Skip to main content
SpringerLink
Log in

Parallel computing-oriented method for long-time duration problem of force identification

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Rapid identification of dynamic forces is an important research subject. To reduce the computing time, a parallel computing-oriented method is proposed in this study for dealing with a long-time duration problem of force identification. The proposed method is implemented via three continues steps, i.e., partition of parallel computing tasks, solution of parallel computing tasks and fusion of the identified results. In the first step, moving time window is applied for splitting an original problem into several sub-problems in time domain. Then the next step focuses on solving the sub-problems which can be executed in parallel. Herein, influences of unknown initial conditions are considered. Sparse regularization such as weighted l1-norm regularization method is introduced for ensuring that the identified result is sparse and stable. In the last step, the identified results calculated from all the sub-problems are fused via a weighted average method. Numerical simulations are carried out on a frame structure and a truss structure, respectively. A cluster constructed from three personal computers is used for implementation of the proposed method. Illustrated results show that the proposed method can be used for identifying the dynamic forces in long-time duration and saving the computing time. Some related issues are discussed as well.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Niu Y, Fritzen CP, Jung H, Buethe I, Ni YQ, Wang YW (2015) Online simultaneous reconstruction of wind load and structural responses—theory and application to Canton Tower. Comput Civ Infrastruct Eng 30(8):666–681

    Article  Google Scholar 

  2. Zhong J, Liu H, Yu L (2019) Sparse regularization for traffic load monitoring using bridge response measurements. Measurement 131:173–182

    Article  Google Scholar 

  3. Zhang D, Han X, Zhang Z, Liu J, Jiang C, Yoda N, Meng X, Li Q (2017) Identification of dynamic load for prosthetic structures. Int J Numer Method Biomed Eng 33(12):e2889. https://doi.org/10.1002/cnm.2889

    Article  MathSciNet  Google Scholar 

  4. Azam SE, Chatzi E, Papadimitriou C, Smyth A (2017) Experimental validation of the Kalman-type filters for online and real-time state and input estimation. J Vib Control 23(15):2494–2519

    Article  Google Scholar 

  5. Sanchez J, Benaroya H (2014) Review of force reconstruction techniques. J Sound Vib 333(14):2999–3018

    Article  Google Scholar 

  6. Mendrok K, Dworakowski Z (2019) A review of methods for excitation force reconstruction. Diagnostyka 20(3):11–19. https://doi.org/10.29354/diag/110241

    Article  Google Scholar 

  7. Yu L, Chan THT (2007) Recent research on identification of moving loads on bridges. J Sound Vib 305(1–2):3–21. https://doi.org/10.1016/j.jsv.2007.03.057

    Article  Google Scholar 

  8. Wu SQ, Law SS (2010) Moving force identification based on stochastic finite element model. Eng Struct 32(4):1016–1027

    Article  Google Scholar 

  9. Wang L, Liu Y, Liu Y (2019) An inverse method for distributed dynamic load identification of structures with interval uncertainties. Adv Eng Softw 131:77–89. https://doi.org/10.1016/j.advengsoft.2019.02.003

    Article  Google Scholar 

  10. Sanchez J, Benaroya H (2017) Asymptotic approximation method of force reconstruction: proof of concept. Mech Syst Signal Process 92:39–63

    Article  Google Scholar 

  11. Li Q, Lu Q (2018) Time domain force identification based on adaptive ℓq regularization. J Vib Control 24(23):5610–5626

    Article  MathSciNet  Google Scholar 

  12. Aucejo M, De Smet O (2018) A space-frequency multiplicative regularization for force reconstruction problems. Mech Syst Signal Process 104:1–18

    Article  Google Scholar 

  13. Logan P, Avitabile P, Dodson J (2020) Reconstruction of external forces beyond measured points using a modal filtering decomposition approach. Exp Tech 44:113–125. https://doi.org/10.1007/s40799-019-00340-0

