Vibration response of a hybrid steel–timber building element with uncertain material and joint parameters

Chocholaty, Bettina; Eser, Martin; Hoppe, Karl-Alexander; Wang, Daotong; Marburg, Steffen

doi:10.1007/s43452-023-00819-z

Artykuł - szczegóły

Tytuł artykułu

Vibration response of a hybrid steel–timber building element with uncertain material and joint parameters

Autorzy

Chocholaty Bettina , Eser Martin , Hoppe Karl-Alexander , Wang Daotong , Marburg Steffen

Wybrane pełne teksty z tego czasopisma

http://www.springer.com/journal/43452

Identyfikatory

DOI

10.1007/s43452-023-00819-z

Warianty tytułu

Języki publikacji

Abstrakty

The design of building elements is usually done conservatively by considering safety factors. However, more efficient designs are gaining interest for economic and sustainability reasons. Hence, an adequate prediction tool can improve the design of building elements. Probabilistic modeling, for example, Monte Carlo simulations, represents a remedy to this by examining uncertainties in a structure through uncertain input parameters. In this work, a Monte Carlo simulation is performer to quantify the uncertainty in the modal properties of a hybrid steel–timber building element. The material properties of the timber material and the stiffness of the structural joints are considered uncertain inputs. The probabilistic properties of the timber material are evaluated utilizing Bayesian inference instead of the usually applied empirical methods. Using these inferred timber material properties leads to a good match of simulated and measured natural frequencies of the timber components. These parameters are utilized together with the joints’ uncertain inputs in the Monte Carlo simulation of the hybrid steel–timber building element. The results show a significant span for the identified eigenfrequencies, which proves the relevance of probabilistic analyses for the vibration characteristics of building elements.

Słowa kluczowe

hybrid steel–timber elements Monte Carlo simulation vibrations Bayesian inference

symulacja Monte Carlo drgania wnioskowanie bayesowskie

Wydawca

Springer
Wrocław University of Science and Technology

Czasopismo

Archives of Civil and Mechanical Engineering

Rocznik

2024

Tom

Vol. 24, no. 1

Strony

art. no. e22, 2024

Opis fizyczny

Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Chocholaty Bettina

Chair of Vibroacoustics of Vehicles and Machines, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany

https://orcid.org/0000-0003-3840-5946

autor

Eser Martin

Chair of Vibroacoustics of Vehicles and Machines, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany

autor

Hoppe Karl-Alexander

Chair of Vibroacoustics of Vehicles and Machines, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany

autor

Wang Daotong

Chair of Vibroacoustics of Vehicles and Machines, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany

autor

Marburg Steffen

chocholaty@tum.de

Chair of Vibroacoustics of Vehicles and Machines, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany

