Damage assessment of structures using modal test data

Hu, Ning; Wang, X.; Fukunaga, H.; Yao, Zhenhan; Zhang, H.X.; Wu, Zhishen

doi:10.1016/s0020-7683(00)00292-4

Cited by 80 publications

(58 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results show that the method proposed in the paper is efficient and utilizes the benefits of both EA, as well as the regularization function by giving much better damage assessment. It can be seen that better results are obtained with the proposed approach as compared to other researchers using this example [16,45]. …”

Section: Experimental Beam (Case 3)mentioning

confidence: 62%

“…The first simulated beam is only damaged at one location, and the second simulated beam is damaged at more than one location to check the effectiveness of objective functions at various noise levels. For the third beam, experimental data are available for damage [45]. The effectiveness of the three objective functions is then evaluated on both simulated and experimental data.…”

Section: Damage Detection Case Studiesmentioning

confidence: 99%

“…The experimental beam was taken from Hu et al [45]. The aluminum beam has a rectangular section of 0.05 m × 0.006 m with two fixed ends.…”

Section: Experimental Beam (Case 3)mentioning

confidence: 99%

“…The experimental beam was taken from Hu et al [45]. Later on, model updating was again done with all three objective functions with regularization, and the results are given in Figure 15b.…”

Section: Experimental Beam (Case 3)mentioning

confidence: 99%

See 3 more Smart Citations

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

et al. 2017

View full text Add to dashboard Cite

Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.

show abstract

Section: Experimental Beam (Case 3)mentioning

confidence: 62%

Section: Damage Detection Case Studiesmentioning

confidence: 99%

“…The experimental beam was taken from Hu et al [45]. The aluminum beam has a rectangular section of 0.05 m × 0.006 m with two fixed ends.…”

Section: Experimental Beam (Case 3)mentioning

confidence: 99%

Section: Experimental Beam (Case 3)mentioning

confidence: 99%

See 2 more Smart Citations

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In this phase certain damage sensitive features, called damage index (DI), are extracted from the structure's response, either empirically obtained or numerically simulated, to measure its discrepancies from the response in the intact state. Previous studies show that the features which capture nonlinearities in the structural response are generally more sensitive to damage, less sensitive to environmental conditions, and hence, more reliable for the purpose of damage detection compared to the DIs that capture linear phenomena such as modal properties [4][5][6][7].Note that the source of nonlinearities can be material, geometry, or nonlinear dynamics phenomenon such as dispersion, mode mixing, and damping. The fractal dimension of the attractor of time-series is the basis for defining DIs in [8][9][10][11].…”

mentioning

confidence: 99%

Damage detection with small data set using energy-based nonlinear features

Ghazi

Büyüköztürk

2015

Struct. Control Health Monit.

View full text Add to dashboard Cite

This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy-based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection the proposed algorithm combines multiple damage sensitive features through a distance-based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance-based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm is experimentally validated by testing a steel laboratory prototype and the results show that the proposed method can effectively detect and localize the defects. KeywordsEnergy method, hypothesis testing, marginal Hilbert spectrum, normalized cumulative energy distribution, Mahalanobis distance, white noise excitation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 IntroductionVibration based structural health monitoring (SHM) is a widely used method for monitoring large scale, complex structures. Aging of infrastructures, higher operational demands, and variety of environmental effects on structural systems are the main reasons that attract more attention to this field in recent years.The algorithms for vibration-based SHM are either model-based or data-based. Both methods compare the response of the system with a baseline. In model-based approach, the baseline is provided using numerical models. Thus, this method is helpful for systems for which the model already exists and in cases where it is justifiable to build a sufficiently accurate structural model [1][2][3]. The data-based approach brings more flexibility to the damage detection scheme since it only uses the sensor data without having to deal with the complications of creating a model. The initial phase in both of these methodologies is feature extraction. In this phase certain damage sensitive features, called damage index (DI), are extracted from the structure's response, either empirically obtained or numerically simulated, to measure its discrepancies from the response in the intact state. Previous studies show that the features which capture nonlinearities in the structural response are generally more sensitive to damage, less sensitive to environmental conditions, and hence, more reliable for the purpose of damage detection compared to the DIs that capture linear phenomena such as modal properties [4][5][6][7].Note that the source of nonlinearities can be material, geometry, or nonlinear dynamics phenomenon such as dispersion, mode mixing, and damping. The fractal dimension of the attractor of time-series is the basis for defining DIs in [8][9][10][11]. Fractal analysis of residual crack patterns in reinforced concrete struct...

show abstract

Vibration‐Based Techniques for Structural Health Monitoring

Fritzen¹

2006

Structural Health Monitoring

View full text Add to dashboard Cite

Damage assessment of structures using modal test data

Cited by 80 publications

References 16 publications

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

Damage detection with small data set using energy-based nonlinear features

Vibration‐Based Techniques for Structural Health Monitoring

Contact Info

Product

Resources

About