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Fatigue life prediction of concrete under cyclic compression based on gradient boosting regression tree

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Abstract

With the development of reinforced concrete bridges, the fatigue problem of concrete has attracted extensive attention. Failure occurs when concrete is subjected to cyclic compression that is less than the static compressive strength of concrete after a certain amount of cyclic loading (fatigue life). Compared to traditional methods with shortage of low accuracy, resulting uncertain safety problem, to address this issue, we utilized gradient boosting regression tree (GBRT) and multiple linear regression (MLR) to predict the fatigue life of concrete under repeated compression based on the collated 275 sets of experimental data. All prediction results were compared with that calculated by the existing European codes. Results analysis reveal that both proposed GBRT and MLR models have lower errors and higher correlation coefficients than that of existing formula/code models. Furthermore, the prediction accuracy of the GBRT model can be improved by using the existing classical formulas to reconstruct the features and selecting suitable features by ranking the feature importance, without increasing the number of features. Experimental results reveal that GBRT model can achieve the best performance with lowest RMSE and highest R2. Moreover, the sensitivity analysis revealed that the proposed GBRT model is insensitive to input characteristics of fatigue life, which can be used for accurate calculation and design analysis of concrete compressive fatigue life, providing important reference value for formulating concrete technical design specification based on fatigue life.

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References

  1. Yadav IN, Thapa KB (2020) Fatigue damage model of concrete materials. Theor Appl Fract Mech 108:102578. https://doi.org/10.1016/j.tafmec.2020.102578

    Article  Google Scholar 

  2. Shiri S, Pourgol-Mohammad M, Yazdani M (2014) Probabilistic assessment of fatigue life in fiber reinforced composites. In: ASME International mechanical engineering congress and exposition. American Society of Mechanical Engineers, p. V014T08A018

  3. Suzuki T, Ohtsu M, Shigeishi M (2007) Relative damage evaluation of concrete in a road bridge by AE rate-process analysis. Mater Struct 40:221–227. https://doi.org/10.1617/s11527-006-9133-9

    Article  Google Scholar 

  4. Su HZ, Tong JJ, Hu J, Wen ZP (2013) Experimental study on AE behavior of hydraulic concrete under compression. Meccanica 48:427–439. https://doi.org/10.1007/s11012-012-9641-3

    Article  Google Scholar 

  5. Choi S-J, Mun J-S, Yang K-H, Kim S-J (2016) Compressive fatigue performance of fiber-reinforced lightweight concrete with high-volume supplementary cementitious materials. Cem Concr Compos 73:89–97. https://doi.org/10.1016/j.cemconcomp.2016.07.007

    Article  Google Scholar 

  6. Shah SP, Chandra S (1970) Fracture of concrete subjected to cyclic and sustained loading. In: Journal Proceedings. pp 816–827

  7. Fitzka M, Karr U, Granzner M et al (2021) Ultrasonic fatigue testing of concrete. Ultrasonics 116:106521. https://doi.org/10.1016/j.ultras.2021.106521

    Article  Google Scholar 

  8. Ruiz G, Medeiros A, Zhang XX (2011) Experimental study of loading frequency effect on compressive fatigue behavior of plain and fiber reinforced concretes. Anales de Mecánica de la Fractura 29(9):535–540

  9. Baktheer A, Hegger J, Chudoba R (2019) Enhanced assessment rule for concrete fatigue under compression considering the nonlinear effect of loading sequence. Int J Fatigue 126:130–142. https://doi.org/10.1016/j.ijfatigue.2019.04.027

    Article  Google Scholar 

  10. Sinaie S, Heidarpour A, Zhao XL, Sanjayan JG (2015) Effect of size on the response of cylindrical concrete samples under cyclic loading. Constr Build Mater 84:399–408. https://doi.org/10.1016/j.conbuildmat.2015.03.076

    Article  Google Scholar 

  11. Comi C, Kirchmayr B, Pignatelli R (2012) Two-phase damage modeling of concrete affected by alkali–silica reaction under variable temperature and humidity conditions. Int J Solids Struct 49:3367–3380

    Article  Google Scholar 

  12. Liu Y, Pang J, Yao W (2021) Fatigue performance of rubber concrete in hygrothermal environment. Adv Mater Sci Eng 2021:1–11

    Article  Google Scholar 

  13. Tue NV, Mucha S (2006) Fatigue behaviour of high strength concrete under compression. Bautechnik 83:497–504

    Article  Google Scholar 

  14. Hsu TTC (1981) Fatigue of plain concrete. J Proc 78(4):292–305

    Google Scholar 

  15. Kim JK, Kim YY (1996) Experimental study of the fatigue behavior of high strength concrete. Cem Concr Res 26(10):1513–1523. https://doi.org/10.1016/0008-8846(96)00151-2

