Direct substructural identification methodology using acceleration measurements with neural networks

Bin Xu; Ting Du

doi:10.1117/12.658886

16 March 2006 Direct substructural identification methodology using acceleration measurements with neural networks

Bin Xu, Ting Du

Proceedings Volume 6178, Nonintrusive Inspection, Structures Monitoring, and Smart Systems for Homeland Security; 617804 (2006) https://doi.org/10.1117/12.658886
Event: Nondestructive Evaluation for Health Monitoring and Diagnostics, 2006, San Diego, CA, United States

Abstract

A substructural identification methodology by the direct use of acceleration measurements with neural networks is proposed. The rationality of the substructural identification methodology employing a substructural acceleration-based emulator neural network (SAENN) and a substructural parametric evaluation neural network (SPENN) is explained. Based on the discrete time solution of the state space equation of the substructure, the theory basis for the construction of SAENN and SPENN is described. An evaluation index called root mean square of prediction difference vector (RMSPDV) corresponding to acceleration response is presented to evaluate the condition of object structure. The performance of the SAENN for acceleration forecasting and SPENN for parametric identification is examined by numerical simulations with a substructure of a 50-DOFs shear structure involving all stiffness and damping coefficient values unknown. Based on the trained SAENN and the PENN, the inter-storey stiffness and damping coefficients of the substructure are identified. Since the strategy does not require structural modes or frequencies extraction, it is computationally efficient, thus providing a possibly viable tool for structural identification and damage detection of large-scale infrastructures.

Citation Download Citation

Bin Xu and Ting Du "Direct substructural identification methodology using acceleration measurements with neural networks", Proc. SPIE 6178, Nonintrusive Inspection, Structures Monitoring, and Smart Systems for Homeland Security, 617804 (16 March 2006); https://doi.org/10.1117/12.658886

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