Deterministic - Stochastic Subspace Identification for Bridges

Abstract

System identification for structural engineering has received significant attention in the last thirty years. With the ever increasing capacity of computing technology, system identification has been applied to important structures such as bridges and aircraft. In the case of bridges, the output can easily be measured by accelerometers. Considerable research in system identification on bridges has been done using output-only models. Of course, it is difficult to measure the inputs on an in-service bridge. In this paper, we see how the inputs can be estimated from the output measurements. We then use an input-output model to develop an improved system identification technique for identifying bridges. We show that the proposed method using the estimated inputs yields superior identification in a simulated case (i.e., where everything is controlled). We then use the method on the in-service Walnut Creek Bridge located on the north-bound lanes of U.S. Interstate I-35 between Dallas, TX and Oklahoma City, OK.

A Structural Health Monitoring System Based on an Analysis of Changes in the Static, Dynamic and Magnetic Properties of the Structure

“…Online analysis consist of real time calculating resonance frequencies as accelerometer data is collected. For this purpose recursive Stochastic Subspace Identification [5,11,12] algorithm is used. Resonance frequencies of the bridge are then constantly monitored for any changes that could be linked to the faults of the structure.…”