PREDIKSI PENURUNAN KAPASITAS STRUKTUR ATAS JEMBATAN RANGKA BAJA DENGAN METODE ARTIFICIAL NEURAL NETWORK

• Main Authors: , ANGGA TRISNA Y, , Akhmad Aminullah, S.T., M.T., Ph.D.
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2014
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/133625/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74346

Indonesia use Bridge Management System (BMS) methodfor bridge monitoring and inspection system. This method still need development in accuracy and objectivity. In this paper, a stell truss bridge upper structure capacity prediction method using Artificial Neural Network (ANN) has been proposed. Furthermpre, this method may be advanced development of BMS method ANN is a matematics modelling method for derivate an empirical equation to solve an unique process from several unique input and output. Empirical equation derivated from ANN has an high accuracy and proven by previous study. In this case, empirical equation has derivated from input which describe bridge capacity reduction factor and output which describe rating factor. Bridge capacity reduction factor that has been proposed were age of bridge, actual maximum load, actual yield stress, and element compactness. Study has implemented in three bridge as case study, there were Lubuk Jambi Bridge, Kampar Kanan Bridge, and Batang Nilau Bridge in Riau Province. The study result indicated that empirical equation derivated from ANN for Lubuk Jambi Bridge, Kampar Kanan Bridge, and Batang NilauBridge given good data consistency and maximum error smaller than 10%, so the empirical equation has been valid and accurate. Furthermore the empirical equation can be used to predict capacity reduction for each bridge.