Araştırma Makalesi
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Uncertainty quantification of damage detection results for a plane frame model

Yıl 2024, , 188 - 195, 15.01.2024
https://doi.org/10.28948/ngumuh.1361253

Öz

In this study, uncertainty quantification was carried out on the damage detection results obtained through the sensitivity-based finite element model updating method for a two-story, single-span plane frame model, where damage was simulated as a regional stiffness reduction at the ends of columns and beams. The damage detection method employed is based on iteratively reducing the differences in modal parameters between damaged and undamaged conditions. The presence of noise in dynamic measurements introduces uncertainties in damage detection results by affecting the estimation of modal parameters. The reliability of the obtained results is possible by quantifying the amount of uncertainty they encompass. In the presented study, uncertainty quantification of damage detection results was carried out by adding noise data associated with different coefficient of variations to modal parameters related to the damaged condition. The model updating process was performed by using modal parameters with added noise data for the damaged condition and modal parameters for the undamaged condition, as well as by updating the element stiffness values of the finite element model representing the undamaged condition. Stiffness reductions of elements were calculated using the determined stiffness values before and after updating, thus enabling the detection of the location and extent of damage. Uncertainty quantification of the damage results obtained under different noise conditions was conducted using analysis of variance, meta-modeling, and sensitivity analysis methods. In this way, factors that introduce substantial uncertainty in damage detection results have been identified.

