1 |
Atcin, P.-C. (2011), High performance concrete, CRC Press, 203-211.
|
2 |
Baykasoglu, A., ztas, A., & zbay, E. (2009), Prediction and multiobjective optimization of high-strength concrete parameters via soft computing approaches. Expert Systems with Applications, 36(3), 6145-6155.
DOI
ScienceOn
|
3 |
Do, J., Kim, D. (2013), Effect of Substrate Surface Water on Adhesive Properties of High Flowable VA/VeoVa-modified Cement Mortar for Concrete Patching Material, Journal of Korea Institute of Safety Inspection, KSMI, 17(5), 94-104.(In Korean, with English abstract).
|
4 |
Duan, Z., Kou, S., & Poon, C. (2013), Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Construction and Building Materials, 40, 1200-1206.
DOI
ScienceOn
|
5 |
EFNARC. (2002), Specification and Guidelines for Self Compacting Concrete, DFNARC, UK, 23-25.
|
6 |
Fardis, M. N. (2012), Innovative Materials and Techniques in Concrete Construction, Springer, 45-49.
|
7 |
Ghafari, E., Costa, H., & Jlio, E. (2014), RSM-based model to predict the performance of self-compacting UHPC reinforced with hybrid steel micro-fibers, Construction and Building Materials, 66, 375-383.
DOI
ScienceOn
|
8 |
Khan, M. I. (2012), Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks, Automation in Construction, 22, 516-524.
DOI
ScienceOn
|
9 |
Ko, Y., Kim, C., Hwang, J., and Lee, S. (2015), Experimental Study on Lateral Pressure Characteristics of a Formwork for High-Flowable and High-Strength Concrete, Journal of Korea Institute of Safety Inspection, KSMI, 19(3), 130-138.(In Korean, with English abstract).
|
10 |
Montgomery, D. C. (2013), Design and analysis of experiments, John Wiley & Sons, 478-544.
|
11 |
Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009), Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons, 446-449.
|
12 |
Ozbay, E., Oztas, A., Baykasoglu, A., & Ozbebek, H. (2009), Investigating mix proportions of high strength self compacting concrete by using Taguchi method, Construction and Building Materials, 23(2), 694-702.
DOI
ScienceOn
|
13 |
Siddique, R., Aggarwal, P., & Aggarwal, Y. (2011), Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 42(10), 780-786.
DOI
ScienceOn
|
14 |
Yeh, I.-C. (1998), Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research, 28(12), 1797-1808.
DOI
ScienceOn
|