Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of compressive strength of concrete by neural networks
In this paper, a method to predict 28-day compressive strength of concrete by using multi-layer feed-forward neural networks (MFNNs) was proposed based on the inadequacy of present methods dealing with multiple variable and nonlinear problems. A MFNN model was built to implement the complex nonlinear relationship between the inputs (many factors that influence concrete strength) and the output (concrete strength). The neural network (NN) models give high prediction accuracy, and the research results conform to some rules of mix proportion of concrete. These demonstrate that using NNs to predict concrete strength is practical and beneficial.
Prediction of compressive strength of concrete by neural networks
In this paper, a method to predict 28-day compressive strength of concrete by using multi-layer feed-forward neural networks (MFNNs) was proposed based on the inadequacy of present methods dealing with multiple variable and nonlinear problems. A MFNN model was built to implement the complex nonlinear relationship between the inputs (many factors that influence concrete strength) and the output (concrete strength). The neural network (NN) models give high prediction accuracy, and the research results conform to some rules of mix proportion of concrete. These demonstrate that using NNs to predict concrete strength is practical and beneficial.
Prediction of compressive strength of concrete by neural networks
Ni, Hong-Guang (Autor:in) / Wang, Ji-Zong (Autor:in)
Cement and Concrete Research ; 30 ; 1245-1250
2000
6 Seiten, 7 Quellen
Aufsatz (Zeitschrift)
Englisch
Prediction of compressive strength of concrete by neural networks
Online Contents | 2000
|Prediction of concrete compressive strength through artificial neural networks
DOAJ | 2020
|Prediction of compressive strength of concrete by neural networks
British Library Online Contents | 2000
|Neural prediction of concrete compressive strength
British Library Online Contents | 2018
|