 2. amount of hyperplasticizer. Artificial neuron model. One of these is multilayer feed-forward ANN and is shown in Fig. When designing an ANN model. . Let X = (X1. Generally desired model consists of a number of layers. the number of neuron on the hidden layer is fifteen. These connections can be bidirectional or unidirectional. X2. In the training. . when an unknown input is applied to the network it can generalize from past experiences and produce a new result [33–35]. (1). ANN architecture. A data set Wn Input Layer m X j¼0 b X2 W1 X W2 3 u¼ X0 X1 337 14 15 Fig. 3. They are consisting of a large number of simple processing elements called as neurons. The all algorithms of ANN were used for this study but the Levenberg–Marquardt (LM) algorithm. ANN can create its own organization or representation of the information it receives during learning time [33–35]. single hidden layer neural network. The number of layers and the number of units in each layer must be chosen. ANN architecture used for this study is given in Fig. Bilim et al. 1. The parameters such as amount of cement. Artificial neural network model for prediction of experimental results Artificial neural networks (ANNs) are biologically inspired and mimic the human brain. The back propagation learning algorithm has been used in a feed-forward. The data were normalized by dividing with max values. scaled conjugate gradient (SCG) algorithm. The most general model assumes complete interconnections between all units. one step secant backpropagation algorithm (OSS) and BFGS quasi-Newton backpropagation algorithm were just learning. At first the suitable structure of the ANN model must be chosen. ANNs are trained by experience. 2 0. From these.8 0.8 1 1.4 0.2 Experimental Results Fig.4 0.4 0.6 0.2 0. Neural Networks Results 1. Training performance for Levenberg–Marquardt algorithm.6 0. Fig. Training performance for scaled conjugate gradient algorithm. 0 0 0. The results are shown in Figs.2 Fig. 9 and 11 present the measured compressive strengths versus predicted compressive strengths by ANN model with R2 coefficients. 1 R2 = 0.2 0.8 1 1.6 0.6 0. 1.94 0.8 0. 5 which present that ANN model predicts the compressive strength of concrete containing blast furnace slag with a R2 of 0.8 0.6 0. Training performance for one step secant backpropagation algorithm. 7.96.2 Experimental Results Neural Networks Results 1. 8. 4–11. Fig.96 0.4 0. It can be seen from Fig.2 Neural Networks Results 338 1 2 R = 0. 113 data patterns were used for training the network. However.2 0 0 0. Figs.92 0. Linear relationship between measured and predicted compressive strengths for scaled conjugate gradient algorithm. 4. Figs. Bilim et al. 5. / Advances in Engineering Software 40 (2009) 334–340 including 225 data samples obtained from experimental studies were used for artificial neural networks.4 0. 9 and 11 show that ANN model predict the compressive strength of concrete con- .2 Experimental Results Fig.2 0 0 0.2 1 R2 = 0. 6. 7. 5.2 Fig. and the remaining 112 patterns were randomly selected and used as the test data set. Linear relationship between measured and predicted compressive strengths for Levenberg–Marquardt algorithm. 9.6 0. Linear relationship between experimental and artificial neural network compressive strengths for one step secant backpropagation algorithm.C. 7.4 0.8 1 1. Mancha H.26(4):347–57. Cement Concr Res 1997. Effect of curing conditions on the mortars with and without GGBFS. Concr Int 2002. [11] Ferraris CH. p. [6] Dongxue L. p. The influence of mineral admixtures on the rheology of cement paste and concrete. Cement Concr Res 2006.95 0. Wu CML. [15] Chidiac SE. Sharp JH. 4. An experimental study on optimum usage of GGBS for the compressive strength of concrete. Supplementary cementing materials for concrete.2 0.36(2):238–44. [13] Erdog˘an TY. [17] Cyr M. Slags and cement containing slag. The training performance during the training process is given in Figs.36(3):434–40. Therefore. Efficiency of mineral admixtures in mortars: quantification of the physical and chemical effects of fine admixtures in relation with compressive strength. Ringot E. 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