Statistical Modeling and Prediction of Compressive Strength of High Performance Concrete

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1 Statistical Modeling and Prediction of Compressive Strength of High Performance Concrete Dr. B. Vidivelli 1 T. Subbulakshmi 2 1 Professor, Department of Structural Engineering, Annamalai University, Chidambaram. 2 Research Scholar, Department of Structural Engineering, Annamalai University, Chidambaram. Abstract The aim of this study is to investigate the ability of the effects of utilizing industrial by-products using statistical analysis and regression methods. In this paper, statistical analysis was made to predict the compressive strength of concrete containing different mixtures at an age of 28 days. The input parameters includes ratio of cement & silica fume, ratio of fine aggregate & bottom ash, ratio of coarse aggregate & steel slag, superplasticizer (ml) and compressive strength (N/mm 2 ) were used as output parameters. A total of 15 mixes was sued for statistical modelling in training phase and testing phase. To construct the model, 8 input parameters were used to achieve one output parameter based on compressive strength of concrete. The performance of the model can be judged by the correlation coefficient and root mean square errors have been adopted as the comparative measures against the experimental results. Keywords: Compressive strength, silica fume, bottom ash, steel slag aggregate, prediction, modelling. 1. Introduction Now a day s very few studies have investigated on the effect of the matrix mixture and its effect on the compressive strength of concrete in construction field. Most of the researchers present experimental results for the different matrix mixture without providing modelling procedure to determine the effect of the different parameters in the studies [1]. Concrete is an essential material in Civil Engineering field. The property of concrete differs depending on various factors. The proportions of its constituents, the construction methods, the loading and environmental conditions to which it will be subjected over time are some of the issues that determine its strength [2]. High performance concrete mixes containing different percentages of metakaolin were tested for strength and durability and have shown better resistances to the attacks of chemicals such as chloride and sulphates [3]. Sumit Kumar et al investigated that the significance of silica fume in enhancing the quality of concrete and found its effect on concrete after addition of silica fume. An experimental investigation on the flexural behaviour of reinforced high performance concrete was conducted by Paramasivam suresh kumar et al by using crushed stone sand replaced by fine aggregate and coarse aggregates in addition with silica fume and fly ash combination with superplasticizer [4]. A. Talah et al to assess the suitability of using marble powder as a partial 146 substitute for Portland cement to produce high performance concrete having constant water binder ratio of 0.5 [5]. Pazhani et al produced High performance concrete by replacing 20%, 40%, 60%, 80% and 100% of fine aggregate with copper slag and 30% of cement with GGBS and tested to assess the durability parameters such as water absorption and chloride ion penetration [6]. Khalifa S. Al Jabri et al suggested that the effect of copper slag as a replacement of sand on the properties of high performance concrete [7]. Sung Won Yoo et al expressed autogenous shrinkage in High performance concrete with w/c ratio 0.3 with mineral admixtures fly ash 0%, 10%, 15%, 20% & 30% and silica fume 0%, 5%, 7.5%, 10% & 15% and chemical admixture shrinkage reducing agent and expansion agent. The autogenous shrinkage in high performance concrete with fly ash was decreased continuously with larger fly ash replacement and silica fume has increased when compared to that in OPC concrete and they explained both of these admixtures in adequate amount can provide decrease in autogenous shrinkage as well as improvement of the strength [8]. 2. Experimental Investigations Ingredients of concrete In the experimental study, commercially available Ordinary Portland Chettinad Cement 43 Grade

