Damage Characterization of the IASC-ASCE Structural Health Monitoring Benchmark Structure by Transfer Function Pole Migration

Abstract

In this paper, a novel approach to the characterization of structural damage in civil structures is presented. Structural damage often results in subtle changes to structural stiffness and damping properties that are manifested by changes in the location of transfer function characteristic equation roots (poles) upon the complex plane. Using structural response time-history data collected from an instrumented structure, transfer function poles can be estimated using traditional system identification methods. Comparing the location of poles corresponding to the structure in an unknown structural state to those of the undamaged structure, damage can be accurately identified. The IASC-ASCE structural health monitoring benchmark structure is used in this study to illustrate the merits of the transfer function pole migration approach to damage detection in civil structures.

Review of Benchmark Studies and Guidelines for Structural Health Monitoring

“…However, some false positive damage locations were indicated for relatively small damage occurrences. The flexibility-based approach Bernal and Gunes 2004) and the transfer function pole migration method (Lynch 2005), were also investigated based on the defined SHM benchmark structure. System identification methodologies were investigated using this benchmark structure, such as the natural excitation technique (NExT) and eigensystem realization algorithm (ERA) (Caicedo et al 2004), Observer/Kalman filter identification algorithm (Lus et al 2004), Observer/Kalman and subspace identification method (Bernal and Gunes 2000).…”

“…System identification methodologies were investigated using this benchmark structure, such as the natural excitation technique (NExT) and eigensystem realization algorithm (ERA) (Caicedo et al 2004), Observer/Kalman filter identification algorithm (Lus et al 2004), Observer/Kalman and subspace identification method (Bernal and Gunes 2000). Various other methods were also studied based on the defined SHM benchmark structure, such as the two-stage eigensensitivity-based model updating techniques (Wu and Li 2006), the transfer function pole migration method (Lynch 2005) and artificial neural network (ANN) methods (Wang et al 2011).…”

Story‐by‐story estimation of the stiffness parameters of laterally‐torsionally coupled buildings using forced or ambient vibration data: II. Application to experimental data

“…The rest of the related work on Experimental Phase II data focused mostly on detecting the presence of damage in the specimen structure, without translating the detection results to changes/loss in physical structural parameter values or specifying the damage location. For example, the method proposed in correlates damage to the migration of the poles of the system transfer function as the structure undergoes different damage patterns. The authors use data from forced vibration of the test structure with random shaker excitation to detect damage in configurations 2 to 5, and conclude that the degree of migration is sensitive to the extent of damage.…”