Skip to main content
Springer Nature Link
Log in

A novel inverse procedure for load identification based on improved artificial tree algorithm

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

Abstract

This paper presents an accurate and effective method, namely, the improved artificial tree algorithm based on the Green’s kernel function method (IAT-GKFM), to identify the load in time domain. The forward problem of load identification is constructed by using the Green’s kernel function method. The forward problem is discretized using the time domain Galerkin method, where a matrix form for load identification is formed. The IAT algorithm is proposed to solve the inverse multi-dimensions problem in the inverse stage, which aims to minimize the measuring dispersion between the calculated response and the actual response. Several numerical examples are conducted. It is demonstrated that the IAT with high performance can provide more optimum results than those of other compared algorithms. Using this optimized strategy, the loads acting on a simple plate and a vehicle roof are reconstructed successfully. The superiority of IAT-GKFM may motivate the improvement of the other inverse problems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Liu J, Meng X, Zhang D, Jiang C, Han X (2017) An efficient method to reduce ill-posedness for structural dynamic load identification. Mech Syst Signal Process 95:273–285

    Article  Google Scholar 

  2. Rad JA, Rashedi K, Parand K, Adibi H (2016) The meshfree strong form methods for solving one dimensional inverse Cauchy–Stefan problem. Eng Comput 33(3):1–25

    MATH  Google Scholar 

  3. Saeedi A, Pourgholi R (2017) Application of quintic B-splines collocation method for solving inverse Rosenau equation with Dirichlet’s boundary conditions. Eng Comput 33(3):1–14

    Article  Google Scholar 

  4. Shivanian E, Jafarabadi A (2016) Inverse Cauchy problem of annulus domains in the framework of spectral meshless radial point interpolation. Eng Comput 33(3):1–12

    MATH  Google Scholar 

  5. Yang HQ, Zhang L, Xue J, Jie Z, Xu L (2018) Unsaturated soil slope characterization with Karhunen–Loève and polynomial chaos via Bayesian approach. Eng Comput 2:1–14

    Google Scholar 

  6. Kaveh A, Dadras A, Geran Malek N (2019) Optimum stacking sequence design of composite laminates for maximum buckling load capacity using parameter-less optimization algorithms. Eng Comput 35(3):813–832. https://doi.org/10.1007/s00366-018-0634-2

    Article  Google Scholar 

  7. Pourgholi R, Tabasi SH, Zeidabadi H (2018) Numerical techniques for solving system of nonlinear inverse problem. Eng Comput 34(3):487–502. https://doi.org/10.1007/s00366-017-0554-6

    Article  MATH  Google Scholar 

  8. Thite AN, Thompson DJ (2003) The quantification of structure-borne transmission paths by inverse methods. Part 1: improved singular value rejection methods. J Sound Vib 264(2):411–431

    Article  Google Scholar 

  9. Thite AN, Thompson DJ (2003) The quantification of structure-borne transmission paths by inverse methods. Part 2: use of regularization techniques. J Sound Vib 264(2):433–451

    Article  Google Scholar 

  10. Liu GR, Ma WB, Han X (2002) An inverse procedure for identification of loads on composite laminates. Compos B 33(6):425–432

    Article  Google Scholar 

  11. Zhou J, Cheng Y, Zhang H, Huang G, Hu G (2015) Experimental study on interaction between a positive mass and a negative effective mass through a mass–spring system. Theor Appl Mech Lett 5(5):196–199

    Article  Google Scholar 

  12. Thite AN, Thompson DJ (2006) Selection of response measurement locations to improve inverse force determination. Appl Acoust 67(8):797–818. https://doi.org/10.1016/j.apacoust.2006.01.001

    Article  Google Scholar 

  13. Law SS, Fang YL (2001) Moving force identification: optimal state estimation approach. J Sound Vib 239(2):233–254. https://doi.org/10.1006/jsvi.2000.3118

    Article  Google Scholar 

  14. Liu J, Meng X, Zhang D, Jiang C, Han X (2017) An efficient method to reduce ill-posedness for structural dynamic load identification. Mech Syst Signal Process 95:273–285. https://doi.org/10.1016/j.ymssp.2017.03.039

    Article  Google Scholar 

  15. Qiao B, Zhang X, Luo X, Chen X (2015) A force identification method using cubic B-spline scaling functions. J Sound Vib 337:28–44. https://doi.org/10.1016/j.jsv.2014.09.038

    Article  Google Scholar 

  16. Yan G, Zhou L (2009) Impact load identification of composite structure using genetic algorithms. J Sound Vib 319(3):869–884

    Article  Google Scholar 

  17. Liu J, Sun X, Han X, Jiang C, Yu D (2014) A novel computational inverse technique for load identification using the shape function method of moving least square fitting. Comput Struct 144(C):127–137

    Article  Google Scholar 

  18. 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. https://doi.org/10.1016/j.camwa.2018.05.013

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang Q, Zhou W, Cheng Y, Ma G, Chang X, Miao Y, Chen E (2018) Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices. Appl Math Comput 325:120–145. https://doi.org/10.1016/j.amc.2017.12.017

