Experimental damage detection in a wind turbine blade model using principal components of response correlation functions

Peer reviewedPublisher PD

Evaluation of seismic response trends from long-term monitoring of two instrumented RC buildings including soil-structure interaction

Peer reviewedPublisher PD

Long term seismic response monitoring and finite element modeling of a concrete building considering soil flexibility and non-structural components

Peer reviewedPostprin

Application of multi-objective optimization to structural damage estimation via model updating

Peer reviewedPostprin

Finite element model updating of a RC building considering seismic response trends

ACKNOWLEDGEMENTS The authors would like to thank their supporters. GeoNet staff, particularly Dr Jim Cousins, Dr S.R. Uma and Dr Ken Gledhill, helped with access to seismic data and building information. Faheem Butt’s PhD study was funded by Higher Education Commission (HEC) Pakistan. Piotr Omenzetter’s work within The LRF Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by The Lloyd's Register Foundation (The LRF). The LRF supports the advancement of engineering-related education, and funds research and development that enhances safety of life at sea, on land and in the air.Peer reviewedPostprin

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

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Risk based bridge data collection and asset management and the role of structural health monitoring

Peer reviewedPostprin

Fuzzy finite element model updating for damage severity assessment

This work was performed using the Maxwell High-Performance Computing Cluster of the University of Aberdeen IT Services (www.abdn.ac.uk/staffnet/research/hpc.php), provided by Dell Inc. and supported by Alces Software.Peer reviewe

Application of time series analysis for bridge monitoring

Peer reviewedPreprin

Identification of unusual events in multi-channel bridge monitoring data

Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure such as bridges. However, converting large amounts of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localising sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events