박사

콘크리트 타설현장의 외부영향요인을 고려한 로버스트 콘크리트 배합설계 및 강도예측 = Robust Concrete Mix Design and Concrete Strength Prediction Considering External Influence Factors During Concrete Placement

최현욱 2019년
논문상세정보
' 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 선정 방법
논문영향력 요약
주제
  • 토목 공학
  • 강도예측
  • 뉴럴 네트워크
  • 로버스트
  • 배합설계
  • 외부영향요인
  • 콘크리트
  • 콘크리트배합설계
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
288 0

0.0%

' 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.