    Article  Google Scholar 

  14. Wang L, Xie Y, Wu Z, Du Y, He K (2018) A new fast convergent iteration regularization method. Eng Comput 35(1):127–138. https://doi.org/10.1007/s00366-018-0588-4

    Article  Google Scholar 

  15. Pan CD, Yu L (2019) Identification of external forces via truncated response sparse decomposition under unknown initial conditions. Adv Struct Eng 22(15):3161–3175. https://doi.org/10.1177/1369433219859479

    Article  Google Scholar 

  16. González A, Rowley C, OBrien EJ (2008) A general solution to the identification of moving vehicle forces on a bridge. Int J Numer Methods Eng 75(3):335–354

    Article  Google Scholar 

  17. Li Q, Lu Q (2019) A revised time domain force identification method based on Bayesian formulation. Int J Numer Methods Eng 118(7):411–431

    Article  MathSciNet  Google Scholar 

  18. Wensong J, Zhongyu W, Jing L (2018) A fractional-order accumulative regularization filter for force reconstruction. Mech Syst Signal Process 101:405–423

    Article  Google Scholar 

  19. Wang L, Liu J, Xie Y, Gu Y (2018) A new regularization method for the dynamic load identification of stochastic structures. Comput Math Appl 76(4):741–759

    Article  MathSciNet  Google Scholar 

  20. Qiao B, Zhang X, Gao J, Liu R, Chen X (2017) Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction. Mech Syst Signal Process 83:93–115

    Article  Google Scholar 

  21. Pan CD, Liu HL, Yu L (2019) A sparse self-estimated sensor-network for reconstructing moving vehicle forces. Smart Mater Struct 28:085009

    Article  Google Scholar 

  22. Yan G, Sun H (2019) A non-negative Bayesian learning method for impact force reconstruction. J Sound Vib 457:354–367. https://doi.org/10.1016/j.jsv.2019.06.013

    Article  Google Scholar 

  23. Qiao B, Liu J, Liu J, Yang Z, Chen X (2019) An enhanced sparse regularization method for impact force identification. Mech Syst Signal Process 126:341–367

    Article  Google Scholar 

  24. Liu J, Li B (2018) A novel strategy for response and force reconstruction under impact excitation. J Mech Sci Technol 32(8):3581–3596

    Article  Google Scholar 

  25. Aucejo M, De Smet O (2017) A multiplicative regularization for force reconstruction. Mech Syst Signal Process 85:730–745. https://doi.org/10.1016/j.ymssp.2016.09.011

    Article  Google Scholar 

  26. Pan CD, Yu L, Liu HL (2017) Identification of moving vehicle forces on bridge structures via moving average Tikhonov regularization. Smart Mater Struct 26(8):085041. https://doi.org/10.1088/1361-665X/aa7a48

    Article  Google Scholar 

  27. Pan C, Yu L, Liu H, Chen Z, Luo W (2018) Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization. Mech Syst Signal Process 98:32–49. https://doi.org/10.1016/j.ymssp.2017.04.032

    Article  Google Scholar 

  28. Tran H, Inoue H (2018) Development of wavelet deconvolution technique for impact force reconstruction: application to reconstruction of impact force acting on a load-cell. Int J Impact Eng 122:137–147. https://doi.org/10.1016/j.ijimpeng.2018.07.020

    Article  Google Scholar 

  29. Qiao B, Chen X, Xue X, Luo X, Liu R (2015) The application of cubic B-spline collocation method in impact force identification. Mech Syst Signal Process 64:413–427

    Article  Google Scholar 

  30. Liu J, Meng X, Jiang C, Han X, Zhang D (2016) Time-domain Galerkin method for dynamic load identification. Int J Numer Methods Eng 105(8):620–640

    Article  MathSciNet  Google Scholar 

  31. Prawin J, Rama Mohan Rao A (2018) An online input force time history reconstruction algorithm using dynamic principal component analysis. Mech Syst Signal Process 99:516–533. https://doi.org/10.1016/j.ymssp.2017.06.031

