ARMA modelled time-series classification for structural health monitoring of civil infrastructure

issue: 2articleAuthor's post-print subject to a Creative Commons Attribution Non-Commercial No Derivatives LicenseStructural health monitoring (SHM) is the subject of a great deal of ongoing research leading to the capability that reliable remote monitoring of civil infrastructure would allow a shift from schedule-based to condition-based maintenance strategies. The first stage in such a system would be the indication of an extraordinary change in the structure's behaviour. A statistical classification algorithm is presented here which is based on analysis of a structure's response in the time domain. The time-series responses are fitted with Autoregressive Moving Average (ARMA) models and the ARMA coefficients are fed to the classifier. The classifier is capable of learning in an unsupervised manner and of forming new classes when the structural response exhibits change. The approach is demonstrated with experimental data from the IASC–ASCE benchmark four-storey frame structure, the Z24 bridge and the Malaysia–Singapore Second Link bridge. The classifier is found to be capable of identifying structural change in all cases and of forming distinct classes corresponding to different structural states in most cases

Modal testing of Tamar suspension bridge

startedmonth: FebruaryCopyright © 2002-2016. Society for Experimental Mechanics, Inc.As part of a continuing investigation on the Tamar Suspension Bridge, vibration measurements on the Tamar Bridge were conducted on two occasions. Using three sensors, the first measurement (in 2005) identified possible deck vibration modes and characterised the performance of new additional stay cables. The second measurement, in 2006, used 16 accelerometers to characterise the motion of main and side spans as well as the towers, by means of identification of a full set of modal parameters (except modal masses). The procedures for these two brief operational modal tests are described with the significance of some of the findings in relation to the 'structural mechanisms' of the bridge

Real-time performance monitoring of tuned mass damper system for a 183 m reinforced concrete chimney

A 183 m reinforced concrete chimney for a coal-fired power station was instrumented in the latter part of its life during the construction of a replacement chimney. Because of concerns about large-amplitude response induced by interference effects from the new chimney in the prevailing upwind direction, a response monitoring system was installed, quickly followed by a tuned mass damper (TMD) system. As well as providing live display of the chimney response, the monitoring system was also used to check the functioning of the TMD. The monitoring system featured a direct implementation of the stochastic subspace identification procedure in the 'virtual instrument' controlling the system, so that modal damping values for the system were displayed automatically, in real-time. The system thus provided an immediate visual indication of increased damping levels during strong winds, showing the correct functioning of the TMD. The paper describes the chimney, the monitoring system and its installation, the data processing and system identification procedure, together with performance data before, during and after installation of the TMD. (C) 2009 Elsevier Ltd. All rights reserved

Stochastic method for in-situ damage analysis

Based on the physics of stochastic processes we present a new approach for structural health monitoring. We show that the new method allows for an in-situ analysis of the elastic features of a mechanical structure even for realistic excitations with correlated noise as it appears in real-world situations. In particular an experimental set-up of undamaged and damaged beam structures was exposed to a noisy excitation under turbulent wind conditions. The method of reconstructing stochastic equations from measured data has been extended to realistic noisy excitations like those given here. In our analysis the deterministic part is separated from the stochastic dynamics of the system and we show that the slope of the deterministic part, which is linked to mechanical features of the material, changes sensitively with increasing damage. The results are more significant than corresponding changes in eigenfrequencies, as commonly used for structural health monitoring.Comment: This paper is accepted by European Physical Journal B on November 2. 2012. 5 pages, 5 figures, 1 tabl

Fuzzy clustering of stability diagrams for vibration-based structural health monitoring

This is the peer reviewed version of the following article: Carden, E. P. and Brownjohn, J. M. W. (2008), Fuzzy Clustering of Stability Diagrams for Vibration-Based Structural Health Monitoring. Computer-Aided Civil and Infrastructure Engineering, 23: 360–372, which has been published in final form at: doi: 10.1111/j.1467-8667.2008.00543.x.A primary challenge to implementing structural health monitoring techniques on civil infrastructure is the differentiation of effects of environmental variables on the behaviour of structures from other causes of structural change. Data from the Z24 Bridge recorded over the course of nearly a year are analysed in this paper. Covariance-driven Stochastic Subspace Identification is applied to the data and a Fuzzy Clustering Algorithm is used to extract parameters indicative of the bridge’s state. The main benefit of this approach is the lack of need for mode shape information and thus it’s applicability to structures monitored with spatially sparse sensor grids. The method is shown to provide very encouraging results in separating the response data from the Z24 Bridge in normal and damaged states in varying environmental conditions, and the procedure is then applied to a second data set obtained from monitoring a tall building over several years of its early life in order to identify gradual or sudden structural changes