Prediction and Evaluation of Hardened Concrete Strength
Based on Machine Learning and Mixture Composition
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Beschreibung
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 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
Autor*in
Yidong Xu
Themen in »Prediction and Evaluation of Hardened Concrete Strength«
Open Access F-P maturity model Particle Swarm Optimization Ant Colony Optimization Artificial Neural Network Intelligent Prediction Program