– Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings.
– Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables.
– The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining.
– This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.
