Damage Assessment Using Vibration Analysis on the Z24-Bridge

Maeck, Johan; Roeck, Guido De

doi:10.1006/mssp.2002.1550

Cited by 67 publications

(14 citation statements)

References 3 publications

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“…Solution procedure and experimental validation results of direct stiffness calculation technique are reported and discussed in [1]. More can be found on http:// www.kuleuven.ac.be/bwm/SIMCES.htm.…”

Section: Introductionmentioning

confidence: 97%

Description of Z24 Benchmark

Maeck

Roeck

2003

Mechanical Systems and Signal Processing

Self Cite

109

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Section: Introductionmentioning

confidence: 97%

Description of Z24 Benchmark

Maeck

Roeck

2003

Mechanical Systems and Signal Processing

Self Cite

109

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“…1-5. More study efforts had been implemented based on frequency change-based method 6,7 , mode shape change-based method [8][9][10] , frequency and mode shape change-based method 11 , mode shape curvature 12,13 , modal strain enery 14,15 , flexibility based approach 16,17 , and frequency response function [18][19] .…”

Section: Introductionmentioning

confidence: 99%

Multi-stage method to identify structural damage using multiple damage location assurance criterion and an improved differential evolution algorithm

Wu¹,

Liu²,

Qin³

et al. 2017

IJCIS

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For the damage identification technique of civil structures, the reduction of the computational cost for methods based on the optimization algorithms is the most crucial step. In this study, a fast multi-stage method is developed that uses the multiple damage location assurance criterion and an improved differential evolution algorithm. In the new method, the suitable damage range is selected in different stages to reduce the computational cost of structural analysis. Five mutation operators are analysized not only in the basic differential evolution algorithm but also in the improved one. A new adaptive scaling factor with a segmented function is proposed which can operate the decay rate to avoid the premature phenomenon. The results of the study show that the precise locations and extents of structural damage are successfully realized. It is also shown that the multi-stage method using the improved differential evolution algorithm is substantially faster as compared to the basic one and can be used as an efficient and powerful measure of structural damage identification.

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“…Posenato et al [9,18] proposed two model-free data interpretation methods, MPCA and RRA, to detect and localize anomalous behavior for the particular context of civil-engineering structures, and compared their performance with many other methods: DWT [12][13], ARIMA [14], auto regressive with moving average [19][20][21], Box-Jenkins method [13], wavelet packet transform [22][23], instance based method [24] and correlation anomaly scores analysis [25]. These comparative studies demonstrated that the performances of (MPCA) [9] and (RRA) [26][27][28] for anomaly detection were superior to other methods when dealing with civil-engineering challenges such as high noise levels, missing data and outliers. Both methods were also observed to require low computational resources to detect anomalies, even when there were large quantities of data.…”

Section: Introductionmentioning

confidence: 99%

Evaluating two model-free data interpretation methods for measurements that are influenced by temperature

Laory

Trinh

Smith

2011

Advanced Engineering Informatics

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Interpreting measurement data to extract meaningful information for damage detection is a challenge for continuous monitoring of structures. This paper presents an evaluation of two model-free data interpretation methods that have previously been identified to be attractive for applications in structural engineering: moving principal component analysis (MPCA) and robust regression analysis (RRA). The effects of three factors are evaluated: (a) sensor-damage location, (b) traffic loading intensity and (c) damage level, using two criteria: damage detectability and the time to damage detection. In addition, the effects of these three factors are studied for the first time in situations with and without removing seasonal variations through use of a moving average filter and an ideal low-pass filter. For this purpose, a parametric study is performed using a numerical model of a railway truss bridge. Results show that MPCA has higher damage detectability than RRA. On the other hand, RRA detects damages faster than MPCA. Seasonal variation removal reduces the time to damage detection of MPCA in some cases while the benefits are consistently modest for RRA.Keywords: Moving principal component analysis; robust regression analysis; damage detection; damage detectability; time to damage detection; seasonal temperature variation.Evaluating two model-free data interpretation methods for measurements that are influenced by temperature IntroductionRecently, the collapse of civil structures such as the I-35W bridge (USA, 2007) [1-2] and the Paris Airport (France, 2004) [3] has decreased public confidence in the safety of structures.Thus, to ensure that structures behave according to design criteria, it is useful to monitor their performance. Monitoring for possible damage is referred to as structural health monitoring (SHM). Due to advances in sensor technology, data acquisition systems and computational power, the number of structures that are monitored is growing. Thus, large quantities of measurement data are retrieved every day and much more will be available in the future.Extracting useful information from this data to detect damage is a challenge for SHM. This task is even more difficult when measurement data are influenced by environmental variations, such as temperature, wind and humidity. Brownjohn et al [4] studied the thermal effects on performance on Tamar Bridge and showed that thermal effects dominate the measured bridge behaviour. Catbas et al. [5] observed that the peak-to-peak strain differential due to temperature over a one-year period is more than ten times higher than the strain due to observed maximum daily traffic.Generally, there are two classes of data interpretation methods in SHM: model-based methods and model-free methods. These two classes are complementary since they are appropriate in different contexts. Strengths and weaknesses of both classes have been summarized in the ASCE state-of-the-art report on structural identification of constructed systems [6].Model-based data interpretation...

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Damage Assessment Using Vibration Analysis on the Z24-Bridge

Cited by 67 publications

References 3 publications

Description of Z24 Benchmark

Description of Z24 Benchmark

Multi-stage method to identify structural damage using multiple damage location assurance criterion and an improved differential evolution algorithm

Evaluating two model-free data interpretation methods for measurements that are influenced by temperature

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