Experimental Study of Damage Detection by Data-Driven Subspace Identification and Finite-Element Model Updating

Abstract

The need to identify the physical properties of a structure given its force-response (input-output) relationship is driven primarily by the need to validate the approximate solution models, such as finite-element models. This paper proposes a method, which combines the structural system identification and model updating techniques, for the damage detection of a steel frame structure and a RC frame. The damage detection procedure consists of two steps: (1) identifying the system dynamic characteristics using the subspace identification (SI) technique from input/output measurements and (2) developing a damage assessment method for structural members (including joints) based on a progressive finite-element model updating and a large-scale optimization using a nonlinear least-square technique. The proposed method was verified through a shaking table experimental study using: (1) a 1/4-scale six-story steel frame structure by loosening the connection bolts for damage simulations and (2) a two-story RC frame subject to different levels of ground excitations back to back. As demonstrated by experimental results, the proposed damage detection method, based on the combination of SI technique and the model updating approach, is very effective for the damage assessment of frame structures. The method not only can detect the damage locations but also can quantify the damage severities.