Develop On-line Parameter Estimation Methods for Bridges Under Changing Environment

The objective of this paper is to develop on-line system modal parameter estimation technique from the response measurements through using both the Stochastic Subspace identification (SSI) and Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approaches. To avoid time-consumption of SVD in RSSI the Extended Instrumental Variable version (EIV-PAST) is used in SSI-COV. To avoid time-consumption of SVD in RSSI the Extended Instrumental Variable version (EIV-PAST) is used in SSI-COV. To reduce the effect of noise and down-sampling for identification, discussion on the preprocessing of data using recursive singular spectrum analysis technique (rSSA) is also presented, so as to enhance the stability of data analysis. Application of the identification algorithm to the vibration measurements of a steel arch bridge under operation condition is demonstrated. Time-varying environmental and operational conditions such as temperature and traffic loading induce changes in the identified features are examined through statistical novelty analysis.