Principal Component Analysis for Condition Monitoring of a Network of Bridge Structures

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Hanley, Ciarán^1 ; Kelliher, Denis^2 ; Pakrashi, Vikram^1

Dynamical Systems and Risk Laboratory (DSRL), School of Engineering, University College Cork, Ireland^1

Research Unit for Structures and Optimisation (RUSO), School of Engineering, University College Cork, Ireland^2

关键词: Bridge management system;    Bridge structures;    Large amounts of data;    Large datasets;    Large scale data sets;    Multivariate techniques;    Predictive tools;    Visual inspection;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012060/pdf
DOI  :  10.1088/1742-6596/628/1/012060

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The use of visual inspections as the primary data gathering tool for modern bridge management systems is widespread, and thus leads to the collection and storage of large amounts of data points. Consequently, there exists an opportunity to use multivariate techniques to analyse large scale data sets as a descriptive and predictive tool. One such technique for analysing large data sets is principal component analysis (PCA), which can reduce the dimensionality of a data set into its most important components, while retaining as much variation as possible. An example is applied to a network of bridges in order to demonstrate the utility of the technique as applied to bridge management systems.