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Development of equivalent stationary dynamic loads for moving vehicular loads using artificial intelligence-based finite element model updating

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Abstract

An equivalent relationship between stationary dynamic load and moving vehicular load is of necessity and importance for the fact that pavement responses from nondestructive testing devices with high speeds are usually validated with responses from falling weight deflectometer (FWD), which applies stationary dynamic loads to pavements. Also, two-dimensional (2D) axisymmetric finite element (FE) models with statinary dynamic loads are still popular to represent pavements in service conditions for their less storage space and computational time compared with three-dimensional (3D) FE models. This study aims to provide a methodology using the FE model updating implemented with artificial intelligence algorithms to obtain equivalent stationary dynamic loads applied in 2D axisymmetric FE pavement models for moving vehicular loads applied in 3D FE pavement models. The 2D axisymmetric FE models can eventually provide similar results as 3D FE models but with higher efficiency. Besides, obtained equivalent relationship is independent of structural and material properties such as layer thickness and moduli. This finding significantly extends the application of this equivalent relationship. Furthermore, techniques applied in this study can be used as references for problems in pavement materials and structures such as the model updating, model equivalency, and model optimization.

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References

  1. Elbagalati O, Elseifi M, Gaspard K, Zhang Z (2018) Development of the pavement structural health index based on falling weight deflectometer testing. Int J Pavement Eng 19(1):1–8

    Article  Google Scholar 

  2. Lytton RL (1989) Backcalculation of pavement layer properties. In Nondestructive testing of pavements and backcalculation of moduli, ASTM International

    Book  Google Scholar 

  3. Deng Y, Luo X, Zhang Y, Lytton RL (2020) Determination of complex modulus gradients of flexible pavements using falling weight deflectometer and artificial intelligence. Mater Struct 53(4):1–17

    Article  Google Scholar 

  4. Terzi S, Saltan M, Küçüksille EU, Karaşahin M (2013) Backcalculation of pavement layer thickness using data mining. Neural Comput Appl 23(5):1369–1379

    Article  Google Scholar 

  5. Sangghaleh A, Pan E, Green R, Wang R, Liu X, Cai Y (2014) Backcalculation of pavement layer elastic modulus and thickness with measurement errors. Int J Pavement Eng 15(6):521–531

    Article  Google Scholar 

  6. Saltan M, Terzi S, Küçüksille EU (2011) Backcalculation of pavement layer moduli and Poisson’s ratio using data mining. Expert Syst Appl 38(3):2600–2608

    Article  Google Scholar 

  7. Li M, Wang H (2019) Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. Int J Pavement Eng 20(4):490–498

    Article  Google Scholar 

  8. Wang H, Al-Qadi IL (2009) Combined effect of moving wheel loading and three-dimensional contact stresses on perpetual pavement responses. Transp Res Rec 2095(1):53–61

    Article  Google Scholar 

  9. Deng Y, Luo X, Gu F, Zhang Y, Lytton RL (2019) 3D simulation of deflection basin of pavements under high-speed moving loads. Constr Build Mater 226:868–878

    Article  Google Scholar 

  10. Steele DA, Vavrik WR (2004) Rolling wheel deflectometer (RWD) demonstration and comparison to other devices in Texas. ARA Project, 15874

  11. Carlson P, Storey B, Poorsartep M, Stevens C, Ettelman B, Lindheimer TE, Dastgiri M, Khodakarami A, Miles J, Song D, Lytton RL (2017) Advancing innovative high-speed remote-sensing highway infrastructure assessment using emerging technologies: technical report (No. FHWA/TX-16/0–6869–1). Texas A&M Transportation Institute.

  12. Elseifi MA, Gaspard K, Wilke PW, Zhang Z, Hegab A (2015) Evaluation and validation of a model for predicting pavement structural number with rolling wheel deflectometer data. Transp Res Rec 2525(1):13–19

    Article  Google Scholar 

  13. Elbagalati O, Elseifi MA, Gaspard K, Zhang Z (2017) Implementation of the structural condition index into the Louisiana pavement management system based on rolling wheel deflectometer testing. Transp Res Rec 2641(1):39–47

    Article  Google Scholar 

  14. Maser K, Schmalzer P, Shaw W, Carmichael A (2017) Integration of traffic speed deflectometer and ground-penetrating radar for network-level roadway structure evaluation. Transp Res Rec 2639(1):55–63

    Article  Google Scholar 

  15. Flintsch GW, Ferne B, Diefenderfer B, Katicha S, Bryce J, Nell S (2012) Evaluation of traffic-speed deflectometers. Transp Res Rec 2304(1):37–46

    Article  Google Scholar 

  16. Muller WB, Roberts J (2013) Revised approach to assessing traffic speed deflectometer data and field validation of deflection bowl predictions. Int J Pavement Eng 14(4):388–402

