콘크리트 타설현장의 외부영향요인을 고려한 로버스트 콘크리트 배합설계 및 강도예측 = Robust Concrete Mix Design and Concrete Strength Prediction Considering External Influence Factors During Concrete Placement
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콘크리트 타설현장의 외부영향요인을 고려한 로버스트 콘크리트 배합설계 및 강도예측 = Robust Concrete Mix Design and Concrete Strength Prediction Considering External Influence Factors During Concrete Placement' 의 주제별 논문영향력
논문영향력 요약
주제
토목 공학
강도예측
뉴럴 네트워크
로버스트
배합설계
외부영향요인
콘크리트
콘크리트배합설계
동일주제 총논문수
논문피인용 총횟수
주제별 논문영향력의 평균
288
0
0.0%
주제별 논문영향력
논문영향력
주제
주제별 논문수
주제별 피인용횟수
주제별 논문영향력
주제분류(KDC/DDC)
토목 공학
14
0
0.0%
주제어
강도예측
3
0
0.0%
뉴럴 네트워크
20
0
0.0%
로버스트
4
0
0.0%
배합설계
18
0
0.0%
외부영향요인
3
0
0.0%
콘크리트
222
0
0.0%
콘크리트배합설계
4
0
0.0%
계
288
0
0.0%
* 다른 주제어 보유 논문에서 피인용된 횟수
0
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콘크리트 타설현장의 외부영향요인을 고려한 로버스트 콘크리트 배합설계 및 강도예측 = Robust Concrete Mix Design and Concrete Strength Prediction Considering External Influence Factors During Concrete Placement' 의 참고문헌
Yoyok, S. H. and Sabarudin, B.M. (2014), Taguchi Experiment Design for Investigation of Freshened Properties of Self-Compacting Concrete, American Journal of Engineering and Applied Sciences, 3(2), pp.300~306.
Yeo, W.K., Seo, Y.M., Lee, S.Y. and Jee, H.K. (2010), Study on Water Stage Prediction Using Hybrid Model of Artificial Neural Network and Genetic Algorithm, Journal of Korea Water Resources Association, 43(8), pp.721~731
Taguchi, G. (1986), Introduction to quality engineering, Asian Productivity Organization, Tokyo.
Shin, S. Y., Lee, Y. J. and Kim, Y. S. (2014), A study on the mix design model of 60MPa class high-strength concrete using neural network, Journal of Architectural Institute of Korea, 16(3), pp.169~176.
On, J.H., Lee, S.K. and Kim, Y.S. (2013), A study on optimum mix design model of 60MPa high strength concrete using neural network theory, Proceedings of Architectural Institute of Korea, pp.509~510.
Oh, J. W., Lee, J. H. and Lee, I. W. (1997), Use of Neural Networks on Concrete Mix Design, Korea Concrete Institute, 9(2), pp. 145~151.
Nuruddin, M.F. and Bayuaji, R. (2009), Application of Taguchi’s approach in the optimization of mix proportion for Microwave Incinerated Rice Husk Ash Foamed Concrete, International Journal of Civil & Environmental Engineering, 9(9), pp.121~129.
Ministry of Land, Infrastructure and Transportation (2009), Concrete Standard Specification.
Mathworks (2017), Matlab R2017b Documentation < h t t p s : / / w w w . m a t h w o r k s . c o m / h e l p / s l c o v e r a g e / release-notes-R2017b.html>
Lhee, S. C., Flood, I. and Issa, R. R. (2014). Development of a Two-Step Neural Network-Based Model to Predict Construction Cost Contingency, Journal of Information Technology in Construction, 19, pp. 399-411.
Lee, S. S., Won, C., Park, S. J . and Kim, D. S. (2001), A study on the Mix Design and the Control of thermal Crack of Mass Concrete, Proceedings of Korea Concrete Institute, 13(1), pp. 533~538.
Lee, S. C., Feng, M. Q. and Kwon, S. J. (2010). Concrete Mixture Design for RC Structures under Carbonation - Application of Genetic Algorithm Technique to Mixture Conditions, Journal of the Korea Concrete Institute, 22(3), pp. 335~343.
Lee, S. C., Feng, Feng, M. Q. and Kwon, S. J. (2010), Concrete Mixture Design for RC Structures under Carbonation – Application of Genetic Algorithm Technique to Mixture Conditions, Journal of the Korea Concrete Institute, 22(3), pp. 335~343.
Kwon, S. J., Lee, S. Ch (2016), Study on Optimum Mixture Design for Service Life of RC Structure subjected to Chloride Attack - Genetic Algorithm Application, Journal of Korean Society of Civil Engineers, 30(5), pp. 433-442.
Kim, Y. C., Yoo, W. S. and Shin, Y. S. (2017), Application of Artificial Neural Networks to Prediction of Construction Safety Accidents, Journal of the Korean Society of Hazard Mitigation, 17(1), pp.7~14.
Kim, S. K., Hong, Y. H., park, J. W. and Yun, K. K. (2012), Optimized concrete mixture for airport pavement considered optimized aggregate gradation, Proceedings of Korea Concrete Institute, pp.811~812.
Kim, R.H., Bang, J.S., Kim, Y.R., Song, Y.C., Lee, T.G., and Choi, S.W. (2017), Performance Evaluation according to Mixing of Concrete, Proceedings of Korea Concrete Institute, pp.469~470.
Kim, J. H., Oh, I. S., Phan, D. H. and Lee, K. S. (2010), Application of Performance Based Mixture Design (PBMD) for High Strength Concrete, Journal of the Korean Society of Civil Engineering, 30(6A), pp. 561~571.
Kim, G. Y. (2018), Concrete Mix Design for Super High-rise Buildings Construction, Master’s Thesis, Pusan National University, Pusan, Korea
Kim, D.K., Lee, J.J., Chang, S.K. and Lim, B.Y. (2003), Prediction of compressive strength of concrete using probabilistic neural network, Proceedings of Korean Society of Civil Engineers, pp.1451~1454.
Ismaeel, A. G., Mikhail, D. Y. (2016), Effective data mining technique for classification cancers via mutations in gene using neural network, International Journal of Advanced Computer Science and Applications (IJACSA), 7(7), pp.69~76.
Hwang, S. D., Yoon, A. S. and Kim, B. I. (2004), Improvement of Marshall Mix Design and Comparative Evaluation with current Marshall Mix Design Method, Journal of Korean Society of Road Engineers, 6(4), pp. 13-24.
Han, C. K. (1998). Concrete characteristics and mix design for quality control engineers, Kimoondang
Doh, Y. S., Kwon, O. S., Kim, J. Y. and Kim, K. W. (2004). Volumetric Property Difference in Mix Design Results by Superpave and Marshall Method, Journal of Korean Society of Road Engineers, 6(4), pp. 65-73.
Boussabaine, A.H. (1996), The use of artificial neural networks in construction management: a review, Journal Construction Management and Economics, 14(5), pp.427~436.
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콘크리트 타설현장의 외부영향요인을 고려한 로버스트 콘크리트 배합설계 및 강도예측 = Robust Concrete Mix Design and Concrete Strength Prediction Considering External Influence Factors During Concrete Placement'
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