Abstract
In situ damage detection and localization using real acceleration structural health monitoring technique are the main idea of this study. The statistical and model identification time series, the response spectra, and the power density of the frequency domain are used to detect the behavior of Yonghe cable-stayed bridge during the healthy and damage states. The benchmark problem is used to detect the damage localization of the bridge during its working time. The assessment of the structural health monitoring and damage analysis concluded that (1) the kurtosis statistical moment can be used as an indicator for damage especially with increasing its percentage of change as the damage should occur; (2) the percentage of change of the Kernel density probability for the model identification error estimation can detect and localize the damage; (3) the simplified spectrum of the acceleration-displacement responses and frequencies probability changes are good tools for detection and localization of the one-line bridge damage.
Highlights
Structural health monitoring (SHM) systems are important in assessing various forms of bridges, especially long-span bridges damage detection and safety evaluation
This study demonstrates that simple and effective damage detection and localization algorithms based on a pattern classification framework can detect structural changes using the data that was collected from a real structure
The acceleration observation of the Yonghe bridge health monitoring system is used in this study to represent four methods that can be used to detect and localize the damage, which are the statistical moment, the model identification in time domain, the power spectrum, and the response spectra in frequency domain
Summary
Structural health monitoring (SHM) systems are important in assessing various forms of bridges, especially long-span bridges damage detection and safety evaluation. The assessment of long-span bridges during working process is a main advantage of SHM, while studying the effect of environmental and traffic loads cannot be controlled or measured [3, 4]. Li et al [3] noted that the vibration application damage detection for engineering structures is strongly affected by some factors, namely, variations in material properties, environmental variability (such as temperature, wind velocity, and humidity), variability in operational conditions (such as traffic flow) during measurement, and errors associated with measured datasets and processing techniques. SHM of bridges with monitoring loads effects is a good tool to measure and assess the behavior of bridges. The early damage detection is one of the advantages of SHM, while the vibration acceleration measurements are sufficient to detect damage and localization [4]
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