Utilizing Data-Driven Methods to Predict the Fatigue Life of Cement Concrete Considering Corrosive Environmental Factors
DOI:
https://doi.org/10.62676/3th69185Keywords:
Portland cement concrete, Corrosive environment, Axial fatigue test, Fatigue life, Data mining techniquesAbstract
The primary objective of this study is to assess the fatigue resistance of cement concrete when exposed to corrosive environments. To achieve this, experimental results from high-cycle fatigue (HCF) and low-cycle fatigue (LCF) tests conducted on cement concrete samples subjected to various corrosive conditions were used. Various data-driven techniques, including multiple linear regression (MLR), Taguchi sensitivity analysis (TSA), and response surface method (RSM) were utilized. The aim was not only to identify the most influential parameter affecting fatigue life but also to offer a simpler and cost-effective alternative to experimental approaches. Consequently, two key parameters related to the corrosive environment: pH value and immersion time, along with the cyclic force applied to the concrete samples as input variables across different approaches were considered. The number of cycles until sample failure regarded as the output variable in all analyses. Furthermore, the analyses were conducted with the assumption that longer fatigue life is preferable. The findings revealed that the fatigue life of Portland cement concrete consistently decreased with increasing immersion time. Notably, the pH value emerged as the most significant parameter, while the other two factors exhibited equivalent impacts.
References
1. G.K. Glass, N.R. Buenfeld, Chloride‐induced corrosion of steel in concrete, Progress in Structural Engineering and Materials 2(4) (2000) 448-458, https://doi.org/10.1002/pse.54.
2. Y. Tian, G. Zhang, H. Ye, Q. Zeng, Z. Zhang, Z. Tian, X. Jin, N. Jin, Z. Chen, J. Wang, Corrosion of steel rebar in concrete induced by chloride ions under natural environments, Construction and Building Materials 369 (2023) 130504, https://doi.org/10.1016/j.conbuildmat.2023.130504.
3. J. Cao, L. Liu, S. Zhao, Relationship between Corrosion of Reinforcement and Surface Cracking Width in Concrete, Advances in Civil Engineering 2020 1-14, https://doi.org/10.1155/2020/7936861.
4. M.W.T. Mak, P. Desnerck, J.M. Lees, Corrosion-induced cracking and bond strength in reinforced concrete, Construction and Building Materials 208 (2019) 228-241, https://doi.org/10.17863/CAM.37733.
5. Z.H. Lu, P.Y. Lun, W. Li, Z. Luo, Y. Li, P. Liu, Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments, Advances in Structural Engineering 22(1) (2019) 223-239., https://doi.org/10.1177/1369433218783313.
6. F. Guo, S. Al-Saadi, R.K. Singh Raman, X. Zhao, Durability of Fibre Reinforced Polymers in Exposure to Dual Environment of Seawater Sea Sand Concrete and Seawater, Materials 15(14) (2022) 4967, https://doi.org/10.3390/ma15144967.
7. M. Golestaneh, G. Najafpour, G. Amini, M. Beygi, Evaluation of chemical resistance of polymer concrete in corrosive environments, Iranica Journal of Energy & Environment 4(3) (2013) 304-310, https://doi.org/10.5829/idosi.ijee.2013.04.03.19.
8. K. Reza Kashyzadeh, Effect of Corrosive Environment on the High-Cycle Fatigue Behavior of Reinforced Concrete by Epoxy Resin: Experimental Study, Polymers 15(19) (2023) 3939, https://doi.org/10.3390/polym15193939.
9. K. Reza Kashyzadeh, S. Ghorbani, M. Forouzanmehr, Effects of drying temperature and aggregate shape on the concrete compressive strength: Experiments and data mining techniques, International Journal of Engineering 33(9) (2020) 1780-1791, https://doi.org/10.5829/ije.2020.33.09c.12.
10. K. Reza Kashyzadeh, N. Amiri, S. Ghorbani, K. Souri, Prediction of concrete compressive strength using a back-propagation neural network optimized by a genetic algorithm and response surface analysis considering the appearance of aggregates and curing conditions, Buildings 12(4) (2022) 438, https://doi.org/10.3390/buildings12040438.
