Statistical analysis and Response Surface Modelling of the compressive strength inhibition of crude oil in concrete test cubes

  • Sebastian Azowenu Nwose Department of Civil Engineering, Delta State Polytechnic, Ogwashi-Uku, Nigeria
  • Francis Edoziuno Department of Metallurgical Engineering, Delta State Polytechnic, Ogwashi-Uku (DSPG), Nigeria.
  • Sylvester Osuji Department of Civil Engineering, University of Benin, Benin city, Nigeria
Keywords: Concrete; Crude oil; RSM modeling; Compressive Strength; Optimization

Abstract

The use of crude oil contaminated fine aggregates in the production of concrete significantly affect the properties of such concrete, especially the compressive strength. In the present investigation, response surface methodology (RSM) of the Design-Expert software version 11.1.0.1 was used for the statistical analysis and predictive modelling of the compressive strength of concrete cubes made from crude oil contaminated fine aggregates at 7, 14, 28, and 56 days curing periods. The fine aggregates were mixed with varying concentrations of crude oil contamination (ranging from 0% to 5% by weight of the fine aggregates, at 1% interval). Concrete test cubes were produced for compressive strength determination and prediction phase of the modelling. A steady reduction in the compressive strength of the concrete cubes was recorded as the crude oil content increases, due to the inhibitive and surface shielding influence of crude oil molecules on the fine aggregates, thereby hindering physical bond formation between the cement paste and the aggregates. Statistical analysis of the output/response was carried out; a correlation coefficient of 0.9923 was obtained. The result of the modelling has shown that the use of RSM is adequate in the prediction of the compressive strength inhibition of crude oil in concrete made from crude oil-contaminated sand.

DOI

Cite as:

Nwose SA, Edoziuno FO, Osuji SO. Statistical analysis and Response Surface Modelling of the compressive strength inhibition of crude oil in concrete test cubes. Alger. J. Eng. Technol. 2021, 4:99-107.  http://dx.doi.org/10.5281/zenodo.4696030

