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.

Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

2020 ◽  
pp. 13-38
Author(s):  
Melda Yucel ◽  
Sinan Melih Nigdeli ◽  
Gebrail Bekdaş
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Properties ◽  
Structural Design ◽  
Civil Engineering ◽  
Back Propagation ◽  
Training Algorithm ◽  
Design Problems ◽  
Engineering Problems ◽  
Artificial Neural

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

Integrating knowledge-based systems and artificial neural networks for engineering

1995 ◽  
Vol 9 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Nabil Kartam ◽  
Ian Flood ◽  
Tanit Tongthong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Solution ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Engineering Problems ◽  
Scope Of Application ◽  
Artificial Neural ◽  
The Individual

AbstractThe feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

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