Damage detection in frame structures using noisy accelerometers and Damage Load Vectors (DLV)

Abstract

In the domain of damage detection, many notable methods have been introduced over the past years. Damage Load Vectors (DLV) is among the most powerful methods, which computes a set of load vectors from variations in exibility matrices of a frame in undamaged and damaged conditions. These exibility matrices are derived from acceleration responses of the frame that can be captured using accelerometers. The DLV method then scrutinizes this shift among exibility matrices, which ultimately enables locating the damaged member(s). This study holistically conducted seven experimental tests with seven damage scenarios of a test frame installed on a semi-harmonic shaking table. The DLV method was subsequently employed to locate the damaged members using recorded frame vibration data that were obtained from`noisy' accelerometers positioned on the frame at eight prede ned locations. The Eigen Realization Algorithm (ERA) alongside Pandy's recommendations was adopted herein to facilitate the generation of accurate exibility matrices derived from the noisy accelerometers. The outcome is very encouraging with the accurate identi cation of damaged members in all seven damage scenarios without any`positive-false' and`negative-false' ndings. Additionally, there is a decrease (from 0.045 to 0.289) in the accuracy of Weighted Stress Indices (WSI) index when the number of damaged members is increased.

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

“…Obrien et al [32] have used the response of vehicle axle force as information to detect the existence of damage in bridge structure. Toloue et al [33] have carried out an experimental work to develop a damage detection procedure by using the noisy accelerometers and damage load vectors in a three dimensional framed structures. He et al [34] have applied the mode shape curvature concepts as damage localizing method for vibrating structure.…”

Numerical Modeling of Lyapunov Exponents for Structural Damage Identification

“…Now that the methodology has shown itself capable of verifying the structural integrity, the practical use with experimental data is an interesting application, remembering that, as explained in the introduction, one can use data collected from accelerometers installed in real structures. As an example, Toloue et al [38] used accelerometers to assess the damage of aluminum spatial frames. The authors used the damage load vectors (DLV) method in association with the application of the Markov matrix to the noisy accelerometer data, enabling the exclusive inclusion of the most useful signals.…”

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

“…T  E X I PP (10) In this equation, 𝐗 ̅ denotes the projection of the matrix X onto the selected k principal components and E is the projection onto the residual left components.…”

“…The central idea of SHM via this strategy lies in the fact that the numerical (updated) and real models of the structure are considered as undamaged and current states. Hence, it is attempted to use information of both models to define a damage equation as an inverse problem and solve it via various mathematical and optimization techniques [8][9][10][11]. By contrast, the data-based methods only utilize measured vibration signals without any FE modeling or updating procedures, and any data transformation from raw time domain into frequency or modal domains.…”