Remote Structure Parameters Estimation on NetSlab Platform Using Response Time Series

A platform called Networked Structural Laboratories (NetSLab) which was originally developed for remote pseudo-dynamic test of large-scale structures and structural elements provides a relatively easier and friendlier interface for data transfer among remote monitoring stations or computers which are geographically distributed and connected by Internet for remote structural health monitoring and identification. In this study, a remote structural parameters identification strategy based on the updated NetSlab platform and the neural networks based soft identification algorithm with the direct use of structural dynamic response time series is proposed and the performance of the proposed remote identification methodology was validated for a multi-degree of freedom structural model. Results show that the updated NetSlab provides a reliable remote monitoring data transfer platform and because of the parallel computation performance of the neural networks based identification algorithm, the proposed soft identification algorithm provides a potential way for near real-time remote parameter identification for structural health monitoring.