11. ISO-1920-3; Testing of Concrete-Part 3: Making and Curing Test Specimens. International Standard Organization: Geneva, Switzerland, 2004.
12. ACI-211.1-91; Standard Practice for Selecting Proportions for Normal, Heavyweight, and Mass Concrete. American Concrete Institute: Farmington Hills, MI, USA, 2002.
13. K. Reza Kashyzadeh, E. Maleki, Experimental investigation and artificial neural network modeling of warm galvanization and hardened chromium coatings thickness effects on fatigue life of AISI 1045 carbon steel, Journal of Failure Analysis and Prevention 17 (2017) 1276-1287, https://doi.org/10.1007/s11668-017-0362-8.
14. A. Arghavan, K. Reza Kashyzadeh, A. A. Asfarjani, Investigating effect of industrial coatings on fatigue damage, Applied Mechanics and Materials 87 (2011) 230-237, https://doi.org/10.4028/www.scientific.net/AMM.87.230.
15. K.R. Kashyzadeh, A. Arghavan, Study of the effect of different industrial coating with microscale thickness on the CK45 steel by experimental and finite element methods, Strength of materials 45 (2013) 748-757, https://doi.org/10.1007/s11223-013-9510-x.
16. H.A. Al-Jamimi, W.A. Al-Kutti, S. Alwahaishi, K.S. Alotaibi, Prediction of compressive strength in plain and blended cement concretes using a hybrid artificial intelligence model, Case Studies in Construction Materials 17 (2022) e01238, https://doi.org/10.1016/j.cscm.2022.e01238.
17. N.P. Rajamane, J.A. Peter, P.S. Ambily, Prediction of compressive strength of concrete with fly ash as sand replacement material, Cement and concrete composites 29(3) (2007) 218-223, https://doi.org/10.1016/j.cemconcomp.2006.10.001.
18. S. Paudel, A. Pudasaini, R.K. Shrestha, E. Kharel, Compressive strength of concrete material using machine learning techniques, Cleaner Engineering and Technology 15 (2023) 100661, https://doi.org/10.1016/j.clet.2023.100661.
19. G.H. Farrahi, K.R. Kashyzadeh, M. Minaei, A. Sharifpour, S. Riazi, Analysis of resistance spot welding process parameters effect on the weld quality of three-steel sheets used in automotive industry: Experimental and finite element simulation, International Journal of Engineering 33(1) (2020) 148-157, https://doi.org/10.5829/ije.2020.33.01a.17.
20. M. Omidi Bidgoli, K. Reza Kashyzadeh, S.S. Rahimian Koloor, M. Petru, Estimation of critical dimensions for the crack and pitting corrosion defects in the oil storage tank using finite element method and taguchi approach, Metals 10(10) (2020) 1372, https://doi.org/10.3390/met10101372.
21. A. Freddi, M. Salmon, A. Freddi, M. Salmon, Introduction to the Taguchi method, Design principles and methodologies: from conceptualization to first prototyping with examples and case studies (2019) 159-180. https://doi.org/10.1007/978-3-319-95342-7_7.
22. E. Maleki, O. Unal, K. Reza Kashyzadeh, Influences of shot peening parameters on mechanical properties and fatigue behavior of 316 L steel: Experimental, Taguchi method and response surface methodology, Metals and Materials International 27(11) (2021) 4418-4440. https://doi.org/10.1007/s12540-021-01013-7.
23. K.R. Kashyzadeh, G.H. Farrahi, Improvement of HCF life of automotive safety components considering a novel design of wheel alignment based on a Hybrid multibody dynamic, finite element, and data mining techniques, Engineering Failure Analysis 143 (2023) 106932. https://doi.org/10.1016/j.engfailanal.2022.106932.
[24] K.R. Kashyzadeh, S.S. Rahimian Koloor, M. Omidi Bidgoli, M. Petrů, A. Amiri Asfarjani, An optimum fatigue design of polymer composite compressed natural gas tank using hybrid finite element-response surface methods, Polymers 13(4) (2023) 483. https://doi.org/10.3390/polym13040483.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Design Against Fatigue
This work is licensed under a Creative Commons Attribution 4.0 International License.