Bibliografia

1. Cheraghi-Shirazi N, Crews K, Malek S. Review of Vibration Assessment Methods for Steel-Timber Composite Floors. Buildings. 2022;12(12):2061. https://doi.org/10.3390/buildings12122061.
2. Hassanieh A, Chiniforush AA, Valipour HR, Bradford MA.Vibration behaviour of steel-timber composite floors, part (2):evaluation of human-induced vibrations. J Constr Steel Res.2019;158:156–70. https://doi.org/10.1016/j.jcsr.2019.03.026.
3. Technical Committee ISO/TC 98/SC 2. ISO 10137:2007(E) -Bases for design of structures - Serviceability of buildings and walkways against vibrations; 2007.
4. Technical Committee ISO/TC 43/SC 2. EN ISO 10140-3 - Acoustics - Laboratory measurement of sound insulation of building elements - Part 3: Measurements of impact sound insulation; 2021.https://doi.org/10.31030/3237068.
5. Chocholaty B, Roozen NB, Maeder M, Marburg S. Vibroacoustic response of steel-timber composite elements. Eng Struct.2022;271: 114911. https:// doi. org/ 10. 1016/j. engst ruct. 2022.114911.
6. Chiniforush AA, Alamdari MM, Dackermann U, Valipour HR, Akbarnezhad A. Vibration behaviour of steel-timber composite floors, part (1): Experimental & numerical investigation. J ConstrSteel Res. 2019;161:244–57. https://doi.org/10.1016/j.jcsr.2019.07.007.
7. Soize C. A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics. J Sound Vib. 2005;288(3):623–52. https://doi.org/10.1016/j.jsv.2005.07.009.
8. Brake MR. The mechanics of jointed structures. Cham: Springer;2016.
9. Gant F, Rouch P, Champaney L. Updating of uncertain joint models using the Lack-of-Knowledge theory. Computers & Structures.2013;128:128–35. https://doi.org/10.1016/j.compstruc.2013.06.013.
10. Qian C, Ménard S, Bard D, Negreira J. Development of a vibroa-coustic stochastic finite element prediction tool for a CLT floor.Appl Sci. 2019;9(6):1106. https://doi.org/10.3390/app9061106.
11. Persson P, Flodén O. Effect of material parameter variability on vibroacoustic response in wood floors. Appl Acoust.2019;146:38–49. https://doi.org/10.1016/j.apacoust.2018.10.034.
12. Sepahvand K, Marburg S. Identification of composite uncertain material parameters from experimental modal data. Probab EngMech. 2014;37:148–53. https://doi.org/10.1016/j.probengmech.2014.06.008.
13. Sepahvand K, Marburg S, Hardtke HJ. Stochastic free vibration of orthotropic plates using generalized polynomial chaos expansion. J Sound Vib. 2012;331(1):167–79. https://doi.org/10.1016/j.jsv.2011.08.012.
14. Gogu C, Haftka R, Le Riche R, Molimard J, Vautrin A, Sankar B.Bayesian statistical identification of orthotropic elastic constants accounting for measurement and modeling errors. In: 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 17th AIAA/ASME/AHS Adaptive Structures Conference 11th AIAA No; 2009. p. 2258. https://doi.org/10.2514/6.2009-2258.
15. Daghia F, de Miranda S, Ubertini F, Viola E. Estimation of elastic constants of thick laminated plates with in a Bayesian framework. Compos Struct. 2007;80(3):461–73. https:// doi. org/ 10. 1016/j.compstruct.2006.06.030.
16. Melzerová L, Janda T, Šejnoha M, Šejnoha J. FEM models of glued laminated timber beams enhanced by Bayesian updating of elastic moduli. International Journal of Civil and Environmental Engineering. 2015;9(5):692–8.
17. Schneider F, Papaioannou I, Straub D, Winter C, Müller G. Bayesian parameter updating in linear structural dynamics with frequency transformed data using rational surrogate models. MechSyst Signal Process. 2022;166: 108407. https://doi.org/10.1016/j.ymssp.2021.108407.
18. Ewins DJ. Modal Testing - Theory, Practice and Application. NewYork: John Wiley & Sons; 2009.
19. ANSYS, Inc . Ansys Engineering Simulation Software; 2019.R1.Available from: https://www.ansys.com/.
20. Shen Z, Liu X, Zang C, Hu S. Bayesian Uncertainty Identification of Model Parameters for the Jointed Structures with Nonlinearity. Shock and Vibration. 2021;2021. https://doi.org/10.1155/2021/2638995.
