Cost Effective Computer Vision Based Structural Health Monitoring using Adaptive LMS Filters

摘要

Structural health monitoring (SHM) algorithms based on Adaptive Least MeanSquares (LMS) filtering theory can directly identify time-varying changes instructural stiffness in real time in a computationally efficient fashion. However, thebest metrics of seismic structural damage are related to permanent and plasticdeformations. The recent work done by the authors uses LMS-based SHM methodswith a baseline non-linear Bouc-Wen structural model to directly identify changesin stiffness (modelling or construction error), as well as plastic or permanentdeflections, in real-time. The algorithm validated, in silico, on a non-linear sheartypeconcrete structure using noise-free simulation-derived structural responses.In this paper, efficiency of the proposed SHM algorithm in identifying stiffnesschanges and plastic/permanent deflections under different ground motions isassessed using a suite of 20 different ground acceleration records. The results showthat even with a fixed filter tuning parameters, the proposed LMS SHM algorithmidentifies stiffness changes to within 10% of true value in 2.0 seconds. Permanentdeflection is identified to within 14% of the actual as-modelled value using noisefreesimulation-derived structural responses.Accuracy of the proposed SHM algorithm mainly relies on providing high-speedstructural responses. However, due to a variety of practical constraints, direct highfrequency measurement of displacement and velocity is not typically possible. Thisstudy explores the idea that emerging high speed line scan cameras can offer arobust and high speed displacement measure required for the modified LMS-basedSHM algorithm proposed for non-linear yielding structures undergoing seismicexcitation, and can be used for more precise estimation of the velocity usingmeasured acceleration and displacement data. The displacement measurementmethod is tested to capture displacements of a computer-controlled cart under 20 different displacement records. The method is capable of capturing displacementsof the cart with less than 2.2% error.