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IEEE Transactions on Software Engineering
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Empirical Software Engineering
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Journal of Systems and Software
Model-free data interpretation for continuous monitoring of complex structures
Advanced Engineering Informatics
Autoregressive coefficients based Hotelling's T2 control chart for structural health monitoring
Computers and Structures
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Configuring and enhancing measurement systems for damage identification
Advanced Engineering Informatics
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KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Advanced Engineering Informatics
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Computers and Structures
Support vector regression for anomaly detection from measurement histories
Advanced Engineering Informatics
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Structural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation. This paper presents two methodologies for model-free data interpretation to identify and localize anomalous behaviour in civil engineering structures. Two statistical methods based on (i) moving principal component analysis and (ii) robust regression analysis are demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The methodologies are tested on numerically simulated elements with sensors for a range of noise in measurements. A comparative study with other statistical analyses demonstrates superior performance of these methods for damage detection. Approaches for accommodating outliers and missing data, which are commonly encountered in structural health monitoring for civil structures, are also proposed. To ensure that the methodologies are scalable for complex structures with many sensors, a clustering algorithm groups sensors that have strong correlations between their measurements. Methodologies are then validated on two full-scale structures. The results show the ability of the methodology to identify abrupt permanent changes in behavior.