JKCI - Journal of the Korea Concrete Institute

1

Ahn, Y. H., and Kim, S. Y. (2017) Construction Industry Transition with the 4th Industrial Revolution Technology. Journal of the Korea Institute of Building Construction 17(2), 18-23. (In Korean)

2

Bengio. Y. A. (2013) Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1798-1828.

3

Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences (2nd ed.). New York, NY: Routledge.

4

Ferraris, C. F., Obla, K. H., and Hill, R. (2001) The Influence of Mineral Admixtures on the Rheology of Cement Paste and Concrete. Cement and Concrete Research 31(2), 245-255.

5

Francois, C. (2022) Deep Learning with Python. Seoul: Gilbut.

6

Garcia, S., Ramirez-Gallego, S., Luengo, J., Benitez, J. M., and Herrera, F. (2016) Big Data Preprocessing: Methods and Prospects. Big Data Analysis 1(1), 1-22.

7

Hinton, G. E., Osindero, S., and Teh, Y. W. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18(7), 1527-1554.

8

Humphrey, G. B., Gibbs, M. S., Dandy, G. C., and Maier, H. R. (2016) A Hybrid Approach to Monthly Streamflow Forecasting: Integrating Hydrological Model Outputs into a Bayesian Artificial Neural Network. Journal of Hydrology 540, 623-640.

9

Hyeon, C. (2007) A Useful Rheometer for Cement Paste in Field. Journal of the Architectural Institute of Korea Structure and Construction 23(6), 89-97. (In Korean)

10

Jeon, J. S., Kim, H. S., and Kim, C. H. (2021) Study on Prediction of Compressive Strength of Concrete based on Aggregate Shape Features and Artificial Neural Network. Journal of the Korea Institute for Structural Maintenance and Inspection 25(5), 135-140. (In Korean)

11

Jeong, D. H. (2020) A Study on Prediction of Concrete Carbonation Using Deep Learning. Master Thesis. Hanyang University.

12

Joo, D. S., Choi, D. J., and Park, H. (2000) The Effects of Data Preprocessing in the Determination of Coagulant Dosing Rate. Water Research 34(13), 3295-3302. (In Korean)

13

Jurgen, S. (2014) Deep Learning in Neural Networks: An Overview. Neural Networks 61, 85-117.

14

Kim, D. G. (2022) Linear Regression with Tensorflow. Magazine of the SAREK 51(10), 74-75. (In Korean)

15

Kim, I. S., Lee, J. H., Yang, D. S., and Park, S. K. (2002) Prediction on Mix Proportion Factor and Strength of Concrete Using Neural Network. Journal of the Korea Concrete Institute 14(4), 457-466. (In Korean)

16

Kim, J. H., and Shin, T. Y. (2018) Rheological Properties of Concrete Estimated by the Slump Flow Test. Proceedings of the Korea Concrete Institute 30(2), 401-402. (In Korean)

17

Kwon, S. H., Kim, Y. J., Lee, G. C., and Choi, Y. W. (2013) Measurements and Applications of Concrete Rheology. Magazine of the Korea Concrete Institute 25(3), 24-28. (In Korean)

18

Kwon, S. J., and Yoon, Y. S. (2022) The Prediction of Durability Performance for Chloride Ingress in Fly Ash Concrete by Artificial Neural Network Algorithm. Journal of the Korea Institute for Structural Maintenance and Inspection 26(5), 127-134. (In Korean)

19

Lee, H. J., Kim, W. W., and Moon, J. H. (2018) Study on Rhoelogical Properties of Mortar for the Application of 3D Printing Method. Journal of the Korean Recycled Construction Resources Institute 6(1), 16-24. (In Korean)

20

Lee, S. J., (2019) A Fundamental Study on Development the Predictive System for Compressive Strength of Concrete Based on Deep Learning Algorithms. Master Thesis. Hanyang University.

21

Lee, Y. J., Lee, Y. J., and Han, D. Y. (2020) A Fundamental Research on Determining Segregation Boundary Using Rheological Parameters for 21 and 24MPa Grade of Normal Strength Concrete. Journal of the Korea Institute of Building Construction 20(5), 399-407. (In Korean)

22

Powers, T. C. (1968) The Properties of Fresh Concrete. New York: John Wiley and Sons.

23

Rouseel, N., and Roy, R. L. (2005) The Marsh Cone as a Viscometer : Theoretical Analysis and Practical Limits. Materials and Structures 38, 25-30

24

Roussel, N. (2006) Correlation between Yield Stress and Slump: Comparison between Numerical Simulations and Concrete Rheometers Results. Materials and Structures 39, 501-509.

25

Saak, A. W., Jennings, H. M. and Shah, S. P. (2003) A Generalized Approach for the Determination of Yield Stress by Slump and Slump Flow. Cement and Concrete Research 34(3), 363-371.

26

Samuel, A. L. (1959) Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development 3(3), 210-229.

27

Seo, I., Lee, H. S., Park, H. G., and Kim, W. J. (2009) An Experimental Study on Correlation between Rheological Parameter of Picked Mortar and Fluidity of Concrete from 30 to 150 MPa. Journal of the Architectural Institute of Korea Structure and Construction 25(9), 93-100. (In Korean)

28

Seong, N. C., and Hong, G. P. (2022) An Analysis of the Effect of the Data Preprocess on the Performance of Building Load Prediction Model Using Multilayer Neural Networks. Journal of the Korean Institute of Architectural Sustainable Environment and Building Systems 16(4), 273-284. (In Korean)

29

Shin, H. S., Kim, Y. J., and Park, C. S. (2017) Influence of Data Pre-Processing on a Machine Learning Model. Proceeding of Annual Conference of the Architectural Institute of Korea 37(1), 491-492. (In Korean)

30

Tattersall, G. H. (1991) Workability and Quality Control of Concrete. London: CRC Press.

31

Tattersall, G. H., and Banfill, P. F. G. (1983) The Rheology of Fresh Concrete. London: Pitman Books.

32

Um, S. J. (2017) 4th Industrial Revolution Technology and Construction Industry, and CM. Korean Institute Construction Engineering and Management 18(4), 3-7. (In Korean)

JKCIJournal of
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Journal of the Korea Concrete Institute

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JKCIJournal ofthe Korea Concrete Institute