    Article  Google Scholar 

  17. Zofka A, Sudyka J, Maliszewski M, Harasim P, Sybilski D (2014) Alternative approach for interpreting traffic speed deflectometer results. Transp Res Rec 2457(1):12–18

    Article  Google Scholar 

  18. Nasimifar M, Thyagarajan S, Siddharthan RV, Sivaneswaran N (2016) Robust deflection indices from traffic-speed deflectometer measurements to predict critical pavement responses for network-level pavement management system application. J Transp Eng 142(3):04016004

    Article  Google Scholar 

  19. Elseifi MA, Abdel-Khalek AM, Gaspard K, Zhang Z, Ismail S (2011) Evaluation of continuous deflection testing using the rolling wheel deflectometer in Louisiana. J Transp Eng 138(4):414–422

    Article  Google Scholar 

  20. Madsen SS, Pedersen NL (2019) Backcalculation of Raptor (RWD) Measurements and Forward Prediction of FWD Deflections Compared with FWD Measurements. In International airfield and highway pavements conference 2019 (pp 382–391). American Society of Civil Engineers.

  21. Gedafa D, Hossain M, Miller R, Steele D (2012) Surface deflections of perpetual pavement sections. Current trends, advances, and challenges, In Pavement Performance (ASTM International)

    Book  Google Scholar 

  22. Katicha SW, Flintsch GW, Ferne B, Bryce J (2014) Limits of agreement method for comparing TSD and FWD measurements. Int J Pavement Eng 15(6):532–541

    Article  Google Scholar 

  23. Levenberg E, Pettinari M, Baltzer S, Christensen BML (2018) Comparing traffic speed deflectometer and falling weight deflectometer data. Transp Res Rec 2672(40):22–31

    Article  Google Scholar 

  24. Loulizi A, Al-Qadi IL, Lahouar S, Freeman TE (2002) Measurement of vertical compressive stress pulse in flexible pavements: representation for dynamic loading tests. Transp Res Rec 1816(1):125–136

    Article  Google Scholar 

  25. Qin J (2010) Predicting flexible pavement structural response using falling weight deflectometer deflections. Master thesis, Ohio University, Athens, OH

  26. Leiva-Villacorta F, Timm D (2013) Falling weight deflectometer loading pulse duration and its effect on predicted pavement responses. In: Transportation Research Board 92nd Annual Meeting, Washington DC, United States.

  27. Wang H, Al-Qadi IL (2012) Importance of nonlinear anisotropic modeling of granular base for predicting maximum viscoelastic pavement responses under moving vehicular loading. J Eng Mech 139(1):29–38

    Google Scholar 

  28. Kim M (2007) Three-dimensional finite-element analysis of flexible pavements considering nonlinear pavement foundation behavior. PhD dissertation. University of Illinois, Urbana-Champaign, Urbana, IL

  29. Li M, Wang H, Xu G, Xie P (2017) Finite-element modeling and parametric analysis of viscoelastic and nonlinear pavement responses under dynamic FWD loading. Constr Build Mater 141:23–35

    Article  Google Scholar 

  30. Wang H, Li M (2016) Comparative study of asphalt pavement responses under FWD and moving vehicular loading. J Transp Eng 142(12):04016069

    Article  Google Scholar 

  31. Al-Qadi IL, Xie W, Elseifi MA (2008) Frequency determination from vehicular loading time pulse to predict appropriate complex modulus in MEPDG. Asphalt Paving Technol Proc 77:739

    Google Scholar 

  32. Ulloa A, Hajj EY, Siddharthan RV, Sebaaly PE (2012) Equivalent loading frequencies for dynamic analysis of asphalt pavements. J Mater Civ Eng 25(9):1162–1170

    Article  Google Scholar 

  33. Manual AUS (2012) Abaqus Version 6.12 Documentation. Dassault systemes SIMULIA corporation, Providence, RI, USA

  34. Herr WJ, Hall JW, White TD, Johnson W (1995) Continuous deflection basin measurement and backcalculation under a rolling wheel load using scanning laser technology. In: Proceedings of ASCE 1995 Transportation Congress, Vol. 1 and 2, San Diego, pp 600–611

  35. Friswell M, Mottershead JE (2013) Finite-element model updating in structural dynamics (vol 38). Springer, Berlin

  36. Qin S, Zhang Y, Zhou YL, Kang J (2018) Dynamic model updating for bridge structures using the kriging model and PSO algorithm ensemble with higher vibration modes. Sensors 18(6):1879

    Article  Google Scholar 

  37. Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  38. Shabbir F, Omenzetter P (2015) Particle swarm optimization with sequential niche technique for dynamic finite-element model updating. Comput Aided Civ Infrastruct Eng 30(5):359–375

    Article  Google Scholar 

  39. Lu P, Chen S, Zheng Y (2012) Artificial intelligence in civil engineering. In: Mathematical Problems in Engineering, vol 2012

  40. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Metaheuristic applications in structures and infrastructures. Newnes, London

