Prediction and Evaluation of Hardened Concrete Strength Based on Machine Learning and Mixture Composition - Xu, Yidong; Mao, Jianghong; Zhuge, Weijie; Yu, Xiaoniu; Wu, Ping.; SpringerLink (Online service) (Singapore : Springer Nature Singapore : Imprint: Springer, 2026)
This open access book monitors the development of the temperature field within concrete structures and, based on the Arrhenius equation, constructs F-P maturity equations applicable to different temperature ranges. It investigates the impact of hydration rate on the strength prediction method of the maturity equation. Furthermore, the book employs artificial neural network theory to improve the accuracy of early concrete strength predictions, optimizing the neural network model to develop a more precise and widely applicable prediction model. An intelligent program is developed using MATLAB, facilitating rapid strength prediction and assessment on construction sites using measured parameters.
1. Introduction -- 2. Raw materials and experimental method -- 3. Establishment of maturity equations for different temperature intervals -- 4. Concrete strength prediction based on artificial neural networks -- 5. Development of intelligent concrete strength program -- 6. Conclusions and foresight -- Appendix.
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Keywords: Civil engineering; Materials; Civil Engineering; Materials Engineering