PREDIKSI NILAI RATING FACTOR JEMBATAN KOMPOSIT BAJA-BETON DENGAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK - , NURBETHA HIJRAH R; , Dr. Ir. H. Muslikh, M.Sc., M.Phil ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Generally, composite bridge used composite structure that was made from structural steel and reinforced concrete. Composite structure of steel beam and concrete slab was an inovation to get a good and eficient construction. This system had advantages like reduction in the weight of steel, shallower steel beam, increased floor stiffness and increased span length for a given number. Composite bridge should be evaluated to control last performance of the bridge, so the bridge could be checked whether it is adequate the limit requirements or not. One of method to evaluate the bridge is using rating factor. Nowdays, analysis method to calculate rating factor needs a long time to complete. So there was a requirement of a new method to predict rating factor quickly. This study used a method using Artificial Neural Network to help rating factor calculation of a bridge with typical section. So the process will be more effective and efficient. This study used Merangin Bridgeâs section as a case study. Merangin Bridge which located at Jambi Province was a composite bridge. Rating factor analysis is based on Pedoman Penentuan Nilai Sisa Kapasitas Jembatan from Dirjen Bina Marga to analyse the (operating) daily conditions and (inventory) special conditions. The study results four empirical equations which came from Artificial Neural Network. These empirical equations was used to predict operating rating factor of shear strength, operating rating factor of bending strength, inventory rating factor of shear strength and inventory rating factor of bending strength using average corrosion penetration, span of bridge, concrete compressive strength and yield stress of steel girder. The study showed that the final conclusion about bridge safety decision which came from empirical equations and theoretical calculation was still same. So the empirical equations can be used to predict rating factor of a brige quickly.