Skip to main content
SpringerLink
Log in

A real-time sensing system based on triboelectric nanogenerator for dynamic response of bridges

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

The strain of bridges under traffic loads is time-varying and of small amplitude (∼10−6), which is a type of cumulative response and needs long-term continuous monitoring. To precisely capture the time-varying responses, a dynamic strain triboelectric nanogenerator (TENG) sensor with superior response capability, sensitivity, self-powered, and long-term stability is proposed in this paper. An analytical correlation between the structural strain response signal and the detected electrical signal is established for long-term continuous quantitative strain measurements based on the principles of contact electrification and electrostatic induction. A series of experiments are conducted to investigate the output performance of the proposed lateral-sliding mode TENG sensors. The results reveal that, with the loading condition with frequencies lower than 10 Hz, the time-varying strain responses of the steel bridge within the range of 3 to 150 microstrains can also be detected with high precision of 0.1 microstrains. And it achieves long-term stability after 10000 loading cycles compared with commercial sensors. The proposed novel sensing theory and method based on TENG technology can be applied as a new alternative approach for monitoring realtime structural strain information quantitatively with general applicability and feasibility for bridges.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ni Y Q, Wong K Y, Xia Y. Health checks through landmark bridges to sky-high structures. Adv Struct Eng, 2011, 14: 103–119

    Article  Google Scholar 

  2. Barrias A, Casas J R, Villalba S. A review of distributed optical fiber sensors for civil engineering applications. Sensors, 2016, 16: 748

    Article  Google Scholar 

  3. Ni Y Q, Xia Y X. Strain-based condition assessment of a suspension bridge instrumented with structural health monitoring system. Int J Str Stab Dyn, 2016, 16: 1640027

    Article  Google Scholar 

  4. Ye X W, Yi T H, Su Y H, et al. Strain-based structural condition assessment of an instrumented arch bridge using FBG monitoring data. Smart Struct Syst, 2017, 20: 139–150

    Google Scholar 

  5. Yan T, Wu Y, Yi W, et al. Recent progress on fabrication of carbon nanotube-based flexible conductive networks for resistive-type strain sensors. Sens Actuat A-Phys, 2021, 327: 112755

    Article  Google Scholar 

  6. Alwis L S M, Bremer K, Roth B. Fiber optic sensors embedded in textile-reinforced concrete for smart structural health monitoring: A review. Sensors, 2021, 21: 4948

    Article  Google Scholar 

  7. Peng L, Jing G, Luo Z, et al. Temperature and strain correlation of bridge parallel structure based on vibrating wire strain sensor. Sensors, 2020, 20: 658

    Article  Google Scholar 

  8. Zhang H, Shen M, Zhang Y, et al. Identification of static loading conditions using piezoelectric sensor arrays. J Appl Mech, 2018, 85: 011008

    Article  Google Scholar 

  9. Xiang T, Huang K, Zhang H, et al. Detection of moving load on pavement using piezoelectric sensors. Sensors, 2020, 20: 2366

    Article  Google Scholar 

  10. Zhang H, Zhou Y, Quan L. Identification of a moving mass on a beam bridge using piezoelectric sensor arrays. J Sound Vib, 2021, 491: 115754

    Article  Google Scholar 

  11. Zhang H, Huang K, Zhang Z, et al. Piezoelectric energy harvesting from roadways based on pavement compatible package. J Appl Mech, 2019, 86: 091012

    Article  Google Scholar 

  12. Wei Q, Chen G, Pan H, et al. MXene-sponge based high-performance piezoresistive sensor for wearable biomonitoring and real-time tactile sensing. Small Methods, 2022, 6: 2101051

    Article  Google Scholar 

  13. Su Y, Li W, Yuan L, et al. Piezoelectric fiber composites with polydopamine interfacial layer for self-powered wearable biomonitoring. Nano Energy, 2021, 89: 106321

    Article  Google Scholar 

  14. Gong S, Zhang J, Wang C, et al. Theoretical and experimental study of a monocharged electret for pressure sensor applications. J Appl Phys, 2021, 129: 034501

    Article  Google Scholar 

  15. Hou W, Zheng Y, Guo W, et al. Piezoelectric vibration energy harvesting for rail transit bridge with steel-spring floating slab track system. J Cleaner Production, 2021, 291: 125283

    Article  Google Scholar 

  16. Lu H, Gao Z, Wu B, et al. Dynamic and quasi-static signal separation method for bridges under moving loads based on long-gauge FBG strain monitoring. J Low Frequency Noise Vib Active Control, 2019, 38: 388–402

    Article  Google Scholar 

  17. Wang D, Zhang J, Zhu H. Embedded electromechanical impedance and strain sensors for health monitoring of a concrete bridge. Shock Vib, 2015, 2015: 1–12

