A wave propagation and vibration-based approach for damage identification in structural components

2009 ◽  
Vol 322 (1-2) ◽  
pp. 167-183 ◽  
Author(s):  
Sauvik Banerjee ◽  
Fabrizio Ricci ◽  
Ernesto Monaco ◽  
Ajit Mal
2019 ◽  
Vol 11 (01) ◽  
pp. 1950003 ◽  
Author(s):  
Minzu Liang ◽  
Xiangyu Li ◽  
Yuliang Lin ◽  
Fangyun Lu

The propagation of compaction waves in layered cellular material subjected to air-blast is analyzed to examine the mechanism of compaction wave and reveal the phenomena that develop at the interface between the cellular layers. Similar to the previous studies of cellular materials under dynamic loading, the topology of cellular materials is neglected and homogeneous properties are assumed. The rigid-perfectly plastic-locking (R-PP-L) material idealization and the simple shock theory are employed to analyze the compaction situations. Analytical solutions for compaction wave propagation of double-layer cellular materials with two gradient-arrangements under air-blast loading have been worked out. The densification wave occurs at the blast end and then gradually propagates to the distal end for layers’ densities increase in the propagation direction (positive gradient). While compaction waves simultaneously form in both layers and propagate to the distal end in the same direction for the negative gradient. The finite element (FE) models using the Voronoi technique are carried out with practical aluminum foam to verify the predictions of the theoretical analysis. The potential of layered cellular materials to design efficient structural components under air-blast load is discussed, which would outperform their corresponding single counterpart with equal mass.


2012 ◽  
Vol 19 (3) ◽  
pp. 301-321 ◽  
Author(s):  
R.A. Tenenbaum ◽  
K.M. Fernandes ◽  
L.T. Stutz ◽  
A.J. Silva Neto

The formulation and solution of the inverse problem of damage identification based on wave propagation approach are presented. Different damage scenarios for a bar are considered. Time history responses, obtained from pulse-echo synthetic experiments, are used to identify damage position, severity and shape. In order to account for noise corrupted data, different levels of signal to noise ratio – varying from 30 to 0 dB – are introduced. In the identification process, different optimization methods are investigated: the deterministic Levenberg-Marquardt; the stochastic Particle Swarm Optimization; and a hybrid technique combining the aforementioned methods. It is shown that the damage identification procedure built on the wave propagation approach was successful, even for highly corrupted noisy data. Test case results are presented and a few comments on the advantages of deterministic and stochastic methods and their combination are also reported. Finally, an experimental validation of the sequential algebraic algorithm, used for modeling the direct problem, is presented.


Modelling ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 355-369
Author(s):  
Giao Vu ◽  
Jithender J. Timothy ◽  
Divya S. Singh ◽  
Leslie A. Saydak ◽  
Erik H. Saenger ◽  
...  

High costs for the repair of concrete structures can be prevented if damage at an early stage of degradation is detected and precautionary maintenance measures are applied. To this end, we use numerical wave propagation simulations to identify simulated damage in concrete using convolutional neural networks. Damage in concrete subjected to compression is modeled at the mesoscale using the discrete element method. Ultrasonic wave propagation simulation on the damaged concrete specimens is performed using the rotated staggered finite-difference grid method. The simulated ultrasonic signals are used to train a CNN-based classifier capable of classifying three different damage stages (microcrack initiation, microcrack growth and microcrack coalescence leading to macrocracks) with an overall accuracy of 77%. The performance of the classifier is improved by refining the dataset via an analysis of the averaged envelope of the signal. The classifier using the refined dataset has an overall accuracy of 90%.


2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Enrique García-Macías ◽  
Filippo Ubertini

AbstractOperational Modal Analysis (OMA) is becoming a mature and widespread technique for Structural Health Monitoring (SHM) of engineering structures. Nonetheless, while proved effective for global damage assessment, OMA-based techniques can hardly detect local damage with little effect upon the modal signatures of the system. In this context, recent research studies advocate for the use of wave propagation methods as complementary to OMA to achieve local damage identification capabilities. Specifically, promising results have been reported when applied to building-like structures, although the application of Seismic Interferometry to other structural typologies remains unexplored. In this light, this work proposes for the first time in the literature the use of ambient noise deconvolution interferometry (ANDI) to the structural assessment of long bridge structures. The proposed approach is exemplified with an application case study of a multi-span reinforced-concrete (RC) viaduct: the Chiaravalle viaduct in Marche Region, Italy. To this aim, ambient vibration tests were performed on February 4$$^{\text {th}}$$ th and 7$$^{\text {th}}$$ th 2020 to evaluate the lateral and longitudinal dynamic behaviour of the viaduct. The recorded ambient accelerations are exploited to identify the modal features and wave propagation properties of the viaduct by OMA and ANDI, respectively. Additionally, a numerical model of the bridge is constructed to interpret the experimentally identified waveforms, and used to illustrate the potentials of ANDI for the identification of local damage in the piers of the bridge. The presented results evidence that ANDI may offer features that are quite sensitive to damage in the bridge substructure, which are often hardly identifiable by OMA.


Author(s):  
Giao Vu ◽  
Jithender J. Timothy ◽  
Divya S. Singh ◽  
Leslie Saydak ◽  
Erik H. Saenger ◽  
...  

High costs for the repair of concrete structures can be prevented if damage at an early stage of degradation is detected and precautionary maintenance measures are applied. To this end, we use numerical wave propagation simulations to identify simulated damage in concrete using convolutional neural networks (CNN). Damage in concrete subjected to compression is modeled at the mesoscale using the discrete element method. Ultrasonic wave propagation simulation on the damaged concrete specimens are performed using the rotated staggered finite-difference grid method. The simulated ultrasonic signals are used to train a CNN based classifier capable of classifying three different damage stages (microcrack initiation, microcrack growth and microcrack coalescence leading to macrocracks). The performance of the classifier is improved by refining the dataset via an analysis of the averaged envelope of the signal. The classifier using the refined dataset has an overall accuracy of 90%.


2020 ◽  
Vol 20 (10) ◽  
pp. 2042008
Author(s):  
N. T. Le ◽  
A. Nguyen ◽  
D. P. Thambiratnam ◽  
T. H. T. Chan ◽  
T. Khuc

This paper presents an enhanced method to locate and quantify damage in beam-like structures using changes in deflections estimated from modal flexibility (MF) matrices. The method is developed from explicit relationship between a series of MF-based deflection change vectors and the damage characteristics. Based on this, three damage locating criteria are defined and used to detect and locate damage. Once the damage is located, its severity is estimated conveniently from a closed-form function. The capability of the proposed method is examined through numerical and experimental verifications on a steel beam model. The result shows that the method accurately locates and quantifies damage under various scenarios using a few modes of vibration, with satisfactory or even better results compared to those obtained from traditional static deflection-based method. The performance of the proposed method is also compared with three well-known vibration-based damage detection methods using changes in MF and modal strain energy. It is found that the proposed method outperformed the other three methods, especially for multiple damage cases. As beams can represent various structural components, the proposed method provides a promising damage identification tool targeting the application to real-life structures.


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