A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Durability prediction of glass/epoxy composite using artificial neural network
This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.
Durability prediction of glass/epoxy composite using artificial neural network
This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.
Durability prediction of glass/epoxy composite using artificial neural network
Hussain Idrisi, Amir (author) / Fatima, Kehkashan (author) / Hamid Ismail Mourad, Abdel (author)
2022-02-21
1153987 byte
Conference paper
Electronic Resource
English
First-ply failure prediction of glass/epoxy composite pipes using an artificial neural network model
| British Library Online Contents | 2018
First-ply failure prediction of glass/epoxy composite pipes using an artificial neural network model
| British Library Online Contents | 2018
Long-Term Durability of Carbon- and Glass-Epoxy Composite Materials in Wet Environments
| British Library Online Contents | 1997
Durability of self-healing woven glass fabric/epoxy composites
| British Library Online Contents | 2009