Damage indices evaluation for one-dimensional guided wave-based structural health monitoring

Abstract

Damage detection in structures and systems is essential for monitoring parameters that can affect their integrity. This paper evaluates the efficiency of four damage indices (DIs) commonly used with temporal wave signals. The root mean square deviation, mean absolute percentage deviation, covariance, and correlation coefficient deviation DIs are presented, and a normalization is then proposed. An Euler-Bernoulli beam is used as a guided wave modelled with the spectral element method and excited by a toneburst signal. It includes the theoretical background of the throw-off beam, undamaged and cracked beam spectral elements. The DIs for a single crack position and a map varying crack depth and positions are calculated with deterministic and random temporal signal responses derived from noise addition. Results showed that DIs could identify and quantify the damage conditioned to the pulse location point and the influence of noise in the estimation, which leads in an analysis comparable to practical applications.

An improved Artificial Rabbit Optimization for structural damage identification

“…Structural Health Monitoring (SHM) represents a stimulating field dedicated to the ongoing assessment of structural conditions in entities like bridges, buildings, and infrastructure, with the primary aim of guaranteeing their safety and dependability (Barreto et al, 2021;Jahangiri et al, 2016;Marques et al, 2018). In recent years, notable progress has been made in advancing SHM systems and technologies, driven by the growing imperative of infrastructure maintenance and safety.…”

Experimental vibration dataset collected of a beam reinforced with masses under different health conditions

“…However, establishing techniques that comprehensively cover all these aspects simultaneously within a single monitoring algorithm, exhibiting strong generalisation abilities for multi-task monitoring using vibration-based signals, remains a significant challenge. Additionally, the inherent variabilities and uncertainties within the system further compound the complexity of the structural health monitoring process [ 5 , 6 ]. To overcome certain monitoring processes, the present dataset has been acquired to support and evaluate the methodologies proposed in [ 1 , 3 , 4 ] and structural health monitoring techniques based on vibration signature.…”

Damage identification of thin plate-like structures combining improved singular spectrum analysis and multiscale cross-sample entropy (ISSA-MCSEn)

“…After the signal related to structural damages is obtained, how to use it to evaluate the severity of the damage is another urgent issue. Some statistical parameters of the signal, such as root mean square deviation [34,35], correlation coefficient deviation [36,37], and correlation coefficient deviation measure [25], are widely used to qualify the size of the damage. These statistical parameters are sensitive to the signal which contains enough structural dynamics of the damages, and therefore they are just effective to damages with a relative large size.…”