PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Wykorzystanie badań pod próbnym obciążeniem w procesie tworzenia cyfrowych bliźniaków mostów

Identyfikatory
Warianty tytułu
EN
Creating digital twins ot bridges based on test loads
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono definicję, budowę i możliwości wdrożenia koncepcji cyfrowego bliźniaka w obszarze zarządzania obiektem budowlanym, w tym infrastrukturą mostową. Przywołano założenia i etapy jego powstawania, wskazując na rolę badań pod próbnym obciążeniem przy weryfikacji i aktualizacji modelu obliczeniowego MES, będącego jedną z kluczowych składowych całego systemu. Posługując się przykładem rzeczywistego obiektu mostowego, opisano możliwość wykorzystania modelu BIM w procesie generowania modelu obliczeniowego, wyniki badań pod próbnym obciążeniem oraz zagadnienia kalibracji modelu MES ze szczególnym uwzględnieniem roli zweryfikowanych modeli BIM i MES w strukturze cyfrowego bliźniaka.
EN
The paper presents the definition, framework, and possible implementation of the digital twin concept in the field of building asset management, including bridge facilities. The assumptions and stages of its creation are described, indicating the role of load testing in the validation and updating as one of the key components of the entire system. Using the example of a real bridge, the possibility of using the BIM model in the process of generating a computational model, the results of load tests and the process of calibration of the FEA model are described, with particular emphasis on the role of the verified BIM and FEA models in the digital twin framework.
Rocznik
Strony
360--367
Opis fizyczny
Bibliogr. 35 poz., il.
Twórcy
  • Politechnika Śląska, Wydział Budownictwa
  • Politechnika Śląska, Wydział Budownictwa
autor
  • Politechnika Śląska, Wydział Budownictwa
  • Politechnika Śląska, Wydział Budownictwa
  • Politechnika Śląska, Wydział Budownictwa
Bibliografia
  • [1] Babalola A., Manu P., Cheung C., Yunuse-Kaltungo A., Bartolo P.: A systematic review of the application of immersive technologies for safety and health management in the construction sector. Journal of Safety Research, 2023, doi: 10.1016/j.jsr.2023.01.007.
  • [2] Thelen A. et al.: A comprehensive review of digital twin - part 1: model ing and twinning enabling technologies. Structural and Multidisciplinary Optimization, t. 65, 2022, 354, doi: 10.1 007/s00158-022-03425-4.
  • [3] Thelen A. et al.: A comprehensive review of digital twin - part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Structural and Multidisciplinary Optimization, t. 66, 2023, 1, doi: 10.1007/s00158-022-03410-x.
  • [4] Mazumder A. et al.: Towards next generation digital twin in robotics: Trends, scopes, challenges, and future, Heliyon, t. 9, no. 2, 2023, e13359, doi: 10.1016/j.heliyon.2023.e13359.
  • [5] Somers R.J., Douthwaite J.A., Wagg D.J., Walkinshaw N., Hierons R.M.: Digital-twin-based testing for cyber-physical systems: A systematic literature review. Information and Software Technology, t. 156, 2023, 107145, doi: 10.1016/j.infsof.2022.107145.
  • [6] Semeraro C. i in., Digital twin application in energy storage: Trends and challenges. Journal of Energy Storage, t. 58, 2023, 106347, doi: 10.1 016/j.est.2022.1 06347.
  • [7] Wang J. et al.: Research on coal mine safety management based on digital twin, Heliyon, t. 9, no. 3, 2023, e13608, doi: 10.1016/j.heliyon.2023.e13608.
  • [8] Purcell W., Neubauer T.: Digital Twins in Agriculture: A State-of-the-art review, Smart Agricultural Technology, t. 3, 2023, 100094, doi: 10.1016/j.atech.2022.1 00094.
  • [9] Kumar S.A.P., Madhumathi R., Chellah P.R., Tao L., Wang S.: A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. Journal of Reliable Intelligent Environments, t. 4, no. 4, 2018, pp. 199-209, doi: 10.1007/s40860-018-0069-y.
  • [10] Mauro F., Kana A.A.: Digital twin for ship life-cycle: A critical systematic review. Ocean Engineering, t. 269, 2023, 113479, doi: 10.1016/j.oceaneng.2022.113479.
  • [11] Wong E.Y.C., Mo D.Y., So S.: Closed-Ioop digital twin system for air cargo load planning operations. International Journal of Computer Integrated Manufacturing, t. 34, no. 7-8, 2021, pp. 801-813, doi: 10.1080/0951192X.2020.1775299.
  • [12] Xiong M., Wang H.: Digital twin applications in aviation industry: A review. The International Journal of Advanced Manufacturing Technology, t. 121, no. 9-10, 2022, pp. 5677-5692, doi: 10.1007/s00170-022-09717-9.
  • [13] Eftimie R., Mavrodin A., Bordas S.P.A.: From digital control to digital twins in medicine: A brief review and future perspectives, Advances in Applied Mechanics. Academic Press Inc., 2022. doi: 10.1016/bs.aams.2022.09.001.
  • [14] Jiang F., Ma L., Broyd T., Chen K.: Digital twin and its implementations in the civil engineering sector. Automation in Construction, t. 130, 2021, 103838, doi: 10.1016/j.autcon.2021.1 03838.
