Application of random decrement technique and Bhattacharyya measure to damage detection under environmental and operational variability

Document Type : Original Research

Authors
F. Jalalifar 1
F. Shahabian Moghadam 2
M. R. Esfahani 2
1 PhD student, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad
2 Professor, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad
Abstract
Damage detection is a necessary part for structural health monitoring (SHM), being beneficial to SHM and determining the severity of damage. Application of statistical pattern recognition methods for SHM has gained considerable attention to detect changes in a structure. One of the advantages of these methods is that only data from undamaged state is needed in training phase (unsupervised learning) as opposed to supervised learning where data from both undamaged and damaged conditions is required to train the model. There are different approaches used by researchers and the success of a certain one may depend on the type of structure or structural changes. Most of studies focused on the application of statistical pattern recognition methodologies for SHM utilize the time series analyses for extracting damage-sensitive features. These features are statistical properties of time series models that directly depend on damage. Extracting damage-sensitive features is a fundamental step in damage detection process because pattern recognition algorithms can identify the state of structure unless these features are just dependent on damage. The change in an environmental and operational condition during the data acquisition process is one of the problems that causes damage features to be depended on factors besides existence of damage. This can lead to incorrect structural state identification. On the other hand, after extracting damage-sensitive features, the application of a statistical novelty detection methodology for decision making on structural state is a significant topic in SHM.

This paper proposes a new application of random decrement (RD) technique in order to choose appropriate and accurate damage features which are independent from environmental and operational conditions of structure. The RD technique transforms time series data of the structural response to free decay vibration form that only consist of dynamic properties information by averaging them at specific time. Moreover, a novel statistical method named as Bhattacharyya measure is applied as a robust method for damage diagnosis. The Bhattacharyya measure determines the discrepancy between damage features from different structural states through partitioning data and utilizing numerical information of each partition. Herein, before extracting damage features, time series data are averaged through RD technique. Then the Autoregressive-Autoregressive with exogenous output model (AR-ARX) is used to fit a mathematical model to the averaged time series data and the residuals are considered as damage features. The Bhattacharyya measure is utilized for damage identification and localization. The data obtained from an experimental study on a three-story frame structure model are exploited to validate the accuracy and reliability of the proposed algorithm. Random excitation is applied by varying amplitude level of the input force, simulating various environmental and operational conditions. Damage is induced at two different locations. The proposed algorithm is conducted on data from various environmental and operational conditions at two different locations. A comparative study is also carried out to demonstrate the superiority of the proposed algorithm over some exiting techniques. Results show that the application of random decrement technique reduces the influence of operational and environmental condition due to averaging and normalizing data and correctly determines the state of structure. In addition, using Bhattacharyya measure improves the structural health monitoring results in damage identification and localization.

Keywords

Subjects

[1] Avci O., Abdeljaber O., Kiranyaz S., Hussein M., Gabbouj M., Inman D. 2021. A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications. Mechanical Systems and Signal Processing, 147.
[2] Amezquita-Sanchez J., Adeli H. 2015 Feature extraction and classification techniques for health monitoring of structures. Journal of Sciences, 22(6), 1931-1940.
[3] Qarib H., Adeli H. 2014 Recent advances in health monitoring of civil structures. Journal of Sciences, 21(6), 1733-1742.
[4] Noman A.S., Deeba F. and Bagchi A. 2013 Health Monitoring of Structures Using Statistical Pattern Recognition Techniques. Journal of Performance of Constructed Facilities, 27(5), 575-584.
[6] Yao R. & Pakzad S.N. 2012 Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mechanical Systems and Signal Processing, 31, 355-368.
[7] Mosavi A.A., Dickey D., Seracino R. & Rizkalla S. 2012 Identifying Damage Locations under Ambient Vibrations Utilizing Vector Autoregressive Models and Mahalanobis Distances. Mechanical Systems and Signal Processing, 26, 254–267.
[8] Yeager M., Gregory B. & Key C. 2018 on using robust Mahalanobis distance estimations for feature discrimination in a damage detection scenario. Structural Health Monitoring, 18(1), 245–253.
[9] Wu C., Li S., Zhang Y. 2019 Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution. Sensors, 19(19).
[10] Sohn, H. & Farrar, C. R. 2001 Damage Diagnosis Using Time Series Analysis of Vibration Signals. Smart Materials and Structures, 10(3), 446–452.
[11] Zhang Q.W. 2007 Statistical damage identification for bridges using ambient vibration data. Computers and Structures, 85 (7–8), 476–485.
[12] Gul, M. & Catbas, F. N. 2011 Structural Health Monitoring and Damage Assessment Using a Novel Time Series Analysis Methodology with Sensor Clustering. Journal of Sound and Vibration, 330(6), 1196 – 1210.
[13] Roy K., Bhattacharya B., Ray-Chaudhuri S. 2015 ARX model-based damage sensitive features for structural damage localization using output-only measurements. Journal of Sound Vibration, 349, 99–122.
[14] Farahani R., Penumadu D., 2015 Full-scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight. Structural Control and Health Monitoring, 23(7).
[15] Entezami A., Shariatmadar H. 2019 Structural health monitoring by a new hybrid feature extraction and dynamic time warping methods under ambient vibration and non-stationary signals. Measurement, 134, 548–568.
[16] Wijesundara, K. K., Negulescu, C., Foerster, E. 2015 Estimation of Modal Properties of Low-Rise Buildings Using Ambient Excitation Measurements. Shock and Vibration.
[17] Reynolds Th., Casagrande D., Tomasi R. 2016 Comparison of multi-storey cross-laminated timber and timber frame buildings by in situ modal analysis, Construction and Building Materials, 102(2), 1009-1017.
[18] Wen Q., Hua X. G., Chen Z. Q., Niu H. W., Wang X. Y. 2018 AMD-Based Random Decrement Technique for Modal Identification of Structures with Close Modes. Journal of Aerospace Engineering, 31(50).
[19] Gul M & Catbas FN. 2011 Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. Journal of Sound Vibration, 330, 1196–1210.
[20] Entezami A., Shariatmadar H. 2015 New application of random decrement technique to structural health monitoring using control chart. In 5th international conference on acoustic and vibration.
[21] Cole H. A. 1973 On-line Failure Detection and Damping Measurements of Aerospace Structures by Random Decrement Signature. NASA CR-2205.
[22] Rodrigues J., Rune Brincker P. 2005 Application of the random decrement technique in operational modal analysis. Proceedings of the 1st International Operational Modal Analysis Conference, 191-200.
[23] Ohtsu K., Peng H., Kitagawa G. 2015. Time Series Modeling for Analysis and Control. Springer Japan.
[24] Derpanis K. g. 2008. The Bhattacharyya Measure, Mendeley Computer, 1.1990-1992.
[25] Fasel T. R., Sohn H., Farrar C. R. 2003 Application of frequency domain ARX models and extreme value statistics to damage detection. Engineering Sciences & Applications Division, Los Alamos National Laboratory, NM 87545.
[26] Kullaa, J. 2014. Benchmark Data for Structural Health Monitoring. 7th European Workshop on Structural Health Monitoring, France.
Modares Civil Engineering journal
Volume 20, Issue 6
November and December 2020
Pages 115-126