An Investigation on the Performance of Soft Computing Techniques for Point Displacement Modeling for Suspension Bridge Using GNSS Technique

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months or even years. This paper investigates the performance of several soft computing techniques for point displacement modeling using GNSS technique during the process of monitoring the structural deformation of suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the availability of a large amount of positions data generated from GNSS data positions for monitoring the deformation of such structure, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS positions and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability rho = 0.95, Delta = +/- 2 sigma. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of rho = 0.95, Delta = +/- 2 sigma when using 66.67% of all data, are more accurate and reliable than any other applied methods, and therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements.