ACI Committee 211. 이름, 1993, Standard Practice for Selecting Proportions for Normal, Heavyweight, and Mass Concrete (ACI 211.1), Farmington Hills, MI; American Concrete Institute (ACI)

AIJ. 이름, 2003, Japanese Architectural Standard Specification for Reinforced Concrete Work (JASS 5), Architectural Institute of Japan (AIJ)

Chithra S., Senthil Kumar S.R.R., Chinnaraju K., Alfin Ashmita F., 2016, A Comparative Study on the Compressive Strength Prediction Models for High Performance Concrete Containing Nano Silica and Copper Slag Using Regression Analysis and Artificial Neural Networks, Construction and Building Materials, Vol. 114, No. 1, pp. 528-535

Cho H., Yoon S., Jee N.Y., 2003, The Estimation of Compressive Strength of Ready-Mixed Concrete On the Basis of Mix Design, Journal of the Architectural Institute of Korea - Structure & Construction, Vol. 19, No. 4, pp. 105-112

Choi H.-K., Bae B.-I., Koo H.-S., 2015, Correlation between Mix Proportion and Mechanical Characteristics of Steel Fiber Reinforced Concrete, Journal of the Korea Concrete Institute, Vol. 27, No. 4, pp. 331-341

Chun S.-C., Tak S.-Y., Lee S.-H., Sho K.-H., Ha T.-H., 2014, Evaluation of Construction Loads of Slabs and Shores with Removing Shores and Placing Reshores, Journal of the Korea Concrete Institute, Vol. 26, No. 3, pp. 385-392

Hong G.-H., Park H.-G., Eum T.-S., Mihn J.-S., Kim Y.-N., 2010, Evaluation of Strength and Stiffness Gain of Concrete at Early-Ages, Journal of the Korea Concrete Institute, Vol. 22, No. 2, pp. 237-245

KATS , 2017, Standard Test Method for Compressive Strength of Concrete (KS F 2405), Seoul, Korea: Korea Agency for Technology and Standards (KATS), Korea Standard Association (KSA). 1-12. (In Korean)

Kim I.-S., Lee J.-H., Yang D.-S., Park S.-K., 2002, Prediction on Mix Proportion Factor and Strength of Concrete Using Neural Network, Journal of the Korea Concrete Institute, Vol. 14, No. 4, pp. 457-466

Lee Y.-I., Kim B., Cho S., 2018, Image-Based Spalling Detection of Concrete Structures Using Deep Learning, Journal of the Korea Concrete Institute, Vol. 30, No. 1, pp. 91-99

MOLIT. 이름, 2022, Korean Construction Specification: General Concrete (KCS 14 20 10), Sejong, Korea: Ministry of Land, Infrastructure and Transport (MOLIT). (In Korean)

Nguyen K. T., Nguyen Q. D., Le T. A., Shin J., Lee K., 2020, Analyzing the Compressive Strength of Green Fly Ash Based Geopolymer Concrete Using Experiment and Machine Learning Approaches, Construction and Building Materials 2020, Vol. 247

Park J., Kim D., Shim S., Hong J., Choi H., Jin S.-S., 2021, Water-Cement Ratio Estimation of Cementitious Materials Using Electrochemical Impedance Spectroscopy and Machine Learning, Journal of the Korea Concrete Institute, Vol. 33, No. 4, pp. 353-361

Sobhani J., Najimi M., Pourkhorshidi A. R., Parhizkar T., 2010, Prediction of the Compressive Strength of No-Slump Concrete: A Comparative Study of Regression, Neural Network and ANFIS Model, Construction and Building Materials, Vol. 24, No. 5, pp. 709-718

Xu J., Zhou L., He G., Ji X., Dai Y., Dang Y., 2021, Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete, Materials, Vol. 14, No. 5, pp. 1068

Yeh I.-C., 1998, Modeling of Strength of High Performance Concrete Using Artificial Neural Networks, Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808

Yeh I.-C., 1999, Design of High Performance Concrete Mixture Using Neural Networks, Journal of Computing in Civil Engineering ASCE, Vol. 13, No. 1, pp. 36-42