0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Modeling and Optimization for The Tensile Properties of 3D-Printed FRP using Artificial Neural Network and Artificial Bee Colony Algorithm

 Modeling and Optimization for The Tensile Properties of 3D-Printed FRP using Artificial Neural Network and Artificial Bee Colony Algorithm
Auteur(s): , , ,
Présenté pendant IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, publié dans , pp. 1119-1128
DOI: 10.2749/nanjing.2022.1119
Prix: € 25,00 incl. TVA pour document PDF  
AJOUTER AU PANIER
Télécharger l'aperçu (fichier PDF) 0.15 MB

Fiber-reinforced polymer (FRP) has multiple applications as a primary material or reinforcing material for the structural elements. Controlling the quality of the 3D printed FRP is critical to guar...
Lire plus

Détails bibliographiques

Auteur(s): (Department of Structural Engineering, Tongji University, Shanghai 200092, China.)
(Department of Structural Engineering, Tongji University, Shanghai 200092, China.)
(School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China.)
(Department of Bridge Engineering, Tongji University, Shanghai 200092, China.)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Publié dans:
Page(s): 1119-1128 Nombre total de pages (du PDF): 10
Page(s): 1119-1128
Nombre total de pages (du PDF): 10
DOI: 10.2749/nanjing.2022.1119
Abstrait:

Fiber-reinforced polymer (FRP) has multiple applications as a primary material or reinforcing material for the structural elements. Controlling the quality of the 3D printed FRP is critical to guarantee a FRP material of high performance. In this research, machine learning (ML) model based on data collected from experimental studies was developed by artificial neural network (ANN) to control the quality of 3D printed FRP. ANN model predicts the ultimate tensile strength (UTS) of the FRP as function of 7 material and printing parameters. The UTS of the FRP was maximized via optimizing the printing and material parameters by using artificial bee colony (ABC) algorithm. ANN and ABC algorithms were coded by MATLAB. The results showed that the developed ANN model can predict with good accuracy the UTS of FRP. Moreover, it was found that the ABC optimization algorithm can design the input parameters such that a FRP with maximum UTS can be obtained.

Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
License:

Cette oeuvre ne peut être utilisée sans la permission de l'auteur ou détenteur des droits.