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Summary Challenges concerning the automation of modal identification and tracking procedures in permanent monitoring systems for Structural Health Monitoring purposes are discussed. In this context, an automated procedure based on parametric identification methods that involve the interpretation of stabilization diagrams is proposed. The methodology comprehends two key points: (i) automatic analysis of stabilization diagrams, performed through a first check of reasonable damping ratio, a subsequent modal complexity check and a final clustering of structural modes; (ii) automated tracking of the evolution in time of the identified modal properties. The proposed modal clustering and tracking steps exploit the introduction of self‐adaptable dynamic thresholds, that do not require any a priori manual tuning for the different recorded data set. Finally, the proposed approach was successfully validated using real data collected on a historic iron arch bridge. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings.

Summary In the present study, output‐only modal dynamic identification and monitoring of building properties is attempted successfully by processing real earthquake‐induced structural response signals. This is achieved through an enhanced version of a recently‐developed refined Frequency Domain Decomposition (rFDD) approach, which in the earlier implementation was adopted to analyse synthetic seismic response signals only. Despite that short duration, nonstationary seismic response data and heavy structural damping shall not fulfil traditional Operational Modal Analysis assumptions, the present rFDD response‐only algorithm allows for the effective estimation of strong‐motion natural frequencies, mode shapes, and modal damping ratios, with real seismic response signals. The present rFDD enhancement derives from a preprocessing time‐frequency analysis and from an integrated approach for Power Spectral Density matrix computation, which constitute crucial innovative issues for the treatment of real earthquake response data. A monitoring case study is analysed by taking the real strong‐motion response records from a seven‐storey reinforced concrete building in Van Nuys, California, from 1987 to the latest 2014 events (Center of Engineering Strong Motion Data database), as recorded before, during and after the 1994 Northridge earthquake, which severely damaged the building (then retrofitted). This paper proves the effectiveness of the proposed enhanced rFDD algorithm as a robust method for monitoring current structural modal properties under real earthquake excitations. This shall allow for identifying possible variations of structural features along experienced seismic histories, providing then a fundamental tool towards Earthquake Engineering and Structural Health Monitoring purposes.

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification.

Summary Structural model updating aims at calculating the in‐situ structural properties (e.g., stiffness and mass) based on measured responses. One common approach is to first identify the modal parameters (i.e., natural frequencies and mode shapes) and then use them to update the structural parameters. In reality, the degrees of freedom that can be measured are usually limited by number of available sensors and accessibility of targeted measurement locations. Then, multiple setups are designed to cover all the degrees of freedom of interest and performed sequentially. Conventional methods do not account for identification uncertainty, which becomes critical when excitation information is not available. This is the situation in model updating utilizing ambient vibration data, in which the excitations, such as wind, traffic, and human activities, are random in nature and difficult to be measured. This paper develops a Bayesian model updating method incorporating modal identification information in multiple setups. Based on a recent fundamental two‐stage Bayesian formulation, the posterior uncertainty of modal parameters is incorporated into the updating process without heuristics that are commonly applied in formulating the likelihood function. Synthetic and experimental data are used to illustrate the proposed method.

The population-based structural health monitoring paradigm has recently emerged as a promising approach to enhance data-driven assessment of engineering structures by facilitating transfer learning between structures with some degree of similarity. In this work, we apply this concept to the automated modal identification of structural systems. We introduce a graph neural network (GNN)-based deep learning scheme to identify modal properties, including natural frequencies, damping ratios, and mode shapes of engineering structures based on the power spectral density of spatially sparse vibration measurements. Systematic numerical experiments are conducted to evaluate the proposed model, employing two distinct truss populations that possess similar topological characteristics but varying geometric (size and shape) and material (stiffness) properties. The results demonstrate that, once trained, the proposed GNN-based model can identify modal properties of unseen structures within the same structural population with good efficiency and acceptable accuracy, even in the presence of measurement noise and sparse measurement locations. The GNN-based model exhibits advantages over the classic frequency domain decomposition method in terms of identification speed, as well as against an alternate multilayer perceptron architecture in terms of identification accuracy, rendering this a promising tool for PBSHM purposes.

Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs.

The second-order blind identification (SOBI) and its variants have been extensively explored for output-only modal identification of civil structures under varied excitations. At the core of these methods is the matrix joint approximate diagonalization (JAD) technique, while their efficiency and accuracy are largely determined by how the target-matrices for JAD are constructed from multi-channel structural responses. This study first formulates the JAD framework for structural identification, where different techniques in formulating the target-matrices are summarized and mathematical tools to conduct JAD are also presented. Then two novel ways stemming from conventional identification methods are presented as alternatives to construct the target-matrices for ambient identification, to maintain a low-order formulation and even avoiding the formation of covariance matrix. Subsequently, in view of the large number of candidate target-matrices which are analytically usable, a guiding principle is proposed for selecting reliable target-matrices, where the closeness of the eigenvectors of the target-matrices are compared beforehand, therefore eliminating of distorted target-matrices and also improving the efficiency of the subsequent JAD. The proposed techniques are applied to modal identification of the Donghai Bridge from monitoring data and the proposed JAD-based methods are compared in this context. The results suggest the effectiveness of the proposed techniques and also provide a performance evaluation of these methods.

Purpose Modal identification of time-varying structures is a fundamental aspect in the field of structural dynamics, from which the identified modal parameters are considered as important indicators for damage identification and structural condition assessment in structural health monitoring. Therefor a nonparametric modal identification approach for time-varying structures from a data-driven perspective is investigated. Methods The inherent connection between dynamic mode decomposition and operational modal identification is revealed, where the modal identification is converted to solve the eigenvalues of dynamic matrix in dynamic mode decomposition. Furthermore, the full implementation procedures of the method for time-varying modal parameters extraction are provided, where the dynamic mode decomposition and sliding-window technique is effectively combined. Finally, a series of numerical examples are performed to validate the effectiveness and practicability of the method, where the dynamic modelling of simply supported beam with moving mass is derived and the classical methods based on signal decomposition for time-varying modal identification are used for comparison. Results The results from studied cases show that the proposed method has the capability for accurately identifying the instantaneous modal frequencies with good efficiency when the external excitation is unknown, showing promising prospects for engineering applications. Conclusion An online modal identification method for time-varying structures based on dynamic mode decomposition and sliding-window technique is presented without requiring establishing an accurate parameterized mathematical model. The proposed method can well identify instantaneous modal frequencies, and provides a more easily implementable framework with few adjustable parameters and lower computational resources and better identification performance compared with the VMD-HT and STMVMD method.