2 conforming to IS 8112:1989 [12] was used in the concrete mixture. Locally available river sand was used and passing through 2.36 mm sieve was used. The sand was dried before used to avoid problem of bulking. The sand used conforms to Zone I to IS 383:1970 [13] and Specific Gravity was 2.60 and fineness modulus was Hard Blue granite crushed stone aggregate of sizes ranging from mm were used as per IS 2386:1963 [14]. The shape and particle size distribution of the aggregate is very important as it affects the packing and voids content. The moisture content, water absorption, grading and variations in fines content of all aggregate should be closely and continuously monitored and must be taken into account in order to produce HPC of constant quality. Specific gravity of coarse aggregate used was Ordinary portable tap water available in laboratory was used for making test specimens and curing purpose. Silica fume imparts very good improvement to rheological, mechanical and chemical properties. It improves the mechanical property of the concrete by reinforcing the microstructure through filler effect and thus reduces segregation and bleeding. It is also helps in achieving high early strength. Silica fume used was Elkem Micro silica, Grade 920 D (Non combustible Amorphous SiO 2 Densified) in dry state and packed in 20 kg bags, obtained from Elkem India Private Limited Mumbai, India. Bottom ash obtained from Thermal power plant, Neyveli Lignite Corporation Ltd, at Neyveli. Steel slag aggregate was obtained from Vedha Industries of steel manufacturing company private limited, Karaikal. The specific gravity and fineness modulus of bottom ash was 2.48 and The specific gravity of steel slag aggregate was The mix proportions of the materials are tabulated in Table 1 as per IS 10262:1982 [15]. 3. Casting of Specimens All the test specimens of cube were cast using steel moulds and compacted using a table vibrator. A tilting mixer machine was used for preparing the concrete. The specimens were demoulded after 24 hours and were put into a water tank for curing purpose. For each batch of concrete, three 150mmx150mmx150mm cubes were tested for compressive strength of concrete. 4. Prediction Modelling approach Some of the empirical equations used for the estimation of compressive strength of concrete were reviewed. 147 The following is the scenario analysis that is applied to determine the effect of the response variable (f ck ) = compressive strength of the predictor variable/explanatory/independent for more than 1 variable using multiple linear regression. In this case the compressive strength (y = response variable) is influenced by five variables:, cement and silica fume, fine aggregate and bottom ash, coarse aggregate and steel slag aggregate, weight of the cube on the strength of concrete. The constant parameters are w/c and curing days are 0.45 and 28 days respectively. 5. Results and Discussions In this study performances of previously suggested single and multi variable equations used for the estimation of compressive strength of concrete incorporated with silica fume, bottom ash and steel slag test results were compared. Coefficient of determination R 2 is defined as the proportion of the total variation in Y explained by the regression of Y on X. The coefficient of determination ranges from 0 to 1 and also it can be interpreted as the fraction of uncertainly explained by the fitted model. Normal R 2 is a widely good of fit measure however in sometimes it doesn t have its usual meaning for non linear curves. The performance of the statistical model to predict the compressive strength of concrete using variable inputs in the mixture matrix performed well and confirmed by the testing the statistical values with the coefficient of determination Silica Fume Concrete The comparison between compressive strength observed experimentally and the one obtained using predictive tool developed in the present investigation for the trial mixes for 28 days curing periods is indicated in figure 1. From the figure 1 the values mentioned in Table 2 indicating the actual and predicted strength of the trial mixes in respect of 28 days curing days, it is observed that the regression equations derived in this study give fairly better results. The predicted strength is found to overestimate the actual strength in trial mixes. The predicted strength is found to overestimate the actual strength in 7 mixes by 1.18% on an average. However, in 8 mixes trials the predicted strength is seen to underestimate the actual strength by 1.16% on an average. Average ve difference: % (7) Average +ve difference: % (8) Coefficient of correlation (R 2 ): 0.612

3 Sl.No. Mix Cement Table 1: Mix proportions of concrete mixes Fine aggregate Coarse aggregate Silica fume Bottom Ash Steel slag aggregate SP (ml) Weight of the cube 1 CC SFC SFC SFC SFC BAC BAC BAC BAC BAC SSAC SSAC SSAC SSAC SSAC Table 2: Summary of actual & predicted strength for Silica fume concrete cubes Sl.No 1 Mix Name Cement Cement - Silica fume (C/SF) ratio Experimental Strength MPa Predicted Strength MPa % Error difference CC SFC SFC SFC SFC

4 Table 3: Summary of actual & predicted strength for Bottom ash concrete cubes Fine Sl.No aggregate - Experimental Predicted Mix Fine % Error bottom ash Strength Strength Name Aggregate difference (FA/BA) MPa MPa ratio CC BAC BAC BAC BAC BAC Sl.No Table 4: Summary of actual & predicted strength for Steel slag aggregate concrete cubes Mix Name Coarse Aggregate Coarse aggregate - steel slag aggregate (CA/SSA) ratio Experimental Strength MPa Predicted Strength MPa % Error difference CC SSAC SSAC SSAC SSAC SSAC

5 Predicted compressive strength (MPa) Predicted compressive strength (MPa) Predicted compressive strength (MPa) International Journal of Engineering Technology, Management and Applied Sciences 40 SILICA FUME CONCRETE seen to underestimate the actual strength by 1.25 % on an average Actual Compressive Strength (MPa) Figure 1. Comparison between actual and predicted strength for Silica fume concrete at 28 days curing period 5.2 Bottom Ash Concrete From figure 2 and the values mentioned in Table 3 indicating the actual and predicted strength of the trial mixes, it is observed that the regression equations derived given better results. The predicted strength is found to overestimate the actual strength in 8 trial mixes on 1.7 % on an average. However, in remaining 10 trial mixes the predicted strength is seen to underestimate the actual strength by 1.3 % on an average. Average ve difference: % (8) Average +ve difference: % (10) Coefficient of correlation (R 2 ): BOTTOM ASH CONCRETE Actual Compressive Strength (MPa) Figure 2. Comparison between actual and predicted strength for Bottom ash concrete at 28 days curing period. 4.3 Steel slag aggregate Concrete From figure 3 and the values mentioned in Table 4 indicating the actual and predicted strength of the trial mixes, it is observed that the regression equations derived given better results. The predicted strength is found to overestimate the actual strength in 8 trial mixes on 1.56 % on an average. However, in remaining 10 trial mixes the predicted strength is STEEL SLAG AGGREGATE CONCRETE Actual Compressive Strength (MPa) Figure 3. Comparison between actual and predicted strength for steel slag concrete at 28 days curing period Average ve difference: % (8) Average +ve difference: % (10) Coefficient of correlation (R 2 ): Conclusions This paper concluded that statistical modelling is applied on the task of finding concrete compressive strength data set and the most accurate learning method is evaluated. With high correlation coefficient is found to work well for this matrix mixture for finding compressive strength. Finally the proposed statistical modelling method is capable of predict the effect of the mixture matrix to produce the required concrete compressive strength. 1. The statistical models based on the multiple linear regression analyzes were developed to predict the compressive strength for the various combinations of the materials considered in the present investigations. 2. The correlations were established between the compressive strength values derived from compressive strength, the prediction of concrete strength value appears more reliable. 3. The value of coefficient of correlation for coarse aggregate and steel slag is observed above 0.8 for 28 days curing period giving better agreement between the materials. For silica fume concrete and bottom ash concrete, the values of coefficient of correlation are 0.78 and The values of actual strength are found in the increasing order in respect of Silica fume concrete, Bottom ash concrete and steel slag aggregate concrete for 28 days curing period considered in the present investigation. 150

