Study on strength parameters of steel fiber reinforced high. strength concrete

Transcription

1 by the authors Licensee IJASER- Under Creative Commons License 3.0 Research article ISSN Study on strength parameters of steel fiber reinforced high strength concrete 1 Raikar R.V, 2 Karjinni V. V, 3 Gundakalle V.D 1- Professor, Department of Civil Engineering, K. L. E. S. College of Engineering and Technology, Belgaum 59000,Karnataka. rvraikar@gmail.com 2- Director, Sanjay Ghodawat Group of Institutions, Atigre, Kolhapur 4111, Maharashtra. 3- Associate Professor, Department of Civil Engineering, K. L. E. S. College of Engineering and Technology, Belgaum 59000, Karnataka doi:.0/ijaser Abstract: In general concrete is a brittle material having low tensile strength and is prone to cracking. The increase in compressive strength of High Strength Concrete (HSC) not only increases brittleness but also reduces the ductility of the concrete. The introduction of steel or polymeric fibers in HSC improves the ductility. In this context, the paper presents the results of the experimental study on the strength parameters of Steel Fiber Reinforced HSC such as compressive strength, split tensile strength and flexural strength. The HSC used M0 grade with steel fibers added by volume in the range of 0 to 2.5% with an increment of 0.25%. The length of fibers was 25 mm to 0 mm. The experimental results were used to develop Artificial Neural Network (ANN) model for the prediction of strength parameters. It was observed that the ANN model with architecture trained with Levenberg-Marquardt rule resulted into satisfactory performance for the prediction of strength parameters. Further regression models were also developed based on the experimental data. It was found that all the three strength parameters increase with an increase in percentage of steel fibers. However, the compressive strength increases with increase in fiber length from 25 mm to 50 mm and then decreases when the fiber length becomes 0 mm, while the split tensile strength increases with increase fiber length. On the other hand, flexural strength shows mixed trend. Keywords: High strength concrete, steel fibers; compressive strength, split tensile strength, flexural strength, artificial neural network. 1. Introduction Concrete is a vital construction material having high compressive strength and comparatively low tensile strength. The presence of internal flaws and micro cracks make the concrete low tensile resistant (Sing et al. 2005). In addition, concrete is a brittle material primarily because of its low strain capacity. Further, the increase in compressive strength of high strength concrete enhances the brittleness of the concrete. The addition of steel fibers in the concrete increases the ductility of the concrete as well as toughness (Vervacke and Moyson 199). Many investigators (Appa Rao and Raghu Prasad 2005; Bayasi and Kaiser 2001; Miloud 2005; Rapoport et al. 2001; Appa Rao 2004; Veera Reddy 2005; Thomas and Ramaswamy 2004) have studied the effect of fiber reinforcement on HSC. Appa Rao and Raghu Prasad (2005) found that the fracture energy and toughness of high strength concrete increases significantly with increase in volume fraction of steel fibers. The presence of steel fiber in the concrete improves the crack arresting property of concrete and increases the closure stress at constant crack width (Bayasi and Kaiser 2001). This would increase the energy absorption capacity of the fiber composite. In addition, the fiber concrete may be used as tensile skin to cover steel reinforcement. According to Miloud (2005), the presence of steel fibers in concrete increases its permeability coefficient. Rapoport et al. (2001) indicated that the steel reinforcing macro fibers reduce the permeability of cracked concrete particularly at large *Corresponding author ( rvraikar@gmail.com) Received on June 19, 2012; Accepted on August 09, 2012; Published on August 2,

