title: Damage identification for large span structure based on multiscale inputs to artificial neural networks.
abstract: In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach.
authors: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.
authors: Wei
authors: 0000-0001-8803-8430
authors: W
authors: Lu
authors: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.
authors: Jun
authors: J
authors: Teng
authors: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.
authors: Yan
authors: Y
authors: Cui
mesh_terms: D000465:Algorithms; D060389:Construction Industry / Q000379:methods; D003252:Construction Materials / Q000032:analysis*; D019544:Equipment Failure Analysis / Q000379:methods*; D005160:Facility Design and Construction; D008422:Materials Testing / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*