6 5. The utilization of silica fume for cement, bottom ash for fine aggregate, steel slag aggregate for coarse aggregate reveals higher strength than the conventional concrete. REFERENCES [1] Mahmoud Sayed Ahmed, Statisitical Modelling and Prediction of compressive strength of concrete, Concrete Research Letters, Vol. 3 (2) June [2] Deepa. C, SathiyaKumar. K, Prem Sudha. V, Prediction of Compressive strength of High performance concrete mix using Tree Based modelling, International Journal of Computere Applications, Vol. 6, No. 05, 2010, pp 18 to 24. [3] B.B. Patil and P.D. Kumbhar, Strength and Durability Properties of High performance concrete incorporating high reactivity metakaolin, International Journal of Modern Engineering Research, Vol. 2, No. 3, 2012, pp 1099 to [4] P.S. Kumar, M. A. Mannan and K.V. John, High performance reinforced concrete beams made with sand stone reactive aggregates, The Open civil Engineering Journal, Vol. 36, No. 5, 2008, pp. 41 to 50. [5] A. Talah, F. Kharchi & R. Chaid, Influence of Marble powder on High performance concrete behaviour, 1 st International conference on Structural Integrity, Institute of Science and Innovation in Mechanical and Industrial Engineering, Science direct journal, /j.proeng [6] K. Pazhani and R. Jeyaraj, Study on Durability of High performance concrete with Industrial wastes, Applied Technologies & Innovations, Vol. 2, No. 10, 2010, pp to [7] K.S. Al.Jabri, M. Hisada, S.K. Al.Oraimi and A.H. Al.Saidy, Copper slag as Sand replacement for High performance concrete, Cement and concrete composites, Vol. 31, No. 7, 2009, pp.484 to 488. [8] S.W. Yoo, S.J. Kwon and S.H. Jung, Analysis Technique for Autogenous shrinkage in High performance concrete with Mineral and chemical admixtures, Construction and Building materials, Vol. 34, 2012, pp. 1 to 10. Doi: /j.conbuildmat [9] M. Erdal, Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression techniques, Scientific Research and Essay, vol. 4(10), pp , October [10] Samia Hannachi, Mohamed Nacer Guetteche, Application of the combined method for evaluating the compressive strength of concrete on site, Open journal of Civil Engineering, Vol. 2, 2012, pp. 16 to 21. [11] Vijay Pal Sing, Yogesh Chandra Kotiyal, Prediction of Compressive strength using Artificial Neural Network, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, Vol. 7, No. 12, 2013, pp [12] IS 8112: Grade OPC specifications. [13] IS 383: 1970 Specification for coarse and fine aggregate from natural sources for concrete [14] IS 2386 (Part -3): 1963 Methods of test for aggregate for concrete [15] IS 10262: 2009 Recommended Guidelines for concrete mix design APPENDIX Formulae for compressive strength For Silica fume concrete: 28 days strength with 5 variables f ck = (C) (C/SF) (SP) (Wt.) Whereas C = cement in kg/m 3, C/SF = ratio between cement and silica fume, SP = superplasticizer in ml, W = Weight of the cube in kg. For Bottom ash concrete: 28 days strength with 5 variables f ck = (FA) (FA/BA) (SP) (W) Whereas FA = fine aggregate in kg/m 3, FA/BA = ratio between fine aggregate bottom ash, SP = superplasticizer in ml, W = Weight of the cube in kg. For Steel slag aggregate concrete: 28 days strength with 5 variables f ck = (CA) (CA/SSA) (SP) (W) Whereas CA = coarse aggregate in kg/m 3, CA/SSA = ratio between coarse aggregate and steel slag aggregate, SP = superplasticizer in ml, W = Weight of the cube in kg.

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