    Article  MathSciNet  MATH  Google Scholar 

  20. Pan C-D, Yu L, Liu H-L, Chen Z-P, Luo W-F (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 

  21. Gao X, Sun Y (2017) A new heuristic algorithm for laser antimissile strategy optimization. J Ind Manag Optim 8(2):457–468

    MathSciNet  MATH  Google Scholar 

  22. Goldberg DE (2000) The design of innovation: lessons from genetic algorithms, lessons for the real world. Technol Forecast Soc Chang 64(1):7–12

    Article  Google Scholar 

  23. Onwunalu JE, Durlofsky LJ (2010) Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput Geosci 14(1):183–198

    Article  Google Scholar 

  24. Hao T, Yuan X, Huang Y, Wu X (2015) An improved gravitational search algorithm for solving short-term economic/environmental hydrothermal scheduling. Soft Comput 19(10):2783–2797

    Article  Google Scholar 

  25. Huang SJ (2001) Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches. IEEE Trans Energy Convers Ec 16(3):296–301

    Article  Google Scholar 

  26. Wei S, Guo X, Chao W, Wu D (2011) Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl-Based Syst 24(3):378–385

    Article  Google Scholar 

  27. Guan X, Chen G (2019) Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis. J Mech Sci Technol 33(1):129–138

    Article  Google Scholar 

  28. Jeong JH, Kim SH (2018) Optimization of thick wind turbine airfoils using a genetic algorithm. J Mech Sci Technol 32(7):3191–3199

    Article  Google Scholar 

  29. Liu Z, Li H, Zhu P (2019) Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design. J Mech Sci Technol 33(2):695–709

    Article  Google Scholar 

  30. Sudsawat S, Sriseubsai W (2018) Warpage reduction through optimized process parameters and annealed process of injection-molded plastic parts. J Mech Sci Technol 32(10):4787–4799

    Article  Google Scholar 

  31. Zadeh PM, Fakoor M, Mohagheghi M (2018) Bi-level optimization of laminated composite structures using particle swarm optimization algorithm. J Mech Sci Technol 32(4):1643–1652

    Article  Google Scholar 

  32. Qiu Y, Wang L, Xu X, Fang X, Pardalos PM (2018) A variable neighborhood search heuristic algorithm for production routing problems. Appl Soft Comput 66:311–318. https://doi.org/10.1016/j.asoc.2018.02.032

    Article  Google Scholar 

  33. Schuster Puga M, Tancrez J-S (2017) A heuristic algorithm for solving large location–inventory problems with demand uncertainty. Eur J Oper Res 259(2):413–423. https://doi.org/10.1016/j.ejor.2016.10.037

    Article  MathSciNet  MATH  Google Scholar 

  34. Brest J, Bošković B (2018) A heuristic algorithm for a low autocorrelation binary sequence problem with odd length and high merit factor. IEEE Access 6:4127–4134. https://doi.org/10.1109/ACCESS.2018.2789916

    Article  Google Scholar 

  35. Du Y, Yang N (2018) Analysis of image processing algorithm based on bionic intelligent optimization. Clust Comput. https://doi.org/10.1007/s10586-018-2198-8

    Article  Google Scholar 

  36. Bonyadi MR, Michalewicz Z (2017) Particle swarm optimization for single objective continuous space problems: a review. Evol Comput 25(1):1–54

    Article  Google Scholar 

  37. Prasanth RSS, Raj KH (2017) Optimization of straight cylindrical turning using artificial bee colony (ABC) algorithm. J Inst Eng 98(2):171–177

    Google Scholar 

  38. Tereshko V, Lee T (2002) How information-mapping patterns determine foraging behaviour of a honey bee colony. Open Syst Inf Dyn 9(02):181–193

    Article  MathSciNet  Google Scholar 

  39. Li QQ, Kai S, He ZC, Li E, Cheng AG, Tao C (2017) The artificial tree (AT) algorithm. Eng Appl Artif Intell 65:99–110

    Article  Google Scholar 

  40. Liu GR, Ma WB, Han X (2002) Inversion of loading time history using displacement response of composite laminates: three-dimensional cases. Acta Mech 157(1):223–234. https://doi.org/10.1007/BF01182166

    Article  MATH  Google Scholar 

  41. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  42. He ZC, Lin XY, Li E (2019) A non-contact acoustic pressure-based method for load identification in acoustic–structural interaction system with non-probabilistic uncertainty. Appl Acoust 148:223–237. https://doi.org/10.1016/j.apacoust.2018.12.034

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, People’s Republic of China

    Huxiu Xu & Liyuan Zhang

  2. College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, 410114, People’s Republic of China

    Qiqi Li

  3. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, People’s Republic of China

    Qiqi Li

Authors
  1. Huxiu Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Liyuan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Qiqi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qiqi Li.

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

Xu, H., Zhang, L. & Li, Q. A novel inverse procedure for load identification based on improved artificial tree algorithm. Engineering with Computers 37, 663–674 (2021). https://doi.org/10.1007/s00366-019-00848-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00848-4

Keywords