    Article  Google Scholar 

  16. Cui K, Xu L, Li X et al (2021) Fatigue life analysis of polypropylene fiber reinforced concrete under axial constant-amplitude cyclic compression. J Clean Prod 319:128610. https://doi.org/10.1016/j.jclepro.2021.128610

    Article  Google Scholar 

  17. Tarifa M, Ruiz G, Poveda E et al (2018) Effect of uncertainty on load position in the fatigue life of steel-fiber reinforced concrete under compression. Mater Struct 51:1–11

    Article  Google Scholar 

  18. Cachim PB, Figueiras JA, Pereira PAA (2002) Fatigue behavior of fiber-reinforced concrete in compression. Cem Concr Compos 24:211–217

    Article  MATH  Google Scholar 

  19. Tepfers R, Kutti T (1979) Fatigue strength of plain, ordinary, and lightweight concrete. In: Journal Proceedings. pp. 635–652

  20. Onoue K, Matsushita H (2012) Reduction mechanisms of fatigue strength of concrete under compression due to permeation of liquids. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2012.07.010

    Article  Google Scholar 

  21. Ma C-K, Awang AZ, Omar W (2018) Eccentricity-based design procedure of confined columns under compression and in-plane bending moment. Measurement 129:11–19. https://doi.org/10.1016/j.measurement.2018.07.012

    Article  Google Scholar 

  22. Viswanath S, LaFave JM, Kuchma DA (2021) Concrete compressive strain behavior and magnitudes under uniaxial fatigue loading. Constr Build Mater 296:123718. https://doi.org/10.1016/j.conbuildmat.2021.123718

    Article  Google Scholar 

  23. Zhang Q, Wang L (2021) Investigation of stress level on fatigue performance of plain concrete based on energy dissipation method. Constr Build Mater 269:121287. https://doi.org/10.1016/j.conbuildmat.2020.121287

    Article  Google Scholar 

  24. Code Committee 351 001 (2009) Regulations for Concrete- Bridges- Structural requirements and calculation methods. NEN 6723:2009

  25. CEN (2011) Eurocode 2: design of concrete structures – Concrete bridges – Design and detailing rules. NEN-EN 1992-2+C1:2011. Comité Européen de Normalisation, Brussels, Belgium

  26. Fib (2012) Model Code 2010: Final draft. Fib Bulletin 65–66. International Federation for Structural Concrete, Lausanne, Switzerland

  27. Lantsoght EOL, Van Der Veen C, De Boer A (2016) Proposal for the fatigue strength of concrete under cycles of compression. Constr Build Mater 107:138–156. https://doi.org/10.1016/j.conbuildmat.2016.01.007

    Article  Google Scholar 

  28. Abambres M, Lantsoght EOL (2019) ANN-based fatigue strength of concrete under compression. Materials (Basel). https://doi.org/10.3390/ma12223787

    Article  Google Scholar 

  29. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:1–31. https://doi.org/10.1016/j.neucom.2016.12.038

    Article  Google Scholar 

  30. Khademi F, Jamal SM, Deshpande N, Londhe S (2016) Predicting strength of recycled aggregate concrete using artificial neural network, Adaptive neuro-fuzzy inference system and multiple linear regression. Int J Sustain Built Environ. https://doi.org/10.1016/j.ijsbe.2016.09.003

    Article  Google Scholar 

  31. Khademi F, Akbari M, Jamal SM, Nikoo M (2017) Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng. https://doi.org/10.1007/s11709-016-0363-9

    Article  Google Scholar 

  32. Jiang C-S, Liang G-Q (2021) Modeling shear strength of medium- to ultra-high-strength concrete beams with stirrups using SVR and genetic algorithm. Soft Comput 25(16):10661–10675. https://doi.org/10.1007/s00500-021-06027-2

    Article  Google Scholar 

  33. Min C (2019) Multiple linear regression models. Applied Econometrics. Routledge, New York and London

    Chapter  Google Scholar 

  34. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining

  35. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232. https://doi.org/10.1214/aos/1013203451

    Article  MathSciNet  MATH  Google Scholar 

  36. Cai J, Xu K, Zhu Y et al (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Appl Energy 262:114566. https://doi.org/10.1016/j.apenergy.2020.114566

    Article  Google Scholar 

  37. Nunez I, Marani A, Nehdi ML (2020) Mixture optimization of recycled aggregate concrete using hybrid machine learning model. Materials (Basel). https://doi.org/10.3390/ma13194331

