Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid - Reni Suryanita; Mardiyono Mardiyono; Azlan Adnan (TELKOMNIKA (Telecommunication Computing Electronics and Control), 2017)
The natural disaster and design mistake can damage the bridge structure. The damage caused a severe safety problem to human. The study aims to develop the intelligent system for bridge health monitoring due to earthquake load. The Genetic Algorithm method in Neuro-Genetic hybrid has applied to optimize the acceptable Neural Network weight. The acceleration, displacement and time history of the bridge structural responses are used as the input, while the output is the damage level of the bridge. The system displays the alert warning of decks based on result prediction of Neural Network analysis. The best-predicted rate for the training, testing and validation process is 0.986, 0.99, and 0.975 respectively. The result shows the damage level prediction is agreeable to the damage actual values. Therefore, this method in the bridge monitoring system can help the bridge authorities to predict the health condition of the bridge rapidly at any given time.