2 crack widths due to crack stitching. The addition of steel fibers also helps in significant enhancement of the fracture energy (Appa Rao 2004). Veera Reddy (2005) reported the optimum percentage of steel fibers as 1.5% by volume, which increase the split tensile strength and flexural strength of HSC, respectively by 7.73% and 4.2%. Thomas and Ramaswamy (2004) compared the mechanical properties of normal strength concrete and HSC (M5) reinforced with steel fibers up to 1.5% by volume. In all the above studies, the maximum percentage of steel fibers added was 1.5% by volume. On the other hand, in the recent years, the soft computing tools are used in modeling as well as predicting many engineering systems as they differ from conventional hard computing in many ways like their tolerance to imprecision, robustness and being low in solution cost. In fact, soft computing is the replica of a human brain. Amongst the various soft computing tools, artificial neural networks (ANN) are potentially used in predicting the strength parameters of concrete. The diversified application of ANNs is reported by ASCE Task Committee (2000a, 2000b). Jain et al. (200) and Noorzaei et al. (2007) used ANN in modeling compressive strength of concrete. Ramugade (20) developed the formulae and used Artificial Neural Networks to estimate the compressive strength of concrete. In addition, ANNs find applications in many fields of Civil Engineering. The present study emphasizes on the study of strength parameters of HSC with steel fiber reinforcement up to 2.5%. The strength parameters considered were compressive strength, split tensile strength and flexural strength. The experimental results were used to develop ANN model as well as regression based models for the prediction of strength parameters. 2. Experimental procedure The high strength concrete (HSC) of M0 mix with crimped steel fibers were used in the present study. The ingredients of M0 mix HSC are 1. Cement: 53 grade Ordinary Portland Cement (OPC) satisfying IS1229:197 was used. 2. Aggregates: The fine aggregate used has fineness modulus of 2.2 and confirmed to Zone III grading of IS33:1970. On the other hand, coarse aggregates of size 12.5 mm and down with angular shape also confirming IS33:1970 were used. 3. Mineral Admixtures: The fly ash from Thermal Power Station, Shaktinagar, Raichur, Karnataka was employed to produce HSC. 4. Chemical Admixtures: To improve workability of HSC, a super plasticizer Conplast SP430 procured from FOSROC Chemicals, Bangalore was utilized. The Mehta-Aiticin (1990) method was used to obtain the mix proportion for M0 mix concrete. The proportion of ingredients is Cement: Fine Aggregates: Coarse Aggregates: Fly Ash = 1: 1.41: 2.14: % with water-binder ratio of The crimped steel fibers were procured from Stewols, India Limited, Nagpur, Maharashtra. The length of steel fibers was 25 mm, 3 mm, 50 mm and 0 mm having aspect ratio of 34, 51, 7 and 7. The average width and thickness of fibers were 2.5 mm and 0.75 mm, respectively. The concrete cube specimens of 150 mm 150 mm 150 mm were casted and tested for compressive strength following IS 51: For flexural strength, steel beams of 0 mm 0 mm 500 mm were tested. The cylindrical test specimen of 150 mm diameter and 300 mm length were casted to study the split tensile strength according to IS 51: The casting of specimen and their testing was done at the Department of Civil Engineering, K. L. E. S. College of Engineering and Technology, Belgaum, Karnataka. Table 1 furnishes the experimental results. 13

3 Strength parameters Fiber Volume (%) Table 1: Experimental data Note: 1. CS Compressive strength (MPa); STS Split tensile strength (MPa); FS Flexural strength (MPa) 2. * represents the data used for testing and other data for training the ANN 3. Artificial neural network and database An ANN is a massively parallel-distributed information-processing system that has certain performance characteristics resembling biological neural networks of the human brain (Haykins 1994), which is introduced by McCulloch and Pitts (1943). The ability of ANNs in identifying a relationship from a given patterns make it possible in solving large-scale complex problems such as pattern recognition, nonlinear modeling, classification, association and control. The characteristics of a neural network are defined by its architecture, the method of determining the connection weights, and the activation function (Fausett 1994). Fiber Length 25 mm 3 mm 50 mm 0 mm CS STS FS CS STS FS CS STS FS CS STS FS *.53* 5.7* *.35* 5.94* *.4*.30* * 9.1*.37* * 9.30*.*.97* 11.00* 7.0* * 9.00*.0* * 13.01*.49* * 12.23*.91* * 9.45* 7.4* Fiber length (mm) Fiber volume (%) Net Input Input Layer... Hidden Layers Compressive strength (MPa) Split tensile strength (MPa) Flexural strength (MPa) Net Output Output Layer Figure 1 Schematic representation of a three-layer feed-forward ANN The architecture of a neural network represents the number of layers, number of neurons in each layer and the pattern of connection between neurons. An ANN architecture consists of set of neurons (information processing cells) arranged in three different layers: input layer, hidden layers and output layer 14