    Article  Google Scholar 

  38. Rathakrishnan V, Bt. Beddu S, Ahmed AN (2022) Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms. Sci Rep. https://doi.org/10.1038/s41598-022-12890-2

    Article  Google Scholar 

  39. Feng DC, Wang WJ, Mangalathu S et al (2021) Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Eng Struct. https://doi.org/10.1016/j.engstruct.2021.111979

    Article  Google Scholar 

  40. Zheng D, Wu R, Sufian M et al (2022) Flexural strength prediction of steel fiber-reinforced concrete using artificial intelligence. Materials (Basel). https://doi.org/10.3390/ma15155194

    Article  Google Scholar 

  41. Degtyarev VV (2022) Machine learning models for predicting bond strength of deformed bars in concrete. ACI Struct J. https://doi.org/10.14359/51734833

    Article  Google Scholar 

  42. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal. https://doi.org/10.1016/S0167-9473(01)00065-2

    Article  MathSciNet  MATH  Google Scholar 

  43. Saucedo L, Rena CY, Medeiros A et al (2013) A probabilistic fatigue model based on the initial distribution to consider frequency effect in plain and fiber reinforced concrete. Int J Fatigue 48:308–318

    Article  Google Scholar 

  44. Hohberg R (2004) About the fatigue behavior of concrete. TU Berlin, Berlin

    Google Scholar 

  45. Hordijk DA (1994) Comparative research in material properties of gravel concrete and limestone concrete, Phase II: Fatigue. Report 94-CON-R0270. TNO, Delft, The Netherlands

  46. Fehling E, Schmidt M, Teichmann T, Bunje K, Bornemann R, BM (2005) Development, durability and calculation of Ultra High Performance Concrete (UHPC), Forschungsbericht DFG FE 497/1–1. Universität Kassel

  47. CUR Committee C 33 (1983) Fatigue of Concrete, Part 1: Compressive Stresses, CUR Report 112. Dutch

  48. CUR Committee C 33 (1993) Fatigue of Concrete, Part 4: Compressive Stresses, CUR Report 163. Dutch

  49. Hordijk D, Wolsink G, De Vries J (1995) Fracture and fatigue behaviour of a high strength limestone concrete as compared to gravel concrete. Heron (Delft) 40(2):125–146

    Google Scholar 

  50. Klausen D (1978) Strength and damage of concrete after repeated stress cycles

  51. Lohaus L, Anders S (2006) High-cycle fatigue of ultra-high performance concrete–fatigue strength and damage development. In: Proceedings of the 2nd International Congress, Naples, Italy. pp. 5–8

  52. Petkovic G, Lenschow R, Stemland H, Rosseland S (1990) Fatigue of high-strength concrete. ACI Spec Publ 121(11):505–525

    Google Scholar 

  53. Wefer M (2010) Material behavior and Experimental Results of ultra-high strength concrete subjected to uniaxial fatigue

  54. Huang W (2017) The frequency domain estimate of fatigue damage of combined load effects based on the rain-flow counting. Mar Struct 52:34–49. https://doi.org/10.1016/j.marstruc.2016.11.004

    Article  Google Scholar 

  55. Hair JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis. In: Vectors

  56. Banga A, Ahuja R, Sharma SC (2021) Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-020-01049-9

    Article  Google Scholar 

  57. Costa EB, Fonseca B, Santana MA et al (2017) Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput Human Behav 73:247–256. https://doi.org/10.1016/j.chb.2017.01.047

    Article  Google Scholar 

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Acknowledgements

This study was co-supported by Guangxi Basic Ability Promotion Project for Young and Middle-aged Teachers (Grant No. 2023KY0266), and Guangxi Key Laboratory of Green Building Materials and Construction Industrialization (No. 22-J-21-2). The authors would like to thank them.

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Authors and Affiliations

  1. Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, College of Information Science and Engineering, Guilin University of Technology, Guilin, 541004, China

    Gui-Qin Liang

  2. Audit Division, Guilin University of Aerospace Technology, Guilin, 541004, China

    Xuan Chen

  3. College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China

    Bing-Yu Jiang

  4. College of Civil and Architecture Engineering, Guilin University of Technology, Guilin, 541004, China

    Chun-Song Jiang

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  1. Gui-Qin Liang
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Correspondence to Chun-Song Jiang.

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Liang, GQ., Chen, X., Jiang, BY. et al. Fatigue life prediction of concrete under cyclic compression based on gradient boosting regression tree. Mater Struct 56, 172 (2023). https://doi.org/10.1617/s11527-023-02262-1

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