References
- SCRIVENER, K.L. – JOHN, V.M. – GARTNER, E.M.: Eco-Efficient Cements: Potential Economically Viable Solution for a Low-CO2 Cement-Based Materials Industry. Cement and concrete Research, Vol. 114, 2018, pp. 2–6.
- LATIF, S.D.: Concrete Compressive Strength Prediction Modeling Utilizing Deep Learning Long Short-Term Memory Algorithm for a Sustainable Environment. Environmental Science and Pollution Research, Vol. 28, 2021, pp. 30294–30302.
- NAGROCKIENE, D. – PUNDIENĖ, I. – KICAITE, A.: The Effect of Cement Type and Plasticizer Addition on Concrete Properties. Construction and building materials, Vol. 45, 2013, pp. 324–331.
- BADISSI, A.B. – BEROUAL, A. – BENTALHA, M.: Coarse Aggregate Grading Effect on the Correlation Between Compressive and Flexural Strengths of Concrete. Civil and Environmental Engineering, Vol. 20, Iss. 2, 2024, pp. 1221–1245.
- BROOKS, J.J. – JOHARI, M.M. – MAZLOOM, M.: Effect of Admixtures on the Setting Times of High-Strength Concrete. Cement and concrete Composite, Vol. 22, 2000, pp. 293–301.
- KAREM, S.T. – AL-ASADI, A.K.: Impact of Various Types of Fibres on the Mechanical Properties of Lightweight Concrete. Civil and Environmental Engineering, Vol. 20, Iss. 2, 2024, pp. 1255–1266.
- SHI, X. – DING, D. – SONG, H. et al.: Machine Learning-Based Strength Prediction of Fly Ash-Based Geopolymer Concrete. Industrial Construction, Vol. 1, 2025, pp. 1–12, http://kns.cnki.net/kcms/detail/11.2068.TU.20250102.1011.003.html.
- GAO, W.: Compressive Strength Prediction of Recycled Aggregate Concrete Using Deep Learning. Concrete, Vol. 11, 2018, pp. 58–61, 70.
- ZHENG, L.: Performance and Compressive Strength Prediction Model of Concrete with Composite Mineral Admixtures. Ph.D. Thesis, Zhejiang University, Hangzhou, China, 2012.
- GUAN, M.: Water-Cement Ratio Determination of Fresh Concrete and Compressive Strength Prediction. Master’s Thesis, Qingdao University of Technology, Qingdao, China, 2021, DOI: 10.27263/d.cnki.gqudc.2021.000389.
- WU, X. – LIU, P. – CHEN, H. et al.: Random Forest-Based Compressive Strength Prediction of High-Performance Concrete. Concrete, Vol. 1, 2022, pp. 17–20.
- LUO, G. – HONG, C. – CHENG, Z. et al.: BP and GA-BP Neural Networks for Concrete Compressive Strength Prediction. Concrete, Vol. 3, 2023, pp. 37–41.
- CHOPRA, P. – SHARMA, R.K. – KUMAR, M. – CHOPRA, T.: Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete. Advances in Civil Engineering, Vol. 2018, 2018, Art. ID 5481705.
- ZHANG, R. – YAN, H. – ZHANG, J. et al.: Variation Law and Prediction Model of Concrete Compressive Strength at −5 °C. J. Journal of Shenyang Jianzhu University (Natural Science Edition), Vol. 38, 2022, pp. 323–330.
- ZHANG, J.S. – GUO, G.: Reweighted Robust Support Vector Regression Method. Journal of Computer Science, Vol. 28, 2005, pp. 117–177.
- ERDAL, H. – ERDAL, M. – SIMSEK, O. – ERDAL, H.I.: Prediction of Concrete Compressive Strength Using Non-Destructive Test Results. Computers and Concrete, Vol. 21, 2018, pp. 407–
- KASPERKIEWICZ, J. – RACZ, J. – DUBRAWSKI, A.: HPC Strength Prediction Using Artificial Neural Network. Journal of Computing in Civil Engineering, Vol. 9, 1995, pp. 279–284.
- XIE, H. – WU, M.: Pedagogical Exploration from Fully Connected to Convolutional Neural Networks. Fujian Computer, Vol. 36, 2020, DOI: 10.16707/j.cnki.fjpc.2020.07.040.
- LI, H.: Machine Learning-Based Compressive Strength Prediction Algorithm for Basalt Fiber Reinforced Concrete. Master’s Thesis, Anhui University, Hefei, China, 2023, DOI: 10.26917/d.cnki.ganhu.2023.000755.
- XU, J. – FENG, Q. – YANG, S.: Application of Fuzzy System Method in Non-Destructive Testing for Concrete Compressive Strength Prediction. Engineering Mechanics, Vol. 6, 2007, pp. 104–110.
- YUE, J. – ZHONG, H. – GU, L. et al.: Application of Bayesian Regularized Neural Network in Deep Excavation Deformation Prediction. Journal of Henan University (Natural Science Edition), Vol. 52, 2022, pp. 200–209, DOI: 10.15991/j.cnki. 411100.2022.02.004.
- CHEN, H. – LONG, W. – LI, X. et al.: BP Neural Network-Based Compressive Strength Prediction of Fly Ash Concrete. Building Structures, Vol. 51, 2021, pp. 1041–1045.
- NIU, C. – LI, S. – HU, J. et al.: Machine Learning Applications in Materials Informatics. Materials Review, Vol. 34, 2020, pp. 23100–23108.
- KANG, L. – MI, X. – WANG, H. et al.: Research Progress of Artificial Neural Networks in Materials Science. Mater. Materials Review, Vol. 34, 2020, pp. 21172–21179.
- ZHANG, X. – DAI, C. – LI, W. et al.: TPE-XGBoost Based Compressive Strength Prediction Model for Recycled Coarse Aggregate Concrete. Journal of Building Science and Engineering, Vol. 41, 2024, pp. 100–110, DOI: 10.19815/j.jace. 2022.10013.
- BKA, M.A. – NGAMKHANONG, C. – WU, Y. – KAEWUNRUEN, S.: Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks. Infrastructures, Vol. 6, Iss. 2, 2021, pp. 17.
- CHOU, J.S. – CHIU, C.K. – FARFOURA, M. – AL-TAHARWA, I.: Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques. Journal of Computing in Civil Engineering, Vol. 25, 2011, pp. 242–253.
- ZHOU, J.F. – ZENG, X.H. – XIE, Y.J. et al.: Compressive Strength Prediction of Concrete Based on HEMNG Model. Journal of Railway Science and Engineering, Vol. 22, Iss. 2, 2025, pp. 875–886, DOI: 10.19713/j.cnki. 43-1423/u. T20240592.
- AKBARI, M. – JAFARI DELIGANI, V.: Data Driven Models for Compressive Strength Prediction of Concrete at High Temperatures. Frontiers of Structural and Civil Engineering, Vol. 14, Iss. 2, 2020, pp. 311–321.
- DEROUSSEAU, M.A. – LAFTCHIEV, E. – KASPRZYK, J.R. – RAJAGOPALAN, B. – SRUBAR III, W.V.: A comparison of machine learning methods for predicting the compressive strength of field-placed concrete. Construction and Building Materials, Vol. 228, 2019, Art. ID 116661.