21. Langer P, Maeder M, Guist C, Krause M, Marburg S. More thansix elements per wavelength: The practical use of structural finite element models and their accuracy in comparison with experimental results. J Comput Acoust. 2017;25(04):1750025. https://doi.org/10.1142/s0218396x17500254.
22. Ballast DK. Handbook of construction tolerances. John Wiley &Sons; 2007.
23. Andrej A. Schneider: Bautabellen für Ingenieure. vol. 21. Bunde-sanzeiger Verlag; 2014. https://doi.org/10.1002/best.201190039.
24. STEICO SE. Konstruktionsheft von STEICO zu LVL/Furnier-schichtholz; 2020. Accessed: 2021-01-19. Available from: https://www.steico.com/de/downloads/dokumente/produkte-allgemeines.
25. Faber M, Vrouwenvelder T. JCSS PROBABILISTIC MODELCODE - Part III: Resistance Models; 2006. Accessed: 2023-02-02. https://www.jcss-lc.org/jcss-probabilistic-model-code/.
26. Gelman A. Bayesian Data Analysis. 3rd ed. Chapman and Hall/CRC; 2013.
27. Xiang N. Model-based Bayesian analysis in acoustics-Atutorial. The Journal of the Acoustical Society of America.2020;148(2):1101–20. https://doi.org/10.1121/10.0001731.
28. Shi D, Zhuang Z, Zhang T. Free vibration analysis of orthotropic rectangular Mindlin plates with general elastic boundary conditions. In: INTER-NOISE and NOISE-CON Congress and Conference Proceedings. vol. 249. Institute of Noise Control Engineering; 2014. p. 1477–1485.
29. Mottershead JE, Link M, Friswell MI, Schedlinski C. Model updating. Handbook of Experimental Structural Dynamics.2020;p. 1–53.
30. Basaglia BM, Li J, Shrestha R, Crews K. Response prediction to walking-induced vibrations of a long-span timber floor. J StructEng. 2021;147(2):04020326. https:// doi. org/ 10. 1061/ (asce) st.1943-541x.0002888.
31. Wesner JS, Pomeranz JP. Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution. Ecosphere. 2021;12(9): e03739. https://doi.org/10.1101/2020.12.10.419713.
32. Salvatier J, Wiecki TV, Fonnesbeck C. Probabilistic programming in Python using PyMC3. PeerJ Computer Science. 2016;2: e55.https://doi.org/10.7717/peerj-cs.55.
33. Martin O. Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Packt Publishing Ltd; 2018.
34. Ibrahim R, Pettit C. Uncertainties and dynamic problems of bolted joints and other fasteners. J Sound Vib. 2005;279(3–5):857–936.https://doi.org/10.1016/j.jsv.2003.11.064.
35. Gangadharan SN, Nikolaidis E, Haftka RT. Probabilistic system identification of two flexible joint models. AIAA J.1991;29(8):1319–26. https://doi.org/10.2514/6.1990-1144.
36. Segalman DJ, Gregory DL, Starr MJ, Resor BR, Jew MD, LaufferJP, et al. Handbook on dynamics of jointed structures. Sandia National Laboratories, Albuquerque. 2009.
37. Gauvain JL, Lee CH. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEEtransactions on speech and audio processing. 1994;2(2):291–8.https://doi.org/10.1109/89.279278.
38. Shah MA, Yunus M, Rani MA, Omar R, Mora A. Stochastic model updating of bolt-jointed structure for structural dynamics applications. International Journal of Automotive and Mechanical Engineering. 2021;18(2):8760–71. https://doi.org/10.15282/ijame.18.2.2021.14.0670.
39. Kroese DP, Taimre T, Botev ZI. Handbook of monte carlo methods. John Wiley & Sons; 2013.
40. Olsson AM, Sandberg GE. Latin hypercube sampling for stochastic finite element analysis. J Eng Mech. 2002;128(1):121–5.https://doi.org/10.1061/(asce)0733-9399(2002)128:1(121).
41. Luegmair M, Schmid JD. Challenges in vibroacoustic vehicle body simulation including uncertainties. SAE Technical Paper;2020.
42. Technical Committee CEN/TC 250. DIN EN 1990:2021-10 -Eurocode: Basis of structural design - German Version; 2021.
43. Technical Committee CEN/TC 250. Eurocode 5: Design of timber structures, Part 1-1: General - Common rules and rules for buildings [Standard]; 2010.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-49afe296-939c-49ac-9343-1ebca43939c7