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Traditional algorithms for the identification of dynamic parameters based on vibrational structural response generally involve strict assumptions about stationarity and may not be suitable for time-varying systems. Therefore, there is a tendency to discard the measurements of the structural response collected during short-time events, such as train passages on bridges. The response to a strong event may however include valuable information about structural features that cannot be observed in low-excitation scenarios. Moreover, a strong excitation typically generates a higher-amplitude structural response, which is particularly convenient when using low-cost instrumentation for data collection that is usually characterized by low sensitivity. In this paper, dynamic parameters of bridges during the passage of trains are identified by fitting the structural response to a simplified analytical model specialized for railway applications. The efficacy of a damage index based on the fitting residuals is investigated using a numerical study, simulating the use of an event-triggered smart sensing system. Applying the proposed method, damage detection can be achieved using a single low-cost sensing node.

Detecting early damage in civil structures is highly desirable. In the area of vibration-based damage detection, modal flexibility (MF)-based methods have proven to be promising tools for promptly identifying changes in the global structural behavior. Many of these methods have been developed for specific types of structures, giving rise to different approaches and damage-sensitive features (DSFs). Although structural type classification is an important part of the damage detection process, this part of the process has received little attention in most literature and often relies on the use of a-priori engineering knowledge. Moreover, in general, experimental validations are only performed on small-scale laboratory structures with controlled artificial damage (e.g., imposed stiffness reductions). This paper proposes data-driven criteria for structure-type classification usable in the framework of MF-based damage identification methods to select the most appropriate algorithms and DSFs for detecting and localizing structural anomalies. This paper also tests the applicability of the proposed classification criteria and the damage identification methods on full-scale reinforced concrete (RC) structures that have experienced earthquake-induced damage. The considered structures are a seven-story RC wall building and a five-story RC frame building, which were both tested on the large-scale University of California, San Diego-Network for Earthquake Engineering Simulation (UCSD-NEES) shaking table.

Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identification of dynamic and dense quasi-static structural features exploiting moving vehicles is proposed. Filtering raw acceleration structural responses, triggered by passing vehicles, enables the identification of modal parameters and curvature influence lines. The procedure can be implemented efficiently as its main computational core consists of convolutions. The definition of a curvature-based damage index leads to accurate localization and quantification of structural anomalies using few sensors. The proposed procedure is tested on three viaducts of the Italian A24 motorway. Moreover, a numerical model is studied to evaluate the potentialities of the strategy for damage localization.