Artificial Neural Networks and Phenomenological Degradation Models for Fatigue Damage Tracking and Life Prediction in Laser Induced Graphene Interlayered Fiberglass Composites

2021 ◽  
Author(s):  
Jalal Nasser ◽  
LoriAnne Groo ◽  
Henry A Sodano
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Damage ◽  
Life Prediction ◽  
Degradation Models ◽  
Damage Tracking ◽  
Artificial Neural

Applications of Artificial Neural Networks in Bridge Engineering

2011 ◽  
Vol 243-249 ◽  
pp. 1984-1987
Author(s):  
Xing Wei ◽  
Jun Li
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Prediction ◽  
Bridge Engineering ◽  
General View ◽  
Bridge Health Monitoring ◽  
Engineering Problems ◽  
Artificial Neural ◽  
Prediction Reliability ◽  
Model Slope

Artificial neural networks (ANNs) have been widely applied to many bridge engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in member capacity prediction, reliability analysis, optimal design of structural systems, fatigue life prediction, construction control, material constitutive model , slope stability, bridge health monitoring. The objective of this paper is to provide a general view of some ANNs applications for solving some types of bridge engineering problems. A brief introduction to ANNs is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in bridge engineering are presented.

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