    Article  Google Scholar 

  18. Yu H, He X, Ding W, et al. A self-powered dynamic displacement monitoring system based on triboelectric accelerometer. Adv Energy Mater, 2017, 7: 1700565

    Article  Google Scholar 

  19. Zhang H, Yang C, Yu Y, et al. Origami-tessellation-based triboelectric nanogenerator for energy harvesting with application in road pavement. Nano Energy, 2020, 78: 105177

    Article  Google Scholar 

  20. Qu Z, Wu L, Yue B, et al. Eccentric triboelectric nanosensor for monitoring mechanical movements. Nano Energy, 2019, 62: 348–354

    Article  Google Scholar 

  21. Chen J, Wang Z L. Reviving vibration energy harvesting and self-powered sensing by a triboelectric nanogenerator. Joule, 2017, 1: 480–521

    Article  Google Scholar 

  22. Zhang H, Su X, Quan L, et al. Sponge-supported triboelectric nanogenerator for energy harvesting from rail vibration. J Energy Eng, 2021, 147: 04021006

    Article  Google Scholar 

  23. Wang A, Chen J, Wang L, et al. Numerical analysis and experimental study of an ocean wave tetrahedral triboelectric nanogenerator. Appl Energy, 2022, 307: 118174

    Article  Google Scholar 

  24. Lin S, Zhu L, Qiu Y, et al. A self-powered multi-functional sensor based on triboelectric nanogenerator for monitoring states of rotating motion. Nano Energy, 2021, 83: 105857

    Article  Google Scholar 

  25. Garcia C, Trendafilova I, Sanchez del Rio J. Detection and measurement of impacts in composite structures using a self-powered triboelectric sensor. Nano Energy, 2019, 56: 443–453

    Article  Google Scholar 

  26. Wang S, Lin L, Wang Z L. Triboelectric nanogenerators as self-powered active sensors. Nano Energy, 2015, 11: 436–462

    Article  Google Scholar 

  27. Li X, Cao Y, Yu X, et al. Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture. Appl Energy, 2022, 306: 117977

    Article  Google Scholar 

  28. Wu B, Zhang Z X, Xue X B, et al. A stretchable triboelectric generator with coplanar integration design of energy harvesting and strain sensing. Sci China Tech Sci, 2022, 65: 221–230

    Google Scholar 

  29. Hu G, Zhao C, Yang Y, et al. Triboelectric energy harvesting using an origami-inspired structure. Appl Energy, 2022, 306: 118037

    Article  Google Scholar 

  30. Zhang H, Wang H, Zhang J, et al. A novel rhombic-shaped paper-based triboelectric nanogenerator for harvesting energy from environmental vibration. Sens Actuat A-Phys, 2020, 302: 111806

    Article  Google Scholar 

  31. Yang Y, Hou X J, Geng W P, et al. Human movement monitoring and behavior recognition for intelligent sports using customizable and flexible triboelectric nanogenerator. Sci China Tech Sci, 2022, 65: 826–836

    Article  Google Scholar 

  32. Hu Y, Wang X, Qin Y, et al. A robust hybrid generator for harvesting vehicle suspension vibration energy from random road excitation. Appl Energy, 2022, 309: 118506

    Article  Google Scholar 

  33. Su Y, Chen G, Chen C, et al. Self-powered respiration monitoring enabled by a triboelectric nanogenerator. Adv Mater, 2021, 33: 2101262

    Article  Google Scholar 

  34. Liu B, Libanori A, Zhou Y, et al. Simultaneous biomechanical and biochemical monitoring for self-powered breath analysis. ACS Appl Mater Interfaces, 2022, 14: 7301–7310

    Article  Google Scholar 

  35. Zhang S L, Lai Y C, He X, et al. Auxetic foam-based contact-mode triboelectric nanogenerator with highly sensitive self-Powered strain sensing capabilities to monitor human body movement. Adv Funct Mater, 2017, 27: 1606695

    Article  Google Scholar 

  36. Cao Y, Guo Y, Chen Z, et al. Highly sensitive self-powered pressure and strain sensor based on crumpled MXene film for wireless human motion detection. Nano Energy, 2022, 92: 106689

    Article  Google Scholar 

  37. Xiao Y, Xu Y, Qu C, et al. Micro-crack assisted wrinkled PEDOT:PSS to detect and distinguish tensile strain and pressure based on a tribo-electric nanogenerator. Adv Mater Technol, 2022, 7: 2100423

    Article  Google Scholar 

  38. Su Y, Xie G, Tai H, et al. Self-powered room temperature NO2 detection driven by triboelectric nanogenerator under UV illumination. Nano Energy, 2018, 47: 316–324