Kaynakça

  • M. I. Friswell and J. E. Mottershead, Finite Element Model Updating in Structural Dynamics. Springer Science+Business Media, Dordrecht, 1995.
  • J. Nocedal and S. J. Wright, Numerical Optimization. Springer, New York, 1999.
  • A. Teughels, Inverse modelling of civil engineering structures based on operational modal data. Ph.D. Thesis, Katholieke University, Leuven, Belgium, 2003.
  • H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, B. R. Nadler, and J. J. Czarnecki, A Review of Structural Health Monitoring Literature: 1996-2001. Los Alamos National Laboratory, New Mexico, USA, Technical Report LA-13976-MS, 2003.
  • A. Teughels and G. De Roeck, Structural damage identification of the highway bridge Z24 by FE model updating. Journal of Sound and Vibration, 278 (3), 589-610, 2004. https://doi.org/10.1016/j.jsv.2003.10.041
  • E. P. Carden and P. Fanning, Vibration based condition monitoring: a review. Structural Health Monitoring, 3 (4), 355-377, 2004. https://doi.org/ 10.1177/1475921704047500
  • J. M. Brownjohn, Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365 (1851), 589-622, 2007. https://doi.org/10.1098/rsta.2006.1925
  • B. Moaveni, J. P. Conte, and F. M. Hemez, Uncertainty and sensitivity analysis of damage identification results obtained using finite element model updating. Computer-Aided Civil and Infrastructure Engineering, 24 (5), 320-334, 2009. https://doi.org/10.1111/j.1467-8667.2008.00589.x
  • C. Boller, F. K. Chang, and Y. Fujino, Encyclopedia of Structural Health Monitoring. Wiley, West Sussex, 2009.
  • T. Marwala, Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Springer, London, 2010.
  • J. E. Mottershead, M. Link, and M. I. Friswell, The sensitivity method in finite element model updating: a tutorial. Mechanical Systems and Signal Processing, 25 (7), 2275-2296, 2011. https://doi.org/10.1016/ j.ymssp.2010.10.012
  • W. Fan and P. Qiao, Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 10 (1), 83-111, 2011. https://doi.org/10.1177/1475921710365419
  • E. Simoen, B. Moaveni, J. P. Conte, and G. Lombaert, Uncertainty quantification in the assessment of progressive damage in a 7-story full-scale building slice. Journal of Engineering Mechanics, 139 (12), 1818-1830, 2013. https://doi.org/10.1061/ (ASCE)EM.19437889.0000610
  • A. Nozari, I. Behmanesh, S. Yousefianmoghadam, B. Moaveni, and A. Stavridis, Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building. Engineering Structures, 151, 540-553, 2017. https://doi.org/ 10.1016/j.engstruct.2017.08.044
  • M. C. Kennedy and A. O'Hagan, Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B, 63 (3), 425-464, 2001. https://doi.org/10.1111/1467-9868.00294
  • W. E. Walker, P. Harremoës, J. Rotmans, J. P. Van Der Sluijs, M. B. Van Asselt, P. Janssen, and M. P. Krayer von Krauss, Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4 (1), 5-17, 2003. https://doi.org/10.1076/iaij.4.1.5.16466
  • A. Der Kiureghian and O. Ditlevsen, Aleatory or epistemic? Does it matter?. Structural Safety, 31 (2), 105-112, 2009. https://doi.org/10.1016/ j.strusafe.2008.06.020
  • C. Soize, Generalized probabilistic approach of uncertainties in computational dynamics using random matrices and polynomial chaos decompositions. International Journal for Numerical Methods in Engineering, 81 (8), 939-970, 2010. https://doi.org/10.1002/nme.2712
  • E. Simoen, G. De Roeck, and G. Lombaert, Dealing with uncertainty in model updating for damage assessment: a review. Mechanical Systems and Signal Processing, 56-57, 123-149, 2015. https://doi.org/10.1016/j.ymssp.2014.11.001
  • T. Kernicky, M. Whelan, and E. Al-Shaer, Vibration-based damage detection with uncertainty quantification by structural identification using nonlinear constraint satisfaction with interval arithmetic. Structural Health Monitoring, 18 (5-6), 1569-1589, 2019. https://doi.org/10.1177/1475921718806476
  • Y. Huang, C. Shao, B. Wu, J. L. Beck, and H. Li, State-of-the-art review on Bayesian inference in structural system identification and damage assessment. Advances in Structural Engineering, 22 (6), 1329-1351, 2019. https://doi.org/10.1177/1369433218811540
  • E. Silva, C. Magluta, N. Roitman, and L. Aragoa Filho, Development of a structural identification methodology with uncertainty quantification through the SSI and bootstrap techniques. Mechanical Systems and Signal Processing,165, 108290, 2022. https://doi.org/10.1016/j.ymssp.2021.108290
  • Z. Yin, Z. R. Lu, J. Liu, and L. Wang, Quantifying uncertainty for structural damage identification in the presence of model errors from a deterministic sensitivity-based regime. Engineering Structures, 267, 114685, 2022. https://doi.org/10.1016/ j.engstruct.2022.114685
  • D. Xie, Z. R. Lu, G. Li, J. Liu, and L. Wang, Efficient Laplace prior-based sparse Bayesian learning for structural damage identification and uncertainty quantification. Mechanical Systems and Signal Processing, 188, 110000, 2023. https://doi.org/10.1016/j.ymssp.2022.110000
  • N. E. Silionis and K. N. Anyfantis, Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications. Probabilistic Engineering Mechanics, 71, 103412, 2023. https://doi.org/10.1016/ j.probengmech.2023.103412
  • MATLAB. The MathWorks Inc., Massachusetts, USA, 2023.
  • F. C. Filippou and M. Constantinides, FEDEASLab getting started guide and simulation examples. http://www.neesgrid.org/news/documents.php, Accessed 31 August 2004.
  • E. Durmazgezer, Modal parameter estimation and damage identification on progressively damaged r/c frames. Ph.D. Thesis, Dokuz Eylül University, İzmir, Turkey, 2019.
  • E. Durmazgezer, U. Yucel, and O. Ozcelik, Damage identification of a reinforced concrete frame at increasing damage levels by sensitivity-based finite element model updating. Bulletin of Earthquake Engineering, 17 (11), 6041-6060, 2019. https://doi.org/10.1007/s10518-019-00690-5
  • U. Yucel, Finite element model updating and damage identification of reinforced concrete frames with different infills and unreinforced masonry walls. Ph.D. Thesis, Dokuz Eylül University, İzmir, Turkey, 2020.
  • R. H. Myers and D. C. Montgomery, Response Surface Methodology. John Wiley & Sons, New York, 1995.
  • A. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis. John Wiley & Sons, New York, 2000.
  • C. F. J. Wu and M. Hamada, Experiments: Planning, Analysis, and Parameter Design Optimization. John Wiley & Sons, New York, 2000.
  • R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists. Prentice Hall, Upper Saddle River, 2006
  • D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers. Wiley and Sons, New York, 2007.
  • W. C. Navidi, Statistics for Engineers and Scientists. McGraw-Hill, Boston, 2007.

Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü

Yıl 2024, , 188 - 195, 15.01.2024
https://doi.org/10.28948/ngumuh.1361253

Öz

Bu çalışmada, hasarın kolon ve kiriş uçlarında bölgesel rijitlik azalması olarak simüle edildiği, iki katlı, tek açıklıklı düzlem çerçeve modelinin duyarlık tabanlı sonlu elemanlar modeli güncellemesi yöntemiyle elde edilen hasar tespit sonuçlarının belirsizlik ölçümüne yer verilmiştir. Kullanılan hasar tespit yöntemi, hasarlı ve hasarsız durumlara ait modal parametreler arasındaki farkların iteratif bir şekilde küçültülmesi esasına dayanmaktadır. Dinamik ölçümlerdeki gürültü varlığı, modal parametrelerin tahminini etkileyerek hasar tespit sonuçlarında belirsizliklere neden olmaktadır. Elde edilen sonuçların güvenirliği, içerdikleri belirsizlik miktarının saptanması ile mümkündür. Sunulan çalışma kapsamında hasar tespit sonuçlarının belirsizlik ölçümü, hasarlı duruma ait modal parametreler üzerine farklı varyasyon katsayıları ile ilişkili gürültü verisi eklenmesi ile gerçekleştirilmiştir. Model güncelleme işlemi, hasarlı duruma ait gürültü verisi eklenmiş modal parametreler ile hasarsız durumun modal parametrelerinin kullanılması ve hasarsız durumu temsil eden sonlu elemanlar modelinin eleman rijitlik değerlerinin güncellenmesi ile yapılmıştır. Güncelleme öncesi ve sonrası belirlenen rijitlik değerleri kullanılarak elemanlara ait rijitlik azalmaları hesaplanmış, böylece hasarların yerleri ve miktarları tespit edilmiştir. Farklı gürültü koşullarına ait verilerin kullanılması ile elde edilen hasar sonuçlarının belirsizlik ölçümü varyans analizi, meta-modelleme ve hassasiyet analizi yöntemleri ile gerçekleştirilmiştir. Bu şekilde, hasar tespit sonuçlarında önemli mertebede belirsizlik oluşturan etkenler belirlenmiştir.