4 (see Figure 1). These neurons are connected by links whose strength is represented by synaptic weights. Each neuron typically applies a nonlinear transformation called an activation function to its net input to determine its output signal. In the present study, multilayered perceptron is used to predict the strength parameters of HSC. Figure 1 represents the typical three-layered ANN architecture considered in the study. It includes two neurons in the input layer corresponding to two inputs; fiber volume in % and fiber length in mm, one hidden layer of neurons, and three neurons in the output layer representing three strength parameters (compressive strength, split tensile strength and flexural strength). The optimum number of hidden layers and corresponding number of neurons in each hidden layer is determined based on the performance goal attained by the neural network and the number of epochs during training. The feed-forward back-propagation technique was adopted in training the network. The MATLAB-Neural Network toolbox is used for the development of ANN model. Among 44 data points, 33 data points (75%) are randomly selected for training the network and remaining 11 data (25%) are employed for the validation. The mean squared error (MSE) of was used. The numbers of epochs varied based on the training requirements. Initially, all the training algorithms were tested with the single hidden layer having five neuron, taking the target level as to check the suitability of a particular training procedure. Table 2 gives the comparison. From Table 2 it can be observed that, Levenberg-Marquardt algorithm gives better results as compared with other algorithms with lesser number of epochs (9) and higher value of correlation coefficient of Hence, the Levenberg-Marquardt (trainlm) algorithm is used for training the network. Table 2: Comparison of different training algorithms Training algorithm Correlation coefficient Epochs Target reached Training with bias trainb Levenberg-Marquardt trainlm Fletcher-Reeves conjugate gradient traincgf Gradient descent with adaptive learning rule backpropagation traingda Resilient backpropagation trainrp Adaptive learning rate traingdx Polak-Ribiere conjugate gradient traincgp Powell-Baele conjugate gradient traincgb Scaled conjugate gradient tarinscg Quasi Newton backpropagation trainbfg One step secant method trainoss Sequential order incremental training with learning functions trains Gradient descent with backpropagation traingd Results and discussions 4.1 Experimental results The experimental data (Table 1) of compressive strength of fiber reinforced HSC obtained for fiber lengths 25 mm 0 mm and fiber volume varying from 0 2.5% is used to plot the variation as shown in 15

5 Figure 2 (a), which shows that compressive strength of fiber reinforced HSC increases almost linearly with increase in fiber volume. The increase in compressive strength with fiber volume is up to 2% of fiber volume, while it decreases for fiber volume more than 2%. Therefore, 2% fiber volume can be considered as optimum. It is pertinent to mention that the optimum fiber volume depends on the concrete mix as well as type of fibers. In addition, the compressive strength increases with increase in fiber length up to 50 mm, while it decreases for fiber length of 0 mm. The probable reason is that the increase in fiber length causes the ball effect as a result of movement of longer fibers towards each other during mixing thereby reducing the workability of the concrete. Consequently, the compressive strength decreases for larger fiber lengths. Figure 2 (b) depicts the variation of split tensile strength of fiber reinforced HSC with percentage of fiber volume for fiber lengths 25 mm 0 mm. The split tensile strength of fiber reinforced HSC increases with increase in both fiber volume and fiber length. It indicates that the fiber addition enhances the split tensile strength by holding the matrix. Further, Figure 2 (c) presents the variation of flexural strength of fiber reinforced HSC with percentage of fiber volume and fiber lengths of 25 mm 0 mm. The flexural strength of fiber reinforced HSC also increases with increase in fiber volume and fiber length up to 50 mm. The flexural strength decreases with fiber length of 0 mm beyond 2% of fiber volume. Thus the fiber volume of 2% can be considered as optimum fiber content. However, earlier investigations reported 1.5% by volume of steel fibers is the optimum content (Veera Reddy 2005). 4.2 ANN model In the present study, ANN architectures having single hidden layer with number of neurons in hidden layer ranging from 1 to 1 were considered. The target level (MSE) of was used in ANN modeling. For training of the network, the Levenberg-Marquardt (trainlm) algorithm was employed, which was found more appropriate amongst other training algorithms (see Table 2). Table 3 presents the performance parameters of ANN architectures with different number of neurons in hidden layers, both during training and testing, which include correlation coefficient, MSE goal and number of epochs. From Table 3, it can be observed that the ANN with architecture gives best results amongst all other networks considered. Table 3: Performance details of ANN architectures Number of neurons in hidden layer MSE Goal ( -3 ) Epochs Correlation coefficient