    Article  Google Scholar 

  32. Zhang Q, Jankowski Ł, Duan Z (2010) Identification of coexistent load and damage. Struct Multidiscip Optim 41(2):243–253

    Article  Google Scholar 

  33. Adeli H (2000) High-performance computing for large-scale analysis, optimization, and control. J Aerosp Eng 13(1):1–10

    Article  MathSciNet  Google Scholar 

  34. Noor AK (1997) New computing systems and future high-performance computing environment and their impact on structural analysis and design. Comput Struct 64(1–4):1–30

    Article  Google Scholar 

  35. Park K, Oh BK, Park HS, Choi SW (2015) GA-based multi-objective optimization for retrofit design on a multi-core PC cluster. Comput Civ Infrastruct Eng 30(12):965–980. https://doi.org/10.1111/mice.12176

    Article  Google Scholar 

  36. Adeli H, Kumar S (1995) Concurrent structural optimization on massively parallel supercomputer. J Struct Eng 121(11):1588–1597

    Article  Google Scholar 

  37. Coelho PG, Cardoso JB, Fernandes PR, Rodrigues HC (2011) Parallel computing techniques applied to the simultaneous design of structure and material. Adv Eng Softw 42(5):219–227. https://doi.org/10.1016/j.advengsoft.2010.10.003

    Article  MATH  Google Scholar 

  38. Lu X, Han B, Hori M, Xiong C, Xu Z (2014) A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing. Adv Eng Softw 70:90–103. https://doi.org/10.1016/j.advengsoft.2014.01.010

    Article  Google Scholar 

  39. Li H, Li Z, Teng J (2016) A dynamic analysis algorithm for RC frames using parallel GPU strategies. Comput Concr 18(5):1019–1039

    Article  Google Scholar 

  40. Park K, Torbol M, Kim S (2018) Vision-based natural frequency identification using laser speckle imaging and parallel computing. Comput Civ Infrastruct Eng 33(1):51–63. https://doi.org/10.1111/mice.12312

    Article  Google Scholar 

  41. Adeli H, Kumar S (1995) Distributed genetic algorithm for structural optimization. J Aerosp Eng 8(3):156–163

    Article  Google Scholar 

  42. Ponz-Tienda JL, Salcedo-Bernal A, Pellicer E (2017) A parallel branch and bound algorithm for the resource leveling problem with minimal lags. Comput Civ Infrastruct Eng 32(6):474–498. https://doi.org/10.1111/mice.12233

    Article  Google Scholar 

  43. Zhao L, Zhou Y, Lu H, Fujita H (2019) Parallel computing method of deep belief networks and its application to traffic flow prediction. Knowl Based Syst 163:972–987

    Article  Google Scholar 

  44. Qiao B, Zhang X, Wang C, Zhang H, Chen X (2016) Sparse regularization for force identification using dictionaries. J Sound Vib 368:71–86

    Article  Google Scholar 

  45. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  Google Scholar 

  46. Li J, Hao H (2016) Substructural interface force identification with limited vibration measurements. J Civ Struct Heal Monit 6(3):395–410

    Article  MathSciNet  Google Scholar 

  47. Bauer F, Lukas MA (2011) Comparing parameter choice methods for regularization of ill-posed problems. Math Comput Simul 81(9):1795–1841

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant 51908149 and research fund (RP2020146) provided by Guangzhou University.

Author information

Authors and Affiliations

  1. School of Civil Engineering, Guangzhou University, Guangzhou, China

    Chudong Pan, Xiongjie Deng & Zhenjie Huang

Authors
  1. Chudong Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiongjie Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenjie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chudong Pan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, C., Deng, X. & Huang, Z. Parallel computing-oriented method for long-time duration problem of force identification. Engineering with Computers 38, 919–937 (2022). https://doi.org/10.1007/s00366-020-01097-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01097-6

Keywords

Search

Navigation