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Journal of the Korea Concrete Institute

ISO Journal TitleJ Korea Concr Inst.
  • SCOPUS
  • KCI Accredited Journal

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  3. 2026-08 (Vol.38 No.4)
  4. 10.4334/JKCI.2026.38.4.561
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References

1 
Cho, S., and Kim, M. O. (2026) Development of a Tree-Based Machine Learning Model for Predicting the Compressive Strength of Accelerated Carbonation-Cured Cementitious Composites. Journal of the Korea Concrete Institute 38(1), 91-102. (In Korean) DOI
2 
Fan, C. L. (2025) Evaluation Model for Crack Detection with Deep Learning: Improved Confusion Matrix Based on Linear Features. Journal of Construction Engineering and Management 151(3), 04024210. DOI
3 
Gogineni, A., Panday, I. K., Kumar, P., and Paswan, R. K. (2024) Predicting Compressive Strength of Concrete with Fly Ash and Admixture Using XGBoost: A Comparative Study of Machine Learning Algorithms. Asian Journal of Civil Engineering 25(1), 685-698. DOI
4 
Guo, L., Li, Z., Tian, Q., Guo, L., and Wang, Q. (2023) Prediction of CSG Splitting Tensile Strength Based on XGBoost-RF Model. Materials Today Communications 34, 105350. DOI
5 
JDEC (2012) Engineering Manual for Design, Construction, and Quality Control of Trapezoidal CSG Dam. Japan Dam Engineering Center Tokyo, Japan: Japan Dam Engineering Center (JDEC). (In Japanese) Google Search
6 
KATS (2022) Test Method for Compressive Strength of Concrete (KS F 2405) Seoul, Korea: Korea Agency for Technology and Standards (KATS), Korea Standard Association (KSA). (In Korean) Google Search
7 
KATS (2025) Standard Test Method for Determining the Consistency of Fresh Concrete (Vebe Test Method) (KS F 2427) Seoul, Korea: Korea Agency for Technology and Standards (KATS), Korea Standard Association (KSA). (In Korean) Google Search
8 
Kim, K. Y., Jeon, J. S., and Kim, Y. S. (2006) Laboratory Mix Design of CSG Method. Journal of the Korean Geotechnical Society 22(5), 27-37. (In Korean) Google Search
9 
Lee, S. M., Sung, H. S., and Kang, T. H. K. (2022) Comparison of Performance for Predicting Compressive Strength of Concrete Using Machine Learning. Journal of the Korea Concrete Institute 34(5), 505-513. (In Korean) DOI
10 
Nguyen, H. D., Dao, N. D., and Shin, M. (2025) Capability of Machine Learning to Predict Seismic Damage States of Reinforced Concrete Wall Structures. Journal of Building Engineering 106, 112620. DOI
11 
Nguyen, M. H., Nguyen, T.-A., and Ly, H.-B. (2023) Ensemble XGBoost Schemes for Improved Compressive Strength Prediction of UHPC. Structures 57, 105062. DOI
12 
Sun, Z., Wang, X., Huang, H., Yang, Y., and Wu, Z. (2024) Predicting Compressive Strength of Fiber-Reinforced Coral Aggregate Concrete: Interpretable Optimized XGBoost Model and Experimental Validation. Structures 64, 106516. DOI
13 
Tian, Q., Gao, H., Guo, L., Li, Z., and Wang, Q. (2023) CSG Compressive Strength Prediction Based on LSTM and Interpretable Machine Learning. Reviews on Advanced Materials Science 62(1), 20230133. DOI