Table 1: Summary of damage detection methods for the ASCE benchmark structure.

ReferenceAuthor(s)Damage detection method

Reference document—No dataJohnson et al. [7, 8]Detailed Description of Phase I—Simulated
Dyke et al. [9]Detailed Description of Phase II—Experimental

Phase I: Simulated dataDyke et al. [10]Loss of stiffness of members byoptimizing modal parameters
Hera et al. [11]Spikes in Level 1 details of wavelet decomposed signals
Yang et al. [12]Spectral analysis to identify stiffness parameters
Hera and Hou [13]Spikes in Level 1 details of wavelet decomposed signals
Sun and Chang [14]Covariance of response using wavelet packets
Lam et al. [15]Loss of stiffness using modal update and identification
Yuen et al. [16]Loss of stiffness of members using modal parameter extraction and Bayesian modal updating
Lus et al. and Caicedo et al. [17, 18]State space model, eigensystem realization algorithm and optimization using modal parameters
Bernal and Gunes [19]Extraction of a matrix proportional to structure flexibility
Lin et al. [20]Time-frequency features obtained using Hilbert-Huang transform of the intrinsic mode functions
Chase et al. [21]Recursive least square to identify changes in stiffness matrix
Wu and Li [22]Eingen-sensitive FE for damage detection in ambient vibration
Yang and Huang [23]A recursive nonlinear estimation method is used
Mizuno and Fujino [24]Haar wavelet decomposition, quantization, and dissimilarity
Zhou et al. [25]Residual values from subspace-modal identification

Phase II: Simulated dataHou and Hera [26]Spikes in Level 1 details of wavelet decomposed signals using Daubechies and Meyer wavelets
Barroso and Rodriguez [27]Comparison of healthy to damage curvature in the mode shapes
Casciati [28]Discrepancy between healthy and damaged states using sum of squared errors

Phase II: Simulated and experimental dataHera and Hou [29]Modal parameters determined using continuous wavelet transform
Dincal and Raich [30]Minimization of error term between FRF of experimental & simulated data
Nair et al. [31]Structural stiffness change based on poles; pattern classification with autoregressive coefficients

Phase II: experimental data onlyChing and Beck [32, 33]Expectation-Maximization algorithm used to find most probable stiffness parameters—Config. 2–9
Giraldo et al. [34]Loss of stiffness of members—Config 2–6
Lynch [35]Pole location using system identification, Config. 1–5
Liu et al. [36]Time-frequency obtained using Hilbert-Huang transform of intrinsic modes—Config. 7 & 8
McCuskey et al. [37] and McCuskey [38]Neural-wavelet module—All Configurations
Carden and Brownjohn [39]Autoregressive moving average (ARMA) to build damage classifiers for different damage configurations