Prediction and Evaluation of Hardened Concrete Strength

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.

This book is open access, which means that you have free and unlimited access Codes the F-P maturity model to improve the efficiency of the prediction model Optimizes the BP artificial neural network using PSO and ACO algorithms Combines the F-P maturity model with the PSO-BP ANN model to fit the nonlinear relationship

Autorentext

Professor Yidong Xu is currently serving as Director of the Institute for Coastal Engineering Structure and Materials at NingboTech University China. He is a member of the DCS technical committee of RILEM and technical committee member of the Chinese Ceramic Society and the Chinese Civil Engineering Society. Professor Xu's research focuses on sustainable concrete materials and structures, as well as construction engineering and management. As a principle investigator, he has completed 5 research projects funded by the China National Science Foundation and the Zhejiang Provincial Science Foundation, as well as 6 research projects supported by Ningbo Municipal Science Bureau, and over 30 consulting projects sponsored by industry. Professor Xu has published over 100 journal articles and he serves as a peer reviewer for 75 academic journals.

Inhalt

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.