Pinciples of Using Integrating GPS and Neural Network in Surveying Displacement of Large Span Bridges

Category GIS & Remote sensing
Group GSI.IR
Location Geomatics conference 85
Holding Date 28 January 2008

Pinciples of Using Integrating GPS and Neural Network in Surveying Displacement of Large Span Bridges

[ Mohammad Hooshmandzadeh ] - Department of Civil Engineering , Islamic Azad University of Shushtar branch

abstract

Destructive natural catastrophes has been assumed to be increased. For this end, big engineering structures like suspension bridges, viaducts , tunnels and high building etc. have been subjected to continuously monitoring surveys. The technological development in precision point positioning systems together with no-human data transmission techniques without any atmospheric obligation have led to easily adapting such monitoring systems for the objects in question. Monitoring of engineering structures has become of importance particularly after the possibility of destructive natural catastrophes has been assumed to be increased. For this end, big engineering structures like suspension bridges, viaducts, tunnels and high building etc. have been subjected to continuously monitoring surveys. The technological developments in high precision point positioning systems together with no-human data transmission techniques without any atmospheric obligation have led to easily adapting such monitoring systems for the objects in question. In question . underlying processes are normally so complex to be expressedby one simple expression . The present study motivates the use of artificial neural networks for modeling the behaviours of deforming objects regarding the causing effects such as atmospheric , traffic volume . Artificial neural networks are inspired from biological systems in which large numbers of neurons, which individually perform rather slowly and imperfectly , collectively perform extraordinarily complex computations that even the fastest computers may not match. This new field of computing method is recently widely used by different disciplines such as prediction and control engineering, image processing and identification, pattern recognition , robotic systems etc. It is veryefficient tool for complex system identification in general


tags: etc