Transfer function‐based Bayesian damage detection under seismic excitation

Abstract

Summary Transfer function (TF) data are recognized as diagnostic features in damage detection procedure. The objective of this paper is to present a damage detection method in Bayesian paradigm based on TF data due to ground excitation. The measured seismic responses of the structure in the frequency domain are adopted to obtain displacement TFs and the structural natural frequencies are identified from observed TFs. The derived features are utilized for Bayesian structural damage detection. In addition, the challenging issue of underlying flexible soil in real cases has been addressed. The proposed technique is applied to a numerical shear frame to evaluate the capability of the method. An experimental study on a six‐story steel building has been validated to demonstrate the capability of the method for damage detection purpose. The results of studied cases indicated that the proposed method is capable of identifying the location and the severity of damage precisely.

Damage detection, quantification, and localization for resonant metamaterials using physics-based and data-driven methods

“…Esfandiari 21–23 investigated the sensitivity-based model updating technology based on FRF, which was then applied to locate and quantify damage of beams. Recently, FRF-based damage detection was conducted within the Bayesian framework by accommodating multiple sources of uncertainty in Vahedi et al 24…”

“…Esfandiari [21][22][23] investigated the sensitivitybased model updating technology based on FRF, which was then applied to locate and quantify damage of beams. Recently, FRF-based damage detection was conducted within the Bayesian framework by accommodating multiple sources of uncertainty in Vahedi et al 24 According to Worden et al, 25 the damage identification problem can also be addressed using an inverse problem where a high-fidelity physical model of the structure is required 26 or as a pattern recognition problem requiring a statistical model representation of the system. 27 For the model-based approach, the concept of damage sensitivity equations 28 is usually used in an inverse problem which can be based on any type of data, for example, modal data or time series.…”

Damage tracking and hysteresis‐based evaluation of seismic‐excited RC shear wall using monitoring data

“…Such visual assessment could be subjective, slow, labor‐intensive, costly, and dangerous, and sometimes, the damage hidden in the building finishes and fireproofing could be difficult to identify. On the other hand, the data can be employed to extract the dynamic properties of structures for inferring their damage and to update the existing finite‐element models 8,21,22 . For instance, Iervolino et al 23 adopted the measurement to update an elastic‐perfectly plastic system to evaluate the accumulated damage.…”

“…On the other hand, the data can be employed to extract the dynamic properties of structures for inferring their damage and to update the existing finite-element models. 8,21,22 For instance, Iervolino et al 23 adopted the measurement to update an elastic-perfectly plastic system to evaluate the accumulated damage.…”

Output-only structural parameter identification with evolutionary algorithms and correlation functions

“…Considerable frequency and time domain identification methods have been proposed to identify structural unknown parameters. In most frequency domain methods, the change of modal information, such as modal data [1,2], modal strain energy [3,4], the transfer function parameters [5,6], was extracted to identify variation of structural parameters. To deal with absence of force measurements, some frequency domain methods seek to find a satisfactory result with the premise of assuming the excitation in term of white noise [7][8][9][10].…”