Permanent Deflection Identification of Non-linear Structures Undergoing Seismic Excitation Using Adaptive LMS Filters
Structural Health Monitoring (SHM) algorithms based on Adaptive Least Mean Square (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real time, are robust to noise, and computationally efficient. Common modal or wavelet methods are less robust to noise and small levels of damage. However, the best metrics of seismic structural damage are related to permanent and plastic deformations, which no reported methods identify. This research uses LMS-based SHM methods with a baseline non-linear Bouc-Wen structural model to directly identify permanent deflection and changes in stiffness (modelling or construction error), in realtime. The algorithm is validated, in silico, on an equivalent single degree of freedom of a non-linear 5-storey shear-type concrete structure using MATLAB®. The Cape Mendocino ground motion is scaled to a level that causes permanent deflection to show the algorithm’s capability. For the simulated structure, the algorithm identifies stiffness changes to within 10% of true value in 2.0 seconds, and permanent deflection is identified to within 0.5% of the actual as-modelled value.