This paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extracting key modal parameters such as natural frequencies, damping ratios, and mode shapes from vibration data. To address the limitations of traditional manual methods, several approaches have been developed to automate this process. Among these, SSI stands out as one of the most effective time-domain methods due to its robustness in handling noisy environments and closely spaced modes. This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. Advanced SSI implementations that incorporate real-time recursive estimation, adaptive stabilization criteria, and automated mode selection are also discussed. Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. This review concludes by highlighting the current challenges, such as computational demands and data management, and proposing future directions for research in automated modal analysis to support resilient, sustainable infrastructure.

This paper presents a comprehensive review of bridge health identification (BHI) within the emerging paradigm of intelligent infrastructure, with a particular focus on modal analysis and artificial intelligence (AI)-driven methodologies. Aging bridge networks, increasing traffic demands, and environmental stressors have significantly accelerated structural deterioration, necessitating advanced monitoring and diagnostic frameworks. Modal parameters, including natural frequencies, mode shapes, and damping ratios, are widely recognized as reliable indicators of structural condition and form the foundation of vibration-based BHI. This study systematically reviews operational modal analysis (OMA) techniques, including frequency-domain, time-domain, and hybrid approaches, highlighting their capabilities and limitations under real-world conditions. Furthermore, the integration of AI and machine learning (ML) methods, ranging from supervised and unsupervised learning to deep learning (DL) and reinforcement learning (RL), is critically examined in the context of data-driven damage detection, feature extraction, and predictive maintenance. Special attention is given to Automated Operational Modal Analysis (AOMA), where recent advances in FDD- and SSI-based frameworks have enabled scalable and user-independent modal identification. Despite significant progress, key challenges remain, including environmental variability, data scarcity, lack of interpretability, and deployment constraints. Finally, the paper identifies major research gaps and outlines future directions toward physics-informed AI, multi-modal data fusion, uncertainty-aware decision-making, and digital twin integration. The study provides a unified perspective bridging structural dynamics and intelligent data-driven approaches, contributing to the development of next-generation smart bridge monitoring systems.