    Google Scholar 

  41. Bergh FVD (2001) An analysis of particle swarm optimizers. Doctoral dissertation, University of Pretoria, Pretoria, South Africa

  42. Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753) (Vol 1, pp 325–331) IEEE

  43. Sun J, Xu W, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: IEEE conference on cybernetics and intelligent systems, Vol 1, pp 111–116, IEEE

  44. Yang ZL, Wu A, Min HQ (2015) An improved quantum-behaved particle swarm optimization algorithm with elitist breeding for unconstrained optimization. Comput Intell Neurosci 41

  45. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  46. Serrano NMB, Nieto PJG, Sánchez AS, Lasheras FS, Fernández PR (2018) A Hybrid Algorithm for the Assessment of the Influence of Risk Factors in the Development of Upper Limb Musculoskeletal Disorders. International Conference on Hybrid Artificial Intelligence Systems. Springer, Cham, pp 634–646

    Google Scholar 

  47. Hao H, Xia Y (2002) Vibration-based damage detection of structures by genetic algorithm. J Comput Civ Eng 16(3):222–229

    Article  Google Scholar 

  48. Caicedo JM, Yun G (2011) A novel evolutionary algorithm for identifying multiple alternative solutions in model updating. Struct Health Monit 10(5):491–501

    Article  Google Scholar 

  49. Syswerda G (1991) A study of reproduction in generational and steady-state genetic algorithms. In: Foundations of genetic algorithms, Vol 1, pp 94–101. Elsevier

  50. Brill ED, Flach JM, Hopkins LD, Ranjithan SMGA (1990) MGA: a decision support system for complex, incompletely defined problems. IEEE Trans Syst Man Cybern 20(4):745–757

    Article  Google Scholar 

  51. Baugh JW Jr, Caldwell SC, Brill ED Jr (1997) A mathematical programming approach for generating alternatives in discrete structural optimization. Eng Optim 28(1–2):1–31

    Article  Google Scholar 

  52. Loughlin DH, Ranjithan SR, Brill ED Jr, Baugh JW Jr (2001) Genetic algorithm approaches for addressing unmodeled objectives in optimization problems. Eng Optim 33(5):549–569

    Article  Google Scholar 

  53. Omkar SN, Khandelwal R, Ananth TVS, Naik GN, Gopalakrishnan S (2009) Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures. Expert Syst Appl 36(8):11312–11322

    Article  Google Scholar 

  54. Gu F, Luo X, Luo R, Lytton RL, Hajj EY, Siddharthan RV (2016) Numerical modeling of geogrid-reinforced flexible pavement and corresponding validation using large-scale tank test. Constr Build Mater 122:214–230

    Article  Google Scholar 

  55. Zhang Y, Gu F, Luo X, Birgisson B, Lytton RL (2018) Modeling Stress-dependent anisotropic elastoplastic unbound granular base in flexible pavements. Transp Res Rec 2672(52):46–56

    Article  Google Scholar 

  56. Zhang Y, Birgisson B, Lytton RL (2015) Weak form equation–based finite-element modeling of viscoelastic asphalt mixtures. J Mater Civ Eng 28(2):04015115

    Article  Google Scholar 

  57. De Beer M, Fisher C, Jooste FJ (2002) Evaluation of non-uniform tyre contact stresses on thin asphalt pavements. Ninth international conference on asphalt pavements 5:19–22

    Google Scholar 

  58. Wang H, Al-Qadi IL, Stanciulescu I (2012) Simulation of tyre–pavement interaction for predicting contact stresses at static and various rolling conditions. Int J Pavement Eng 13(4):310–321

    Article  Google Scholar 

  59. Ling M, Luo X, Hu S, Gu F, Lytton RL (2017) Numerical modeling and artificial neural network for predicting J-integral of top-down cracking in asphalt pavement. Transp Res Rec 2631(1):83–95

    Article  Google Scholar 

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Authors and Affiliations

  1. Zachry Department of Civil and Environmental Engineering, Texas A&M University, 3136 TAMU, College Station, TX, 77843, USA

    Yong Deng & Robert L. Lytton

  2. CCCC Second Highway Consultants Co., Ltd, Wuhan, 430052, Hubei, China

    Yazhou Zhang

  3. College of Civil Engineering and Architecture, Zhejiang University, 866 Yuhangtang Road, An-zhong Bldg., Hangzhou, 310058, China

    Xue Luo

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  1. Yong Deng
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  2. Yazhou Zhang
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  4. Robert L. Lytton
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Correspondence to Yazhou Zhang.

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Deng, Y., Zhang, Y., Luo, X. et al. Development of equivalent stationary dynamic loads for moving vehicular loads using artificial intelligence-based finite element model updating. Engineering with Computers 38, 2955–2974 (2022). https://doi.org/10.1007/s00366-021-01306-w

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  • DOI: https://doi.org/10.1007/s00366-021-01306-w

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