Structural health monitoring of progressive damage

Mollineaux, Mark; Rajagopal, Ram

doi:10.1002/eqe.2562

Cited by 7 publications

(2 citation statements)

References 33 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Das et al [1] conducted a comparative study of the effectiveness of different vibration-based damage identification methods and proved that time series analysis outperformed other methods in damage identification with the presence of operational or environmental nuisances. Furthermore, they have been successfully incorporated with SVM [29], hidden Markov model (HMM) [30], and ANNs [7,22,23,31] to identify damage. To avoid false diagnoses, an approach combining sensor-clustering-based time-series analysis with the ANN, which was proposed by Kostić and Gül [22], could successfully determine the existence, location, and relative severity of damage for a footbridge finiteelement model under temperature variations.…”

Section: Damage Identification Under Varying Temperature Effectsmentioning

confidence: 99%

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Huang

Zhao

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

The effect of varying temperatures is one of the most important challenges of vibration-based damage identification due to its bigger effects on the structural response than the damage itself. This study presents a methodology incorporating the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. AR coefficients, which are extracted by fitting the AR models to acceleration responses, are however sensitive to temperature changes, resulting in false diagnoses. Thus, two-step ANN models with the inputs of difference in AR coefficients are utilized to compensate the detrimental temperature variations. Finite element (FE) models of a steel-braced frame structure, simulating several damage scenarios with different damage locations and severities at fluctuating temperatures, are used to verify the effectiveness and reliability of this approach. Numerical results indicate that the proposed approach could successfully recognize, locate, and quantify damage by using output-only vibration and temperature data regardless of varying temperatures and noise perturbations.

show abstract

Section: Damage Identification Under Varying Temperature Effectsmentioning

confidence: 99%

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Huang

Zhao

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…(2015) investigated normalized curvature difference of waveform jerk energy and they were able to identify the location of damage. In a different study (Mollineaux and Rajagopal, 2015), an autoregressive model integrated with a hidden Markov model was used to determine the probabilistic progressive damage of a structure. Bagheri et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of progressive damage in structures using tensor decomposition-based wavelet analysis

Sadhu

Sony

Friesen³

2019

Journal of Vibration and Control

View full text Add to dashboard Cite

Effective monitoring and retrofitting of large-scale infrastructure subjected to natural hazards such as strong wind, severe earthquakes or man-made excitation are critical to ensure structural integrity and prevent any premature failure. With the aid of structural health monitoring, it is now possible to acquire rich vibration data, estimate the hidden structural information, and evaluate the existing structural performance. The nonstationary component of vibration response resulting from natural hazards poses difficulty in analysis using traditional modal identification methods that are based on the stationarity assumption of vibration response. Apart from the excitation-induced nonstationarity, inherent damages in the structure also cause frequency-dependent nonstationarity in the response. With such a combination of both amplitude and frequency-dependent nonstationary response, the modal identification becomes a significantly challenging task. In this paper, Cauchy continuous wavelet transform is integrated with the tensor decomposition to track time-varying characteristics of modal responses and detect any progressive damage. The proposed technique is validated using a suite of numerical studies as well as a laboratory experiment where the progressive damage is simulated in the members by heating them using a butane torch. Unlike detection of discrete damage, the proposed method is one of introductory approaches to assess progressive damage in structures.

show abstract

Bayesian Changepoint Modelling for Reference-Free Damage Detection with Acoustic Emission Data

Scott

Jones

Rogers

2022

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Structural health monitoring of progressive damage

Cited by 7 publications

References 33 publications

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Evaluation of progressive damage in structures using tensor decomposition-based wavelet analysis

Bayesian Changepoint Modelling for Reference-Free Damage Detection with Acoustic Emission Data

Contact Info

Product

Resources

About