Compressive Strength Prediction of High-Performance Hydraulic Concrete Using a Novel Neural Network Based on the Memristor
Year: 2024
Author: Jun Lu, Lin Qiu, Yingjie Liang, Ji Lin
Advances in Applied Mathematics and Mechanics, Vol. 16 (2024), Iss. 4 : pp. 878–904
Abstract
This paper proposes the memristor-memristor (M-M) and the memristor-gradient descent (M-GD) neural networks based on the classical back propagation neural network. The presented models are employed to predict the compressive strength of high-performance hydraulic concrete (HPC), and are tested by well-fitting and accurate predictions with the experimental data. The developed algorithms are also evaluated through comparisons with the classical learning algorithms including the gradient descent method, the gradient descent with momentum, the gradient descent with adaptive learning rate, the elastic gradient descent, and the Levenberg-Marquardt algorithm. It is observed that the established M-GD generally outperforms the classical algorithms and M-M. The constructed M-M neural network has a quite high convergence speed, and the strength prediction error induced by it can roughly satisfy the demands in construction engineering. This work extends the nonlinear memristor to a brand-new field, and provides an effective methodology for forecasting the compressive strength prediction of HPC.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/aamm.OA-2022-0127
Advances in Applied Mathematics and Mechanics, Vol. 16 (2024), Iss. 4 : pp. 878–904
Published online: 2024-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 27
Keywords: Artificial neural network memristor high-performance concrete compressive strength.