# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 1 Introduction
Consequently, two common alternatives to SSI have been developed, which differ in terms of how they treat data [19; 20]. One is data-driven SSI (SSI-Data), which directly uses measured responses without special processing; the other is covariance-driven SSI (SSI-Cov), which requires that a covariance matrix be first computed from measured responses and then utilized for identification. Thereafter, a number of researchers further refined SSI methodologies: [PERSON] et al. looked over SSI-Cov, SSI-Data, and frequency-domain decomposition for bridge monitoring, with SSI-Cov delivering superior accuracy [21; 22]; [PERSON] et al. established guidelines for selecting user-defined parameters in SSI-Data, addressing both stationary and non-stationary excitation conditions [23]; [PERSON] et al. analyzed the influences of parameters on SSI-Cov for arch dam monitoring, providing practical recommendations for optimal parameter selection [24]; and so on [25; 26]. As famous output-only approaches, these methodologies have seen widespread adoption across civil engineering (e.g., dams, bridges), mechanical systems, and aerospace structures, underscoring their versatility and robustness. Assuredly, SSI has numerous applications in OMA, which offer modal analysis in ambient environments [27; 28; 29]. However, recently, there has been increasing interest in implementing automated OMA, which works as a cornerstone of streamlining long-term SHM by minimizing human intervention [30; 31]. The newest researchers are therefore attempting to integrate artificial intelligence (AI) [32; 33]. Nevertheless, this implementation faces specific challenges and emergent enhancements [34]. For example, one limitation is the requirement to manually specify the model order during field monitoring, as estimating system order accurately is often difficult [35]. To address this, practitioners frequently over-specify the order to improve data fitting accuracy, but this risks introducing spurious modes into the results [36]. The stabilization diagram has been exploited as a critical tool for distinguishing physical modes from spurious ones by varying parameters and identifying stable vertical lines. Despite their utility, interpreting these diagrams still relies on human expertise. To enable full automation, researchers employ a pre-filtering method, which applies criteria such as Modal Phase Collinearity (MPC) to eliminate obvious outliers [37; 38]. Instead, one can cluster modes with similar dynamic characteristics using grouping algorithms, such as partition-based clustering, density-based clustering, and so on and so forth [39; 40]. While these methods enhance automation, their effectiveness hinges on the robustness of logical rules and thresholds in diverse scenarios. A persistent issue arises for complex structures or field applications, where residual spurious poles (outliers) evade prefiltering and disrupt clustering accuracy, ultimately compromising the reliability of modal analysis. This study aims to propose an improved procedure for implementing automated OMA based on SSI, focusing specifically on the selected size of the SSI matrix, the estimation of system order, and the removal of spurious modes. To facilitate a long-term SHM using SSI, a comprehensive discussion is addressed across data preprocessing, matrix construction, numerical decomposition, and system realization. The rest of this paper is structured as follows. First, the basic formulation of SSI is introduced and derived to illustrate the overall procedure for implementing automated OMA. A general instruction for user-defined parameters, used while applying SSI, is also reviewed and summarized. Subsequently, according to the discussion on adequate user-defined parameters, an improved procedure is proposed and framed into three steps. Some key examinations are numerically demonstrated using a simulation study of a shear-type building structure.