Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Abstract

In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

Research on Quantification of Structural Natural Frequency Uncertainty and Finite Element Model Updating Based on Gaussian Processes

“…The natural frequency of bridges is a crucial dynamic response indicator that is easily testable and can reflect changes in structural stiffness. Damage inevitably results in frequency variation [1,2]. It is commonly utilized in the fields of structural model updating and damage identification [3][4][5][6].…”

Mass and Stiffness Correlation Using a Transformation Matrix

“…The varying weather conditions (temperature, humidity, wind speed and direction, water content of the soil, etc.) can modify the experimental modal parameters of a structure [38,39]. These effects must be removed when using correlation and model updating techniques, as well as with damage detection and localization methods.…”

“…Damage can be detected and localized from modal parameter (natural frequencies, mode shapes, modal curvature) variations estimated from acceleration responses [39]. However, damage assessment from experimental data is also highly susceptible to uncertainty, due to the difficulty in distinguishing between variations of modal parameters due to environmental conditions or the presence of damage [38].…”