Kaynakça

  • M. I. Friswell and J. E. Mottershead, Finite Element Model Updating in Structural Dynamics. Springer Science+Business Media, Dordrecht, 1995.
  • J. Nocedal and S. J. Wright, Numerical Optimization. Springer, New York, 1999.
  • A. Teughels, Inverse modelling of civil engineering structures based on operational modal data. Ph.D. Thesis, Katholieke University, Leuven, Belgium, 2003.
  • H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, B. R. Nadler, and J. J. Czarnecki, A Review of Structural Health Monitoring Literature: 1996-2001. Los Alamos National Laboratory, New Mexico, USA, Technical Report LA-13976-MS, 2003.
  • A. Teughels and G. De Roeck, Structural damage identification of the highway bridge Z24 by FE model updating. Journal of Sound and Vibration, 278 (3), 589-610, 2004. https://doi.org/10.1016/j.jsv.2003.10.041
  • E. P. Carden and P. Fanning, Vibration based condition monitoring: a review. Structural Health Monitoring, 3 (4), 355-377, 2004. https://doi.org/ 10.1177/1475921704047500
  • J. M. Brownjohn, Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365 (1851), 589-622, 2007. https://doi.org/10.1098/rsta.2006.1925
  • B. Moaveni, J. P. Conte, and F. M. Hemez, Uncertainty and sensitivity analysis of damage identification results obtained using finite element model updating. Computer-Aided Civil and Infrastructure Engineering, 24 (5), 320-334, 2009. https://doi.org/10.1111/j.1467-8667.2008.00589.x
  • C. Boller, F. K. Chang, and Y. Fujino, Encyclopedia of Structural Health Monitoring. Wiley, West Sussex, 2009.
  • T. Marwala, Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Springer, London, 2010.
  • J. E. Mottershead, M. Link, and M. I. Friswell, The sensitivity method in finite element model updating: a tutorial. Mechanical Systems and Signal Processing, 25 (7), 2275-2296, 2011. https://doi.org/10.1016/ j.ymssp.2010.10.012
  • W. Fan and P. Qiao, Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 10 (1), 83-111, 2011. https://doi.org/10.1177/1475921710365419
  • E. Simoen, B. Moaveni, J. P. Conte, and G. Lombaert, Uncertainty quantification in the assessment of progressive damage in a 7-story full-scale building slice. Journal of Engineering Mechanics, 139 (12), 1818-1830, 2013. https://doi.org/10.1061/ (ASCE)EM.19437889.0000610
  • A. Nozari, I. Behmanesh, S. Yousefianmoghadam, B. Moaveni, and A. Stavridis, Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building. Engineering Structures, 151, 540-553, 2017. https://doi.org/ 10.1016/j.engstruct.2017.08.044
  • M. C. Kennedy and A. O'Hagan, Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B, 63 (3), 425-464, 2001. https://doi.org/10.1111/1467-9868.00294
  • W. E. Walker, P. Harremoës, J. Rotmans, J. P. Van Der Sluijs, M. B. Van Asselt, P. Janssen, and M. P. Krayer von Krauss, Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4 (1), 5-17, 2003. https://doi.org/10.1076/iaij.4.1.5.16466
  • A. Der Kiureghian and O. Ditlevsen, Aleatory or epistemic? Does it matter?. Structural Safety, 31 (2), 105-112, 2009. https://doi.org/10.1016/ j.strusafe.2008.06.020
  • C. Soize, Generalized probabilistic approach of uncertainties in computational dynamics using random matrices and polynomial chaos decompositions. International Journal for Numerical Methods in Engineering, 81 (8), 939-970, 2010. https://doi.org/10.1002/nme.2712
  • E. Simoen, G. De Roeck, and G. Lombaert, Dealing with uncertainty in model updating for damage assessment: a review. Mechanical Systems and Signal Processing, 56-57, 123-149, 2015. https://doi.org/10.1016/j.ymssp.2014.11.001
  • T. Kernicky, M. Whelan, and E. Al-Shaer, Vibration-based damage detection with uncertainty quantification by structural identification using nonlinear constraint satisfaction with interval arithmetic. Structural Health Monitoring, 18 (5-6), 1569-1589, 2019. https://doi.org/10.1177/1475921718806476
  • Y. Huang, C. Shao, B. Wu, J. L. Beck, and H. Li, State-of-the-art review on Bayesian inference in structural system identification and damage assessment. Advances in Structural Engineering, 22 (6), 1329-1351, 2019. https://doi.org/10.1177/1369433218811540
  • E. Silva, C. Magluta, N. Roitman, and L. Aragoa Filho, Development of a structural identification methodology with uncertainty quantification through the SSI and bootstrap techniques. Mechanical Systems and Signal Processing,165, 108290, 2022. https://doi.org/10.1016/j.ymssp.2021.108290
  • Z. Yin, Z. R. Lu, J. Liu, and L. Wang, Quantifying uncertainty for structural damage identification in the presence of model errors from a deterministic sensitivity-based regime. Engineering Structures, 267, 114685, 2022. https://doi.org/10.1016/ j.engstruct.2022.114685
  • D. Xie, Z. R. Lu, G. Li, J. Liu, and L. Wang, Efficient Laplace prior-based sparse Bayesian learning for structural damage identification and uncertainty quantification. Mechanical Systems and Signal Processing, 188, 110000, 2023. https://doi.org/10.1016/j.ymssp.2022.110000
  • N. E. Silionis and K. N. Anyfantis, Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications. Probabilistic Engineering Mechanics, 71, 103412, 2023. https://doi.org/10.1016/ j.probengmech.2023.103412
  • MATLAB. The MathWorks Inc., Massachusetts, USA, 2023.
  • F. C. Filippou and M. Constantinides, FEDEASLab getting started guide and simulation examples. http://www.neesgrid.org/news/documents.php, Accessed 31 August 2004.
  • E. Durmazgezer, Modal parameter estimation and damage identification on progressively damaged r/c frames. Ph.D. Thesis, Dokuz Eylül University, İzmir, Turkey, 2019.
  • E. Durmazgezer, U. Yucel, and O. Ozcelik, Damage identification of a reinforced concrete frame at increasing damage levels by sensitivity-based finite element model updating. Bulletin of Earthquake Engineering, 17 (11), 6041-6060, 2019. https://doi.org/10.1007/s10518-019-00690-5
  • U. Yucel, Finite element model updating and damage identification of reinforced concrete frames with different infills and unreinforced masonry walls. Ph.D. Thesis, Dokuz Eylül University, İzmir, Turkey, 2020.
  • R. H. Myers and D. C. Montgomery, Response Surface Methodology. John Wiley & Sons, New York, 1995.
  • A. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis. John Wiley & Sons, New York, 2000.
  • C. F. J. Wu and M. Hamada, Experiments: Planning, Analysis, and Parameter Design Optimization. John Wiley & Sons, New York, 2000.
  • R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists. Prentice Hall, Upper Saddle River, 2006
  • D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers. Wiley and Sons, New York, 2007.
  • W. C. Navidi, Statistics for Engineers and Scientists. McGraw-Hill, Boston, 2007.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Deprem Mühendisliği, İnşaat Mühendisliğinde Sayısal Modelleme, İnşaat Mühendisliğinde Sistem Tanımlama, Yapı Dinamiği, İnşaat Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Umut Yücel 0000-0002-9007-0496

