Show simple item record

dc.contributor.authorOmenzetter, P
dc.contributor.authorBrownjohn, James
dc.date.accessioned2016-02-01T15:42:00Z
dc.date.issued2006-01-09
dc.description.abstractDespite the recent considerable advances in structural health monitoring (SHM) of civil infrastructure, converting large amount of data from SHM systems into usable information and knowledge remains a great challenge. This paper addresses the problem through analysis of time histories of static strain data recorded by an SHM system installed in a major bridge structure and operating continuously for a long time. The reported study formulates a vector seasonal autoregressive integrated moving average (ARIMA) model for the recorded strain signals. The coefficients of the ARIMA model are allowed to vary with time and are identified using an adaptive Kalman filter. The proposed method has been used for analysis of the signals recorded during construction and service life of the bridge. By observing various changes in the ARIMA model coefficients, unusual events as well as structural change or damage sustained by the structure can be revealed.en_GB
dc.identifier.citationVol. 15, pp. 129 - 138en_GB
dc.identifier.doi10.1088/0964-1726/15/1/041
dc.identifier.urihttp://hdl.handle.net/10871/19493
dc.language.isoenen_GB
dc.publisherIOP Publishing:en_GB
dc.titleApplication of time series analysis for bridge health monitoringen_GB
dc.typeArticleen_GB
dc.date.available2016-02-01T15:42:00Z
dc.identifier.issn0964-1726
dc.descriptionAuthor's manuscript version. The final published version is available from the publisher website at: http://dx.doi.org/10.1088/0964-1726/15/1/041. © 2006 IOP Publishing Limiteden_GB
dc.identifier.journalSmart Materials and Structuresen_GB


Files in this item

This item appears in the following Collection(s)

Show simple item record