Structural Control & Health Monitoring
John Wiley & Sons Ltd
About: Structural Control & Health Monitoring is an academic journal published by John Wiley & Sons Ltd. The journal publishes majorly in the area(s): Structural health monitoring & Damper. It has an ISSN identifier of 1545-2263. Over the lifetime, 1860 publications have been published receiving 50608 citations.
Papers published on a yearly basis
TL;DR: A brief introduction to smart sensing technology is provided and some of the opportunities and associated challenges are identified.
Abstract: ‘Smart’ sensors with embedded microprocessors and wireless communication links have the potential to change fundamentally the way civil infrastructure systems are monitored, controlled, and maintained. Indeed, a 2002 National Research Council report noted that the use of networked systems of embedded computers and sensors throughout society could well dwarf all previous milestones in the information revolution. However, a framework does not yet exist that can allow the distributed computing paradigm offered by smart sensors to be employed for structural health monitoring and control systems; current algorithms assume that all data is centrally collected and processed. Such an approach does not scale to systems with densely instrumented arrays of sensors that will be required for the next generation of structural health monitoring and control systems. This paper provides a brief introduction to smart sensing technology and identifies some of the opportunities and associated challenges. Copyright © 2004 John Wiley & Sons, Ltd.
TL;DR: This application of Bayes' Theorem automatically applies a quantitative Ockham's razor that penalizes the data‐fit of more complex model classes that extract more information from the data.
Abstract: Probability logic with Bayesian updating provides a rigorous framework to quantify modeling uncertainty and perform system identification. It uses probability as a multi-valued propositional logic for plausible reasoning where the probability of a model is a measure of its relative plausibility within a set of models. System identification is thus viewed as inference about plausible system models and not as a quixotic quest for the true model. Instead of using system data to estimate the model parameters, Bayes' Theorem is used to update the relative plausibility of each model in a model class, which is a set of input–output probability models for the system and a probability distribution over this set that expresses the initial plausibility of each model. Robust predictive analyses informed by the system data use the entire model class with the probabilistic predictions of each model being weighed by its posterior probability. Additional robustness to modeling uncertainty comes from combining the robust predictions of each model class in a set of candidates for the system, where each contribution is weighed by the posterior probability of the model class. This application of Bayes' Theorem automatically applies a quantitative Ockham's razor that penalizes the data-fit of more complex model classes that extract more information from the data. Robust analyses involve integrals over parameter spaces that usually must be evaluated numerically by Laplace's method of asymptotic approximation or by Markov Chain Monte Carlo methods. An illustrative application is given using synthetic data corresponding to a structural health monitoring benchmark structure.
TL;DR: In this paper, the authors explored the use of heterogeneous, non-collocated measurements for nonlinear structural system identification and compared the performance of the unscented Kalman filter (UKF) and particle filter method (SMC).
Abstract: The use of heterogeneous, non-collocated measurements for nonlinear structural system identification is explored herein. In particular, this paper considers the example of sensor heterogeneity arising from the fact that both acceleration and displacement are measured at various locations of the structural system. The availability of non-collocated data might often arise in the identification of systems where the displacement data may be provided through global positioning systems (GPS). The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification. However, as suggested in (J. Eng. Mech. 1999; 125(2):133–142), the EKF is not effective in the case of highly nonlinear problems. Instead, two techniques are examined herein, the unscented Kalman filter method (UKF), proposed by Julier and Uhlman, and the particle filter method, also known as sequential Monte Carlo method (SMC). The two methods are compared and their efficiency is evaluated through the example of a three degree-of-freedom system, involving a Bouc–Wen hysteretic component, where the availability of displacement and acceleration measurements for different DOFs is assumed. Copyright © 2008 John Wiley & Sons, Ltd.
TL;DR: In this paper, a structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring.
Abstract: The Guangzhou New TV Tower (GNTVT), currently being constructed in Guangzhou, China, is a supertall structure with a height of 610 m. This tube-in-tube structure comprises a reinforced concrete inner tube and a steel outer tube adopting concrete-filled-tube columns. A sophisticated structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring. This paper outlines the technology innovation in developing and implementing this SHM system, which includes (i) modular design of the SHM system, (ii) integration of the in-construction monitoring system and the in-service monitoring system, (iii) wireless-based data acquisition and Internet-based remote data transmission, (iv) design and implementation of a fiber Bragg grating sensing system,(v) structural health and condition assessment using static and dynamic monitoring data, (vi) verification of the effectiveness of vibration control devices by the SHM system, and (vii) development of an SHM benchmark problem by taking GNTVT as a test bed and using real-world measurement data. Preliminary monitoring data including those obtained during the Wenchuan earthquake and recent typhoons are also presented. Copyright © 2008 John Wiley & Sons, Ltd.
TL;DR: In this paper, an adaptive tracking technique based on the extended Kalman filter approach is proposed to identify the structural parameters and their changes when vibration data involve damage events, which is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on-line.
Abstract: The identification of structural damage is an important objective of health monitoring for civil infrastructures. System identification and damage detection based on measured vibration data have received intensive studies recently. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes when vibration data involve damage events. The proposed technique is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on-line. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noise. This technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of nonlinear elastic, hysteretic and linear benchmark structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting structural damage, using measured vibration data. Copyright © 2005 John Wiley & Sons, Ltd.