# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 5 Conclusions  
The accurate identification of modal parameters is essential for SHM, and this can be achieved by exploiting the structural responses measured in ambient environments following the OMA framework. Considering its versatility in handling real-world structural monitoring challenges, SSI is particularly applicable to complex structures or field applications. This study aims to propose a three-step procedure for implementing automated SSI-based OMA, including data preprocessing, matrix construction and decomposition, and system realization. To verify the effectiveness of the proposed procedure, two datasets, collected using conventional SHM systems, were investigated and analyzed herein. Accordingly, it is evident that the selected size of the SSI matrix, the estimation of system order, and the removal of spurious modes can significantly enhance system identification techniques, providing a 10% improvement in overall error rates. Most importantly, loss rates, in identifying structure mode in complex field applications, can be decreased by approximately three to four times, and therefore, continuous and long-term SHM can be  
Figure 11: The estimated system orders using four approaches through singular values. (**a**) seven-story RC frame (first dataset); (**b**) four-story RC frame (first dataset). facilitated by enhanced SSI, implemented with the proposed three-step procedure. The conclusions drawn from these results can be summarized as follows:  
* The techniques of P-wave pickers can be utilized to differentiate nonstationary excitation and secure analysis based on OMA and SSI. * The preliminary identification of fundamental frequency helps the overall automated work through down-sampling, sizing the Hankel matrix, and clarifying the stability diagram. * Assigning the size of the Hankel matrix with integers multiplied from the fundamental periods can improve decomposition with lower differences between pairs and avoid spurious modes. * The proposed decay function provides a good estimation of system order by finding the first point at which the system experiences a steady descent. * The proposed three-step procedure based on SSI can facilitate automated OMA for continuous and long-term SHM, in terms of adjusting user-defined parameters autonomously. Identifying modal parameters accurately is crucial for complex and field-based SHM systems, as it establishes a robust foundation for effective monitoring, facilitates early issue detection, and supports data-driven maintenance strategies. Moreover, diagnosis and prognosis grounded in a thorough physical understanding of structural behavior offer valuable insights for comprehensive assessment and informed decision-making regarding maintenance. Although this study has provided initial validations, further research is recommended--particularly a detailed case study on damage detection--to more fully demonstrate the continuous performance of SHM systems prior to field deployment. Conceptualization and funding acquisition, S.-K.H. and H.-P.L. ; resources and visualization, S.-K.H. and Y.-Y.Y. ; writing--original draft preparation, supervision, and project administration, S.-K.H. ; software, formal analysis, and investigation, Z.-Z.L. ; validation and data curation, H.-P.L. and Y.-Y.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science and Technology Council, grant number NSTC 113-2625-M-005-011-, and INTI International University. Not applicable. The data presented in this study are available on request from the corresponding author. The data were collected at the National Center for Research on Earthquake Engineering (NCREE) and National Chung Hsing University in Taiwan. The research was funded by the INTI International University in Malaysia. The authors would like to thank those involved for all their technical and financial support. The authors declare no conflicts of interest.