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

Structural health monitoring (SHM) algorithms based on Adaptive Least Mean Squares (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real time in a computationally efficient fashion. However, the best metrics of seismic structural damage are related to permanent and plastic deformations. The recent work done by the authors uses LMS-based SHM methods with a baseline non-linear Bouc-Wen structural model to directly identify changes in stiffness (modelling or construction error), as well as plastic or permanent deflections, in real-time. The algorithm validated, in silico, on a non-linear sheartype concrete structure using noise-free simulation-derived structural responses. In this paper, efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections under different ground motions is assessed using a suite of 20 different ground acceleration records. The results show that even with a fixed filter tuning parameters, the proposed LMS SHM algorithm identifies stiffness changes to within 10% of true value in 2.0 seconds. Permanent deflection is identified to within 14% of the actual as-modelled value using noisefree simulation-derived structural responses. Accuracy of the proposed SHM algorithm mainly relies on providing high-speed structural responses. However, due to a variety of practical constraints, direct high frequency measurement of displacement and velocity is not typically possible. This study explores the idea that emerging high speed line scan cameras can offer a robust and high speed displacement measure required for the modified LMS-based SHM algorithm proposed for non-linear yielding structures undergoing seismic excitation, and can be used for more precise estimation of the velocity using measured acceleration and displacement data. The displacement measurement method is tested to capture displacements of a computer-controlled cart under 20 different displacement records. The method is capable of capturing displacements of the cart with less than 2.2% error

Structural Health Monitoring using Adaptive LMS Filters

A structure's level of damage is determined using a real time that comes with significant computational cost and non-linear model-based method utilizing a Bouc-Wen hysteretic complexity. Moreover, like other linear approaches they are model. It employs adaptive least mean squares (LMS) filtering not applicable to the typical non-linearities found in seismic theory in real time to identify changes in stiffness due to modeling error damage, as well as permanent displacements, which are structural responses. critical to determining ongoing safety and use. The structural In contrast, direct identification of changes in stiffness health monitoring (SHM) method is validated on a 4-story shear and/or permanent deflection would offer the post-earthquake structure model undergoing seismic excitation with 10% uniform outputs desired by engineers. The goal is to obtain these noise added. The method identifies stiffness changes within 0.5- stiffness changes in real time in a computationally efficient and 1.0% inside 0.2-1.0 seconds at different sampling frequencies. robust fashion. Model-based methods combined with modern Permanent deflections are identified to within 10% of the true value in 1.0 second, converging further over the remainder of the filterang theory offer that opportunity. record

Structural Health Monitoring using Adaptive LMS Filters

A structure’s level of damage is determined using a non-linear model-based method utilising a Bouc-Wen hysteretic model. It employs adaptive Least Mean Squares (LMS) filtering theory in real time to identify changes in stiffness due to modelling error damage, as well as plastic and permanent displacements, which are critical to determining ongoing safety and use. The Structural Health Monitoring (SHM) method is validated on a four-story shear structure model undergoing seismic excitation. For the simulated structure, the algorithm identifies stiffness changes to within 10% of the true value in 0.20 s, and permanent deflection is identified to within 5% of the actual as-modelled value using noise-free simulation-derived structural responses