Acknowledgement
이 논문은 충북대학교 국립대학육성사업(2022) 지원을 받아 작성되었음.
References
- Basha, S. M. and Rajput, D. S. (2019). Survey on evaluating the performance of machine learning algorithms: past contributions and future roadmap. In Deep Learning and Parallel Computing Environment for Bioengineering Systems, 153-164.
- Schmidt, J., Marques, M. R., Botti, S., and Marques, M. A. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5(1), 1-36.
- Xu, Y., Zhou, Y., Sekula, P., and Ding, L. (2021). Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6, 100045.
- Chou, J. S., Tsai, C. F., Pham, A. D., and Lu, Y. H. (2014). Machine learning in concrete strength simulations: Multination data analytics. Construction and Building materials, 73, 771-780. https://doi.org/10.1016/j.conbuildmat.2014.09.054
- Bang, J., Park, S., and Jeon, H. (2022). Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches. Computers Materials and Contitua, 71(1), 1503-1519. https://doi.org/10.32604/cmc.2022.020485
- Kim, H. K., Lim, Y., Tafesse, M., Kim, G. M., and Yang, B. (2022). Micromechanics-integrated machine learning approaches to predict the mechanical behaviors of concrete containing crushed clay brick aggregates. Construction and Building Materials, 317, 125840.
- Degefa A. B., Yang, B., Park, S. (2022). Predicting the degree of reaction of supplementary cementitious materials in hydrated portland cemenet, under review.
- Kim, H. K., Park, I. S. and Lee, H. K. (2014). Improved piezoresistive sensitivity and stability of CNT/cement mortar composites with low water-binder ratio. Composite Structures, 116, 713-719.
- Cormos, C. C. (2022). Decarbonization options for cement production process: A techno-economic and environmental evaluation. Fuel, 320, 123907.
- Bullard, J. W., Garboczi, E. J., Stutzman, P. E., Feng, P., Brand, A. S., Perry, L., Hagedorn, J., Griffin W., and Terrill, J. E. (2019). Measurement and modeling needs for microstructure and reactivity of next-generation concrete binders. Cement and Concrete Composites, 101, 24-31. https://doi.org/10.1016/j.cemconcomp.2017.06.012
- Searson, D. P., Leahy, D. E., and Willis, M. J. (2011). Predicting the toxicity of chemical compounds using GPTIPS: a free genetic programming toolbox for MATLAB. Intelligent Control and Computer Engineering (pp. 83-93). Springer.