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A Study on the Optimum Mix Design Model of 100MPa Class Ultra High Strength Concrete using Neural Network  

Kim, Young-Soo (부산대학교 건축공학과)
Shin, Sang-Yeop (ING&ENG, 연구실)
Jeong, Euy-Chang (ING&ENG, 기획실)
Publication Information
Journal of the Regional Association of Architectural Institute of Korea / v.20, no.6, 2018 , pp. 17-23 More about this Journal
Abstract
The purpose of this study is to suggest 100MPa class ultra high strength concrete mix design model applying neural network theory, in order to minimize an effort wasted by trials and errors method until now. Mix design model was applied to each of the 70 data using binary binder, ternary binder and quaternary binder. Then being repeatedly applied to back-propagation algorithm in neural network model, optimized connection weight was gained. The completed mix design model was proved, by analyzing and comparing to value predicted from mix design model and value measured from actual compressive strength test. According to the results of this study, more accurate value could be gained through the mix design model, if error rate decreases with the test condition and environment. Also if content of water and binder, slump flow, and air content of concrete apply to mix design model, more accurate and resonable mix design could be gained.
Keywords
Neural Network Theory; Ultra High-Strength Concrete; Mix Design; Back-Propagation Algorithm;
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1 Moon, H.Y, Choi, Y.W. (1998). The Effect of Ground Granulated Blast-Furnace Slag on the Control of Temperature Rising in High Strength Concrete. Journal of the Korea Concrete Institute, 10(4), pp.195-204.
2 Lee, S.U. (2008). The Effect of High Strength Concrete by Admixture Types and Mixture Condition [master's thesis]. Dae Jeon University. Korea.
3 Bae, S.K. (2003). Compressive Strength of High-Strength Concrete for the Mix Proportion [master's thesis]. In Ha University. Korea.
4 Yoo, S.Y, Lee, S.L, Koo, J.S, Kang, S.H. (2010). High Strength Concrete Mix Design Program and Ultra High Strength Concrete Ready-mixed Concrete Manufacturing Technology Development. Journal of Korea Cement Association, 185(1), pp.28-35.
5 Kim, K.H., An, S.H., Cho, H.G. (2006). Comparison of the Accuracy between Cost Prediction Models based on Neural Network and Genetic Algorithm-Focused on Apartment Housing Project Cost. Journal of Architectural Institute of Korea, 22(3), pp.111-118.
6 Lee, Y.J. (2008). A study on the mix design model of 60MPa class high strength concrete using neural network theory [master's thesis]. Pusan National University. Korea.
7 Kim, S.Y., Ji, N.Y., Yoon, S.C. (2007). Compressive Strength of High-Strength Concrete using Fly Ash on the Basis of Statistical Analysis. Journal of Architectural Institute of Korea, 23(1), pp.113-121.
8 Kim, C.H. (2007). Study on the High-Strength of High Flowing Concrete using Fly Ash [master's thesis], Dong Yang University. Korea.
9 Ozbay, E., Ahmet, O., Adil, B., Hakan, O. (2009). Invetigating Mix Proportions of High Strength Self Compacting Concrete by using Taguchi Method. Construction and Building Materials, 3(2), pp.694-702.
10 Williams, P., Trefor, P. (1991). Predicting changes in construction cost indexes using neural networks. Journal of construction engineering and management, 117(4), pp. 606-625.   DOI