    Article  Google Scholar 

  39. Pang Y K, Li X H, Chen M X, et al. Triboelectric nanogenerators as a self-powered 3D acceleration sensor. ACS Appl Mater Interf, 2015, 7: 19076–19082

    Article  Google Scholar 

  40. Li S, Liu D, Zhao Z, et al. A fully self-powered vibration monitoring system driven by dual-mode triboelectric nanogenerators. ACS Nano, 2020, 14: 2475–2482

    Article  Google Scholar 

  41. Ye X W, Su Y H, Han J P. A state-of-the-art review on fatigue life assessment of steel bridges. Math Problems Eng, 2014, 2014: 1–13

    Google Scholar 

  42. Ni Y Q, Ye X W, Ko J M. Monitoring-based fatigue reliability assessment of steel bridges: Analytical model and application. J Struct Eng, 2010, 136: 1563–1573

    Article  Google Scholar 

  43. ASCE Committee on Fatigue and Fracture Reliability. Fatigue reliability: Development of criteria for design. J Struct Div ASCE, 1982, 108: 71–88

    Article  Google Scholar 

  44. Deng L, Nie L, Zhong W, et al. Developing fatigue vehicle models for bridge fatigue assessment under different traffic conditions. J Bridge Eng, 2021, 26: 04020122

    Article  Google Scholar 

  45. Farreras-Alcover I, Chryssanthopoulos M K, Andersen J E. Data-based models for fatigue reliability of orthotropic steel bridge decks based on temperature, traffic and strain monitoring. Int J Fatigue, 2017, 95: 104–119

    Article  Google Scholar 

  46. Li Z. Fatigue analysis and life prediction of bridges with structural health monitoring data—Part I: Methodology and strategy. Int J Fatigue, 2001, 23: 45–53

    Article  Google Scholar 

  47. Zhang H, Yao L, Quan L, et al. Theories for triboelectric nanogenerators: A comprehensive review. Nanotechnol Rev, 2020, 9: 610–625

    Article  Google Scholar 

  48. Zhang H, Zhang C, Zhang J, et al. A theoretical approach for optimizing sliding-mode triboelectric nanogenerator based on multiparameter analysis. Nano Energy, 2019, 61: 442–453

    Article  Google Scholar 

  49. Zhang H, Quan L, Chen J, et al. A general optimization approach for contact-separation triboelectric nanogenerator. Nano Energy, 2019, 56: 700–707

    Article  Google Scholar 

  50. Wang Z L. On Maxwell’s displacement current for energy and sensors: The origin of nanogenerators. Mater Today, 2017, 20: 74–82

    Article  Google Scholar 

  51. Shao J J, Jiang T, Wang Z L. Theoretical foundations of triboelectric nanogenerators (TENGs). Sci China Tech Sci, 2020, 63: 1087–1109

    Article  Google Scholar 

  52. Li T, Xu Y, Willander M, et al. Lightweight triboelectric nanogenerator for energy harvesting and sensing tiny mechanical motion. Adv Funct Mater, 2016, 26: 4370–4376

    Article  Google Scholar 

  53. Zhang B, Zhang L, Deng W, et al. Self-powered acceleration sensor based on liquid metal triboelectric nanogenerator for vibration monitoring. ACS Nano, 2017, 11: 7440–7446

    Article  Google Scholar 

  54. Wang Z L. Triboelectric nanogenerators as new energy technology for self-powered systems and as active mechanical and chemical sensors. ACS Nano, 2013, 7: 9533–9557

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China

    He Zhang, KangXu Huang, YuHui Zhou & ZhiCheng Zhang

  2. Center for Balance Architecture, Zhejiang University, Hangzhou, 310058, China

    He Zhang

  3. The Architectural Design & Research Institute of Zhejiang University Co. Ltd., Hangzhou, 310063, China

    LiangFeng Sun

  4. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, China

    JiKui Luo

Authors
  1. He Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. KangXu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YuHui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. LiangFeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. ZhiCheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. JiKui Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to He Zhang, ZhiCheng Zhang or JiKui Luo.

Additional information

This work was supported by the National Key R&D Program of China (Grant No. 2018YFB1600200), the National Natural Science Foundation of China (Grant Nos. 52122801, 11925206, and 51978609), and Zhejiang Provincial Natural Science Foundation for Distinguished Young Scientists (Grant No. LR20E080003).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Huang, K., Zhou, Y. et al. A real-time sensing system based on triboelectric nanogenerator for dynamic response of bridges. Sci. China Technol. Sci. 65, 2723–2733 (2022). https://doi.org/10.1007/s11431-022-2092-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-022-2092-x

Search

Navigation