# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 5 Challenges of SSI in Practical Application
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Concern** \\ \hline [PERSON] and [PERSON] [126] & SSI & Dealing with the sensor data of different measurement setups. \\ \hline [PERSON] and [PERSON] [127] & Correlation-driven subspace algorithm & The effect of sample size and the dimension of the Hankel matrix on the estimated damping coefficient \\ \hline [PERSON] [128] & - & Discriminates the effects of epistemic uncertainty \\ \hline [PERSON] and [PERSON] [129] & SSI & Consistency of the subspace algorithm against non-stationarities in the measurement data \\ \hline [PERSON] et al. [130] & SSI & Removing bias errors in ambient vibrations \\ \hline [PERSON] et al. [131] & SSI-COV and SSI-DATA & Effect of non-structural elements in the vibrational behavior of bridge structures. \\ \hline [PERSON] \& [PERSON] [132] & SSI-COV and SSI-DATA \\  & techniques through BR and CVA. & The effect of structural complexity and ambient conditions \\ \hline [PERSON] et al. [133] & ST-SSI & Time-variant identification \\ \hline [PERSON] et al. [134] & Extended SSI-COV-based & Handling temperature effect in the identified modal parameters. \\ \hline [PERSON] et al. [135] & SSI together with ARX & Damage detection of Hammerstein systems or non-linearity introduced to a linear dynamic system through piece-wise constant inputs \\ \hline [PERSON] et al. [136] & EFDD and SSI-COV & Quality of the extracted damping ratios considering the proximity of natural frequencies, non-proportional damping and accuracy of the identification algorithms \\ \hline [PERSON] et al. [137] & SSI-COV & Multiple non-simultaneously recorded measurement setups. \\ \hline [PERSON] and [PERSON] [138] & A combined SSI-DATA method and first order perturbation technique & The challenges faced in extracting the accurate confidence interval of modal parameters in civil engineering structures. \\ \hline [PERSON] and [PERSON] [139] & SSI- a modular and scalable approach & Merging sensor data by applying a modular approach. \\ \hline [PERSON] et al. [140] & First-order SSI method & Evaluation of the statistical uncertainty in a multi-setup configuration \\ \hline [PERSON] and [PERSON] [141] & Hybrid RSSI-COV, NLPCA and AANN & Distinguishing the damage abnormality from those caused by environmental and operational variations \\ \hline [PERSON] et al. [142] & Power spectral amplitudes and FE model & Determination of the optimum position of the reference sensor \\ \hline [PERSON] et al. [143] & Kalman filter & Contribution of the modal parameters in each mode to the recorded measurement data. \\ \hline [PERSON] and [PERSON] [144] & SSI-based algorithm & Injecting auxiliary input to extract the unexcited modes \\ \hline [PERSON] and [PERSON], [145] & [PERSON] and SSI & Introducing uncertainty into an FE model \\ \hline [PERSON] and [PERSON] [146] & SSI & Influence of the system order and number of block rows in identification accuracy \\ \hline [PERSON] et al. [147] & SDSI & Implementation of VDD using wireless sensor networks \\ \hline [PERSON] [148] & SSI & Influence of missing measurement data in dynamic system \\ \hline [PERSON] \& [PERSON] [149] & SSI, SRA and FDD & Effect of uncertainty in determining modal parameters \\ \hline [PERSON] et al. [150] & PCE & Humidity and temperature effects \\ \hline [PERSON] et al. [151] & A combined SSI algorithm and short-time Fourier transform & Structural analysis of a cable-stayed bridge under typhoons with various wind speeds. \\ \hline [PERSON] et al. [152] & Improved SSI algorithm & Eliminating spurious modes caused by non-white noise \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The challenges of SSI in practical applications.