A MACHINE LEARNING BASED APPROACH FOR EFFICIENT SAFETY EVALUATION OF THE HIGH SPEED TRAIN AND SHORT SPAN BRIDGE SYSTEM

Abstract

THE DYNAMIC RESPONSES OF THE HIGH-SPEED RAILWAY BRIDGE UNDER THE TRAIN PASSAGE CAN GREATLY AFFECT THE SAFETY OF THE ENTIRE HIGH-SPEED TRAIN AND BRIDGE SYSTEM. TRADITIONALLY, THESE RESPONSES ARE OBTAINED USING EITHER FIELD MEASUREMENT OR NUMERICAL ANALYSIS. BOTH TOOLS HAVE THEIR OWN LIMITATIONS. FOR INSTANCE, THE COUPLING DYNAMIC TRAIN-BRIDGE ANALYSIS IS GENERALLY COMPLICATED AND TIME-CONSUMING. THIS PAPER PROPOSES A NOVEL MACHINE LEARNING BASED APPROACH FOR EFFICIENT SAFETY EVALUATION OF THE HIGH SPEED TRAIN AND SHORT SPAN BRIDGE SYSTEM. ARTIFICIAL NEURAL NETWORKS ARE ESTABLISHED TO MAP THE COMPLICATED TRAIN-BRIDGE SYSTEM AND TO ATTAIN THE CRITICAL BRIDGE DISPLACEMENTS. THE PROPOSED APPROACH INCORPORATES A COMPLETE NUMERICAL TRAIN-BRIDGE SYSTEM MODEL TO PRODUCE RELIABLE DATA FOR THE NEURAL NETWORK TRAINING, CONSIDERING MULTIPLE SIGNIFICANT RANDOM FEATURES IN THE TRAIN-BRIDGE SYSTEM. VARIOUS NEURAL NETWORK ARCHITECTURES ARE INVESTIGATED AND COMPARED TO FIND OPTIMAL ONES THAT HAVE CONSIDERABLE POTENTIALS IN REALIZING ONLINE RESPONSE PREDICTION AND SAFETY EVALUATION. ALTHOUGH THE PROPOSED APPROACH FOCUSES ON THE HIGH-SPEED TRAIN AND SHORT SPAN BRIDGE, THE METHODOLOGY IS GENERAL AND CAN ALSO BE APPLIED TO OTHER SCENARIOS ASSOCIATED WITH THE VEHICLE-BRIDGE SYSTEMS.