  • [15] Broo D.G., Bravo-Haro M., Schooling J.: Design and implementation of a smart infrastructure digital twin. Automation in Construction, t. 136, 2022,104171, doi: 10.1016/j.autcon.2022.104171.
  • [16] Naderi H., Shojael A.: Digital twinning of civil infrastructures: Current state of model architectures, interoperability solutions, and future prospects. Automation in Construction, t. 149, 2023, 104785, doi: 10.1016/j.autcon.2023.104785.
  • [17] Alnowaiser K.K., Ahmed M.A., Twin D.: Current Research Trends and Future Directions. Arabian Journal for Science and Engineering, t. 48, no. 2, 2023, pp. 1075-1095, doi: 10.1007/s13369-022-07459-0.
  • [18] Attaran M., Celik B.G., Twin D.: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, t. 6, 2023, 100165, doi: 10.1 016/j.dajour.2023.1 00165.
  • [19] Agnusdei G.P., Elia V., Gnoni M.G.: A classification proposal of digital twin applications in the safety domain. Computers & Industrial Engineering, t. 154, 2021,107137, doi: 10.1016/j.cie.2021.107137.
  • [20] Chiachio M., Megla M., Chiachio J., Fernandez J., Jalón M.L.: Structural digital twin framework: Formulation and technology integration. Automation in Construction, t. 140, 2022, 104333, doi: 10.1016/j.autcon.2022.104333.
  • [21] Wang T., Gan V.J.L., Hu D., Liu H.: Digital twin-enabled built environment sensing and monitoring through semantic enrichment of BIM with SensorML. Automation in Construction, t. 144, 2022, 104625, doi: 10.1 016/j.autcon.2022.104625.
  • [22] Mohammadi M., Rashidi M., Yu Y., Samali B.: Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructures. Computers in Industry, t. 147, 2023, 103881, doi: 10.1016/j.compind.2023.103881.
  • [23] Lu R., Brilakis I.: Digital twinning of existing reinforced concrete bridges from labelled point clusters. Automation in Construction, t. 105, 2019, 102837, doi: 10.1016/j.autcon.2019.1 02837.
  • [24] Gao Y., Li H., Xiong G., Song H.: AloT-informed digital twin communication for bridge maintenance. Automation in Construction, t. 150, 2023, 104835, doi: 10.1016/j.autcon.2023.1 04835.
  • [25] Ritto T.G., Rochinhe F.A.: Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mechanical Systems and Signal Processing, t. 155, 2021,107614, doi: 10.1016/j.ymssp.2021.107614.
  • [26] Yin Y., Zheng P., Wang C.L.L.: A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation. Robotics and Computer-Integrated Manufacturing, t. 81, 2023, 102515, doi: 10.1016/j.rcim.2022.102515.
  • [27] Jiang F., Ding Y., Song Y., Geng F., Wang Z.: Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen. Engineering Structures, t. 241, 2021, 112461, doi: 10.1 016/j.engstruct.2021.112461.
  • [28] Lin K., Xu Y.-L., Lu X., Guan Z., Li J.: Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes. Automation in Construction, t. 123, 2021, 103547, doi: 10.1016/j.autcon.2020.103547.
  • [29] Teng S., Chen X., Chen G., Cheng L.: Structural damage detection based on transfer learning strategy using digital twins of bridges. Mechanical Systems and Signal Processing, t. 191, 2023, 110160, doi: 10.1016/j.ymssp.2023.110160.
  • [30] Consilvio A et al.: Towards a digital twin-based intelligent decision support for road maintenance. Transportation Research Procedia, t. 69, 2023, pp. 791-798, doi: 10.1016/j.trpro.2023.02.237.
  • [31] Yu G., Wang Y., Mao Z., Hu M., Sugumaran V., Wang Y.K.: A digital twin-based decision analysis framework for operation and maintenance of tunnels. Tunnelling and Underground Space Technology, t. 116, 2021,104125, doi: 10.1016/j.tust.2021.104125.
  • [32] Petri I., Rezgui Y., Ghoroghi A., Alzahrani A.: Digital twins for performance management in the built environment. Journal of Industrial Information Integration, t. 33, 2023, 100445, doi: 10.1 016/j.jii.2023.100445.
  • [33] Honghong S., Gang Y., Haijiang L., Tian Z., Annan J.: Digital twin enhanced BIM to shape full life cycle digital transformation for bridge engineering. Automation in Construction, t. 147, 2023, 104736, doi: 10.1016/j.autcon.2022.1 04736.
  • [34] Kuras P., Ortyl Ł., Owerko T., Salamak M., Łaziński P.: GB-SAR in the Diagnosis of Critical City Infrastructure - A Case Study of a Load Test on the Long Tram Extradosed Bridge, Remote Sensing (Basel), t. 12, no. 20, 2020, 3361, doi: 10.3390/rs12203361.
  • [35] Krząkała J., Łaziński P., Gerges M., Pyrzowski Ł., Grządziela G.: Influence of Actual Curing Conditions on Mechanical Properties of Concrete in Bridge Superstructures. Materials, t. 16, no. 1, 2022, 54, doi: 10.3390/ma1601 0054.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9670f6a6-2efc-4453-a967-02e87bf90f44
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.