6 Compressive strength (MPa) Fiber length 25 mm 3 mm 50 mm 0 mm (a) Fiber volume (%) 1 Fiber length Split tensile strength (MPa) mm 3 mm 50 mm 0 mm (b) Fiber length Fiber volume (%) Flexural strength (MPa) 25 mm 3 mm 50 mm 0 mm 4 (c) Fiber volume (%) Figure 2: Variation of (a) Compressive strength (b) Split tensile strength (c) Flexural strength with fiber volume and fiber length 17

7 Actual value Predicted value 7 7 Compressive strength (MPa) (a) Compressive strength (MPa) (d) Training Pattern Testing Pattern Split tensile strength (MPa) 4 (b) Split tensile strength (MPa) 4 (e) Training Pattern 14 Testing Pattern Flexural strength (MPa) Flexural strength (MPa) 12 (c) (f) Training Pattern Testing Pattern Figure 3: Comparison of strength parameters predicted by ANN model with experimental values: (a-c) Training; and (b) Testing 1

8 75 Compressive Strength 75 Compressive Strength Regression Model Values (a) Regression Model Values (d) Experimental Values Split Tensile Strength ANN Model Values Split Tensile Strength Regression M odel Values 4 (b) Regression M odel Values 4 (e) Experimental Values Flexural Strength 15 ANN Model Values Flexural Strength Regression M odel Values 12 9 (c) Regression M odel Values 12 9 (f) Experimental Values ANN Model Values Figure 4: Comparison of strength parameters (in MPa) predicted by regression model with: (a-c) experimental values; and (d-f) ANN values 19

9 The comparison of strength parameters predicted by ANN model having architecture with the actual values both during training and testing are illustrated in Figure 3. Figures 3 (a-c) show the comparison for training data while Figures 3 (d-f) for testing data. During the training stage the ANN predictions demonstrate satisfactory matching. However, at the testing stage, there is a slight deviation. The correlation coefficients between predicted and actual values of different strength parameters along with standard errors are furnished in Table 4. The results given in Table 4 indicate that the ANN model of architecture gives the satisfactory results in the prediction of strength parameters of fiber reinforced HSC. Table 4: Performance of ANN architecture Strength Parameters Training data Testing data Total data Standard Correlation Standard Correlation error coefficient error coefficient Correlation coefficient Standard error Compressive strength Split tensile strength Flexural strength Regression model The experimental data of strength parameters (compressive strength, split tensile strength and flexural strength) of fiber reinforced HSC, given in Tables 1 are used for the regression analysis, which yields the Eqs. (1-3) CS = x y (1) STS = 4.9 x y (2) FS = 3.4 x y (3) where, CS = compressive strength in MPa; STS = split tensile strength in MPa; FS = flexural strength in MPa; x = volume of fibers in %; and y = length of fibers in mm. The comparisons of strength parameters (compressive strength, split tensile strength and flexural strength) of fiber reinforced HSC estimated from the above equation with the experimental data are shown in Figure 4 (a-c). The correlation coefficient (and standard error) between the computed and the experimentally obtained values of compressive strengths, split tensile strengths and flexural strengths are respectively 0.32 (0.115), (0.112) and (0.39). It indicates that the above equations fit well with the experimental data. Further, the comparisons of strength parameters of fiber reinforced HSC predicted by regression equations and ANN model are also made. Figure 4 (d-f) presents the relation between the predicted values of strength parameters by both these methods having correlation coefficients (and standard error) of 0.3 (1.305), 0.99 (0.39) and (0.599), respectively. This indicates that both ANN model and regression models can be effectively used to predict all the three strength parameters of fiber reinforced HSC. 5. Conclusions The experimental data on the strength parameters of steel fiber reinforced HSC such as compressive strength, split tensile strength and flexural strength are presented in the paper. M0 grade of concrete with steel fibers added in the range of 0 to 2.5% by volume is considered. It was observed that all the three strength parameters increase with an increase in percentage of steel fibers. On the other hand, the compressive strength increases with increase in fiber length from 25 mm to 50 mm and then decreases for 20

