GPS monitoring of the Fatih Sultan Mehmet suspension bridge by using assessment methods of neural networks

The second suspension bridge connecting the continents Asia and Europe, namely, Fatih Sultan Mehmet Bridge, has been monitored by using GPS technique. For this end permanent GPS observations with 0.1 seconds epoch interval were recorded for the same days of consecutive weeks. In addition to GPS observations, some other data belong to influencing factors such as traffic volume and weather conditions for the corresponding observation time were collected. At first step the time series of the respective point component displacements (deformations) were composed and linked to the data such as time, traffic volume and weather conditions. Then a detailed comparison of the individual observation days was investigated. Further on, an artificial neural network, from the family of soft computing methods is adapted in order to describe the deformation processes with respect to influencing factors. Such studies have been of special interest after the 17 August Earthquake in North Anatolian Fault Zone (NAFZ) since new earthquakes are expected. Therefore, monitoring of big engineering structures like bridges will bring important information for disaster management and risk analysis. The results present that artificial neural networks are efficient tools for modelling complex behaviours of deforming objects regarding the causing factors especially in case of continuous monitoring systems.