Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures

Abstract

Experimental modal analysis (EMA) has been explored as a technology for condition assessment and damage identification of constructed structures. However, successful EMA applications such as damage detection to constructed systems pose certain difficulties. The properties of constructed systems are influenced by temperature changes as well as other natural influences such as movements in addition to any deterioration and damage. Writers were challenged in their attempts to measure the dynamic properties of an aged bridge by EMA due to inconsistencies within the data set due to short-term variations in ambient conditions. A complex interaction was observed between the dynamic properties of the bridge, hour-to-hour changes in temperature, and controlled damages applied to the bridge. Inconsistencies in the data set made curve fitting difficult for some common parameter estimation algorithms that have been designed to handle consistent data sets. Although the quality of measurements within the entire data set was affected by time variance and nonlinearity, increasing the number of reference measurements significantly improved the reliability of the information which could be extracted. In conjunction with the multiple-input multiple-output technique, a parameter estimation method using complex mode indicator function (CMIF) was developed and implemented in this study to determine the modal properties with proper scaling to obtain modal flexibility. This method proved to be very successful among many others with the data acquired from the aged and deteriorated highway bridge. In this paper, challenges in reliable identification of modal parameters from large structures are reviewed and the new CMIF based algorithm is documented. The method is evaluated on actual bridge data sets from a damage detection research study.