References

  1. Osuji S, Nwankwo E. Effect of Crude Oil Contamination on the Compressive Strength of Concrete. Niger J Technol. 2015;34(2):259–65.
  2. Alsadey S. Effects of Used Engine Oil as Chemical Admixture in Concrete. Int J Energy Sustain Dev. 2018;3(2):38–43.
  3. Ejeh SP, Uche OAU. Effect of Crude Oil Spill on Compressive Strength of Concrete Materials. J Appl Sci Res. 2009;5(10):1756–61.
  4. Nwobi-okoye CC, Umeonyiagu IE. Modelling the effects of petroleum product contaminated sand on the compressive strength of concretes using fuzzy logic and artificial neural networks : A case study of diesel. African J Sci Technol Innov Dev. 2016;8(3):264–74.
  5. Kadhum MM, Alwash NA, Tuama WK, Abdulraheem MS. Experimental and numerical study of influence of crude oil products on the behavior of reactive powder and normal strength concrete slabs. J King Saud Univ - Eng Sci. 2020;32:293–302.
  6. Ajagbe WO, Ganiyu AA, Owoyele MO, Labiran JO. Modeling the Effect of Crude Oil Impacted Sand on the Properties of Concrete Using Artificial Neural Networks. ISRN Civ Eng. 2013;2013:609379.
  7. Shafiq N, Siew C, Hasnain M. Effects of used engine oil on slump , compressive strength and oxygen permeability of normal and blended cement concrete. Constr Build Mater [Internet]. 2018;187:178–84. Available from: https://doi.org/10.1016/j.conbuildmat.2018.07.195
  8. Attom M, Hawileh R, Naser M. Investigation on Concrete Compressive Strength Mixed with Sand Contaminated by Crude Oil Products. J Constr Build Mater. 2013;47:99–103.
  9. Abousnina RM, Manalo A, Shiau J, Lokuge W. Effects of light crude oil contamination on the physical and mechanical properties of fine sand. Soil Sediment Contam An Int J. 2015;
  10. Abousnina RM, Manalo A, Shiau J, Lokuge W. An Overview on Oil Contaminated Sand and its Engineering Applications. Int J Geomaterials. 2016;10(1):1615–22.
  11. Al-lami MS, Hassan WM. Effect of using Sand Contaminated with Petroleum Products on Mechanical Properties of Concrete. Int J Appl Eng Res. 2017;12(24):15332–6.
  12. Diab H. Compressive strength performance of low- and high-strength concrete soaked in mineral oil. Constr Build Mater [Internet]. 2012;33:25–31. Available from: http://dx.doi.org/10.1016/j.conbuildmat.2012.01.015
  13. Shahrabadi H, Vafaei D. Effect of kerosene impacted sand on compressive strength of concrete in different exposure conditions. J Mater Environ Sci. 2015;6(9):2665–72.
  14. Shahrabadi H, Sayareh S, Sarkardeh H. Effect of Silica Fume on Compressive Strength of Oil-Polluted Concrete in Different Marine Environments. China Ocean Eng. 2017;31(6):716–23.
  15. Odoni BU, Edoziuno FO, Nwaeju CC, Akaluzia RO. Experimental analysis , predictive modelling and optimization of some physical and mechanical properties of aluminium 6063 alloy based composites reinforced with corn cob ash . J Mater Eng Struct. 2020;7:451–65.
  16. Khalkkhali A, Nikghalb E, Norouzian M. Multi-objective Optimization of Hybrid Carbon/Glass Fiber Reinforced Epoxy Composite Automotive Drive Shaft. Int J Eng Trans A Basics. 2015;28(4):583–92.
  17. Okafor EC, Ihueze CC, Nwigbo SC. Optimization of Hardness Strengths Response of Plantain Fibers Reinforced Polyester Matrix Composites ( PFRP ) Applying Taguchi Robust Design. Int J Eng Trans A Basics. 2013;26(1):1–11.
  18. Ali SM. Optimization of Centrifugal Casting Parameters of AlSi Alloy by using the Response Surface Methodology. Int J Eng. 2019;32(11):1516–26.
  19. Nwobi-okoye CC, Okonji PC, Okiy S. Optimization of dry compressive strength of groundnut shell ash particles ( GSAp ) and ant hill bonded foundry sand using ann and genetic algorithm. Cogent Eng [Internet]. 2019;6(1):1–17. Available from: https://doi.org/10.1080/23311916.2019.1681055
  20. Rashmi M, Nitin G, Durgesh J. Prediction of Moulding Sand Properties Using Multiple Regression Methodology. J Adv Comput Commun Technol. 2016;4(1):1–4.
  21. Cihan MT. Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods. Adv Civ Eng. 2019;2019:3069046.
  22. Nur W, Wan F, Ismail MA, Lee H, Seddik M, Kumar J, et al. Mixture optimization of high-strength blended concrete using central composite design. Constr Build Mater [Internet]. 2020;243:118251. Available from: https://doi.org/10.1016/j.conbuildmat.2020.118251
  23. Sayed-ahmed M. Statistical Modelling and Prediction of Compressive Strength of Concrete. Concr Res Lett. 2012;3(2):452–8.
  24. Liu G, Zheng J. Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method. Appl Sci. 2019;9:1039.
  25. Kothari CR, Garg G. Research Methodology: Methods and Techniques. 3rd ed. New Delhi: New Age International (P) Ltd., Publishers; 2014.
  26. Edoziuno FO, Akaluzia RO, Odoni BU, Edibo S. Experimental Study on Tribological (Dry Sliding Wear) Behaviour of Polyester Matrix Hybrid Composite Reinforced With Particulate Wood Charcoal and Periwinkle Shell. J King Saud Univ - Eng Sci [Internet]. 2020; Available from: https://doi.org/10.1016/j.jksues.2020.05.007
  27. Edoziuno FO, Adediran AA, Odoni BU, Akinwekomi AD, Adesina OS, Oki M. Optimization and development of predictive models for the corrosion inhibition of mild steel in sulphuric acid by methyl-5-benzoyl- 2-benzimidazole carbamate (mebendazole). Cogent Eng [Internet]. 2020;7(1):1714100. Available from: https://doi.org/10.1080/23311916.2020.1714100
  28. Abousnina R, Manalo A, Lokuge W, Al-jabri KS. Properties and structural behavior of concrete containing fine sand contaminated with light crude oil. Constr Build Mater [Internet]. 2018;189:1214–31. Available from: https://doi.org/10.1016/j.conbuildmat.2018.09.089
  29. Edoziuno FO, Adediran AA, Odoni BU, Akinwekomi AD, Adesina OS, Oki M. Optimization and development of predictive models for the corrosion inhibition of mild steel in sulphuric acid by methyl-5-benzoyl-2-benzimidazole carbamate (mebendazole). Cogent Eng. 2020;7(1):1714100.
  30. Aziminezhad M, Mahdikhani M, Memarpour MM. RSM-based modeling and optimization of self-consolidating mortar to predict acceptable ranges of rheological properties. Constr Build Mater [Internet]. 2018;189:1200–13. Available from: https://doi.org/10.1016/j.conbuildmat.2018.09.019

 

 

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Published
2021-04-16
How to Cite
1.
Nwose SA, Edoziuno F, Osuji S. Statistical analysis and Response Surface Modelling of the compressive strength inhibition of crude oil in concrete test cubes . Alger. J. Eng. Technol. [Internet]. 2021Apr.16 [cited 2021Sep.27];40:99-107. Available from: https://jetjournal.org/index.php/ajet/article/view/97