Validation of an Identification Methodology for a Model Frame Structure on Shaking Table Using Laser Displacement Sensing

Abstract eng:
For the purpose of health monitoring, post-earthquake condition evaluation and safety appraisal of existing infrastructures, many structural parameter identification methodologies based on eigenvalue and/or mode shape extraction from structural vibration measurement have been proposed. In this study, a general structural parameter identification strategy based on neural networks is proposed and the theoretical base for the construction of a neural network emulator(NNE) and a parametric evaluation neural network(PENN) is explained. A two-story model frame structure on a shaking table is employed as an illustrative structure to validate the performance of the proposed approach for structural stiffness identification and damage detection using vibration displacement response measurement from laser displacement sensors. Results show that the NNE can forecast the displacement of the reference structure with high accuracy, and PENN can describe the mapping between an evaluation index and structural stiffness parameter. Compared with results that from traditional identification method based on frequencies extraction, the performance of the proposed methodology is validated. The proposed algorithm is a general and applicable way in practice for near real-time identification, damage detection and structural model updating.

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