Processing of Ambient Vibration Results using Stochastic Subspace Identification based on Canonical Correlation Analysis

The presence of environmental and measurement noises and ignoring the input effects are the main sources of error in system identification using ambient vibration test results. Therefore, reducing uncertainty or noise levels from the records has always been one of the main goals of the new techniques in the field of ambient vibration. Among the modal analysis techniques, stochastic subspace identification is considered as a powerful technique. In this study, the modal analysis method based on canonical correlation analysis in stochastic subspace is presented that identifies dynamic properties in optimized space instead of data space by extracting ortho-normal vector of data space. The advantage of this method, due to the nature of canonical correlation analysis, is lower noise which results in greater accuracy in estimating modal properties. Moreover, the presented process is faster due to the smaller space of identification compared to the previous methods. To validate the proposed method, an analytical model of two-dimensional frame excited under Elcentro earthquake acceleration and also the results of ambient vibration tests carried out on the Alamosa Canyon Bridge are used. The results indicate that this method eliminates more noise than other subspace methods and moreover it is faster in solving practical problems. The computation of dynamic properties, natural frequencies and mode shapes, of Alamosa Canyon Bridge with 30 sampling sensors, space matrix size of 750 and 50 excited modes are carried out in less than 150 seconds with a quad-core 2.30 GHz processor.