LMS-based approach to structural health monitoring of nonlinear hysteretic structures

Abstract

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, better metrics of seismic structural damage and future utility after an event are related to permanent and total plastic deformations. This study presents a modified LMS-based SHM method and a novel two-step structural identification technique using a baseline nonlinear Bouc—Wen structural model to directly identify changes in stiffness due to damage as well as plastic or permanent deflections. The algorithm is designed to be computationally efficient; therefore it can work in real-time. An in silico single-degree-of-freedom (SDOF) nonlinear shear-type structure is used to prove the concept. The efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections is assessed under different ground motions using a suite of 20 different ground acceleration records. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed LMS-based SHM algorithm identifies stiffness changes to within 10% of true values within 2s. Permanent deflection is identified to within 14% of the actual as-modeled value using noise-free simulation-derived structural responses. This latter value provides important post-event information on the future serviceability, safety, and repair cost.

Physical parameter identification of nonlinear base-isolated buildings using seismic response data

“…The primary reason is that no prior methods split the linear half-cycles from the nonlinear half-cycles of response and pull out nonlinear half-cycle displacement and post-yielding stiffness, except Nayerloo et al [42], which is a much more complex, but realtime, algorithm. Equally importantly, the work of Nayerloo et al [42] is restricted to fitting a Bouc-Wen model, whereas this approach is more general to any nonlinear, elasto-plastic method. Finally, it is important to note that we found no prior works that directly identified nonlinear stiffness in this fashion making direct comparison very difficult for those that do address nonlinear behaviour.…”

Seismic structural displacement measurement using a line-scan camera: camera-pattern calibration and experimental validation

“…Limitations on acquiring the necessary input data have made the implementation of many existing SHM algorithms difficult. In particular, most SHM and control algorithms for damage detection and mitigation require continuous monitoring of dynamic responses: acceleration, velocity, and displacement [2][3][4]. Acceleration can be easily measured using ordinary accelerometers.…”

Survey and Introduction to the Focused Section on Mechatronics for Sustainable and Resilient Civil Infrastructure

“…These methods typically only use accelerometers as sensors and rely on the change in natural frequencies to detect damage [5,10,13]. However, a change in a frequency doesn't necessarily represent damage, particularly with highly non-linear responses [14,15].…”

“…In addition, SHM must be identify localized damage, be robust in the presence of noise and evaluate structural health rapidly or in real-time [4,5]. All of these characteristics are ones that can increasingly be met by emerging, improved sensor technologies that are more distributed, low cost and easily used in volume, and/or integrate computation directly with measurement.…”

“…Most efforts to date in this field have focused on structures in seismic zones and assessing damage after a major event, and in particular on computational methods to assess damage (eg [3][4][5][6][7][8][9][10][11]). Many current vibration-based SHM methods, particularly for large civil structures, are based on modal parameter damage detection in both the time series and frequency domain [2,12].…”