10 fiber length of 0 mm, while the split tensile strength increases with increase fiber length. However, the flexural strength shows mixed trend. Further, using the experimental results an ANN and regression models have been developed. The ANN model with architecture trained with Levenberg-Marquardt rule is found to predict the strength parameters satisfactorily. Also, the regression equations estimated the strength parameters reasonably acceptable. References 1. Sing, S. P., Mohammadi, Y., and Kausik, S.K Flexural fatigue analysis of steel fibers concrete containing mixed fibers. ACI Materials Journal, November-December, Vervacke, K., and Moyson, D Shotcrete application with steel fibers. The Indian Concrete Journal, 70(), Appa Rao, G., and Raghu Prasad, B. K Fracture energy of fiber reinforced high-strength concrete. Journal of Structural Engineering, 31(4), Bayasi, Z., and Kaiser, H Steel fibers as crack arrestors in concrete. The Indian Concrete Journal, 75(3), Miloud, B Permeability and porosity characteristics of steel fiber reinforced concrete. Asian Journal of Civil Engineering (Building and Housing), (4), Rapoport, J., Aldea, C-M., Shah, S. P., Ankenman, B., and Karr, A. F Permeability of cracked steel fiber-reinforced concrete. National Institute of Statistical Sciences, Research Triangle Park, NC , Technical Report No Appa Rao, G Influence of specimen size and geometry on fracture toughness of fiber-reinforced high-strength concrete. Proceedings of ICFRC International Conference on Fiber Composites, High Performance Concretes and Smart Materials, , Chennai, India.. Veera Reddy, M Experimental study on M0 grade concrete using silica fume and steel fibers. Proceedings of Second National Conference on Advances in Materials and Mechanics of Concrete Structures, 5-3, Indian Institute of Technology, Madras, India. 9. Thomas, J., and Ramaswamy, A A comparative study on properties of steel-fiber reinforced high-strength concrete. Proceedings of ICFRC International Conference on Fiber Composites, High Performance Concretes and Smart Materials, , Chennai, India.. ASCE Task Committee. 2000a. Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering, 5(2), DOI: (2000)5:2(115) 11. ASCE Task Committee. 2000b. Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering, 5(2), DOI: (2000)5:2(124) 12. Jain, A., Jha, S., and Misra, S Modeling compressive strength of concrete using ANN. The Indian Concrete Journal, 0(), Noorzaei, J., Hakim, S. J. S., Jaafar, M. S., Abang Ali, A. A. and Thanoon, W. A. M An optimal architecture of artificial neural network for predicting compressive strength of concrete. The Indian Concrete Journal, 1(), Ramugade, P. D. 20. Estimating compressive strength. The Indian Concrete Journal, 1(7), Mehta, P. K., and Aitcin, P. C Principles underlying production of high performance 21

11 concrete. Cement and Aggregates, 12(2), Haykins, S Neural networks: a comprehensive foundation. MacMillan, New York. 17. McCulloch, W. S., and Pitts, W A logical calculus of the ideas immanent in nervous activity. Bulletin on Mathematics and Biophysics, 5, Fausett, L Fundamentals of neural networks. Prentice Hall, Englewood Cliffs, N.J. 22