Erkan Durmazgezer Bu kişi benim 0000-0003-0568-9567

Erken Görünüm Tarihi 23 Kasım 2023
Yayımlanma Tarihi 15 Ocak 2024
Gönderilme Tarihi 15 Eylül 2023
Kabul Tarihi 15 Kasım 2023
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Yücel, U., & Durmazgezer, E. (2024). Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 13(1), 188-195. https://doi.org/10.28948/ngumuh.1361253
AMA Yücel U, Durmazgezer E. Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü. NÖHÜ Müh. Bilim. Derg. Ocak 2024;13(1):188-195. doi:10.28948/ngumuh.1361253
Chicago Yücel, Umut, ve Erkan Durmazgezer. “Düzlem çerçeve Modelinin Hasar Tespit sonuçlarının Belirsizlik ölçümü”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13, sy. 1 (Ocak 2024): 188-95. https://doi.org/10.28948/ngumuh.1361253.
EndNote Yücel U, Durmazgezer E (01 Ocak 2024) Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13 1 188–195.
IEEE U. Yücel ve E. Durmazgezer, “Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü”, NÖHÜ Müh. Bilim. Derg., c. 13, sy. 1, ss. 188–195, 2024, doi: 10.28948/ngumuh.1361253.
ISNAD Yücel, Umut - Durmazgezer, Erkan. “Düzlem çerçeve Modelinin Hasar Tespit sonuçlarının Belirsizlik ölçümü”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13/1 (Ocak 2024), 188-195. https://doi.org/10.28948/ngumuh.1361253.
JAMA Yücel U, Durmazgezer E. Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü. NÖHÜ Müh. Bilim. Derg. 2024;13:188–195.
MLA Yücel, Umut ve Erkan Durmazgezer. “Düzlem çerçeve Modelinin Hasar Tespit sonuçlarının Belirsizlik ölçümü”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 13, sy. 1, 2024, ss. 188-95, doi:10.28948/ngumuh.1361253.
Vancouver Yücel U, Durmazgezer E. Düzlem çerçeve modelinin hasar tespit sonuçlarının belirsizlik ölçümü. NÖHÜ Müh. Bilim. Derg. 2024;13(1):188-95.

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