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Exploring the potential of soft computing for predicting compressive strength and slump flow diameter in fly ash-modified self-compacting concrete

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Abstract

Self-compacted concrete (SCC) is one of the special types of concrete. The SCC represents one of the most significant developments in concrete technology over the previous two decades. It can compact itself using its weight without requiring vibration due to its excellent fresh characteristics, which allow it to flow into a uniform level under the impact of gravity. Since cement manufacturing is one of the largest contributors to CO2 gas emissions into the atmosphere, fly ash (FA) is used in concrete as a cement replacement. Currently, FA-modified SCC is widely utilized in construction. This research aimed to study the potential of soft computing models in predicting the compressive strength (CS) and slump flow diameter (SL) of self-compacted concrete modified with different fly ash content. Hence, two databases were created, and relevant experimental data was collected from previous studies. The first database consists of 303 data points and is used to predict the CS. The second database predicts the SL and contains 86 data points. The dependent parameters are the CS, which varies from 9.7 to 79.2 MPa, and the SL, which varies from 615 to 800 mm. The identical five independent parameters are available in each database. The ranges for CS prediction are water-to-binder ratio (0.27–0.9), cement (134.7–540 kg/m3), sand (478–1180 kg/m3), fly ash (0–525 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4%). The data ranges for the SL prediction, on the other hand, are as follows: water-to-binder ratio (0.26–0.58), cement (83–733 kg/m3), sand (624–1038 kg/m3), fly ash (0–468 kg/m3), coarse aggregate (590–966 kg/m3), and superplasticizer (0.1–21.84%). Each database has developed three models for the prediction: full-quadratic (FQ), interaction (IN), and M5P-tree models. Each database is divided into two groups, with training comprising two-thirds of the total data points and testing containing one-third. As a result, 202 training data and 101 testing data are in the first database. The other database consists of 57 data points for training and 29 for testing. Various statistical tools are used to evaluate the performance of each proposed model, such as R2 (correlation of coefficient), RMSE (root mean squared error), SI (scatter index), MAE (mean absolute error), StDev, OBJ (objective value), a-20 index, and Z-score. The results showed that the FQ and IN models have the highest accuracy and reliability in predicting the compressive strength and slump flow of FA-based SCC, respectively. Moreover, the sensitivity analysis revealed that the cement content is the most influential contributor to the mixtures.

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References

  1. Okamura H. Self-compacting high-performance concrete. Concr Int Design Constr. 1997;19(7):50–4.

    Google Scholar 

  2. Ozawa K. Development of high performance concrete based on the durability design of concrete structures. EASEC-2. 1989;1:445–50.

    Google Scholar 

  3. Khambra G, Shukla P. Novel machine learning applications on fly ash based concrete: an overview. Mater Today Proc. 2023;80:3411–7.

    Article  Google Scholar 

  4. Shi C et al. Design and application of self-compacting lightweight concrete. In: SCC’2005-China: 1st international symposium on design, performance and use of self-consolidating concrete. London: RILEM Publications SARL; 2005.

  5. Shi C, Wu Y. Mixture proportioning and properties of self-consolidating lightweight concrete containing glass powder. ACI Mater J. 2005;102(5):1.

    Google Scholar 

  6. Goodier CI, Development of self-compacting concrete; 2003.

  7. Liu YH, Xie YJ, Long GC. Development of self-compacting concrete. J Chin Ceram Soc. 2007;35(5):671–8.

    Google Scholar 

  8. Han J, Fang H, Wang K. Design and control shrinkage behavior of high-strength self-consolidating concrete using shrinkage-reducing admixture and superabsorbent polymer. J Sustain Cem Based Mater. 2014;2014:1–9.

    Google Scholar 

  9. Esmaeilkhanian B, Khayat KH, Yahia A, Feys D. Effects of mix design parameters and rheological properties on dynamic stability of selfconsolidating concrete. Cem Concr Compos. 2014;54:21–8.

    Article  Google Scholar 

  10. Siddique R, Aggarwal P, Aggarwal Y. Mechanical and durability properties of self-compacting concrete containing fly ash and bottom ash. J Sustain Cem Based Mater. 2012;1(3):67–82.

    Google Scholar 

  11. Wang XH, Wang KJ, Taylor P, Morcous G. Assessing particle packing based selfconsolidating concrete mix design method. Constr Build Mater. 2014;70:439–52.

    Article  Google Scholar 

  12. Basu P, Thomas BS, Gupta RC, Agrawal V. Properties of sustainable self-compacting concrete incorporating discarded sandstone slurry. J Clean Prod. 2021;281:125313.

    Article  Google Scholar 

  13. Bouzuubaa N, Lachemi M. Self compacting concrete incorporating high volumes of class F fly ash preliminary results. Cem Concr Res. 2001;2001:413–20.

    Article  Google Scholar 

  14. Deilami S, Aslani F, Elchalakani M. An experimental study on the durability and strength of SCC incorporating FA, GGBS and MS. Proc Inst Civ Eng Struct Build. 2018;2018:1–3.

    Google Scholar 

  15. Ismael Jaf DK. Soft computing and machine learning-based models to predict the slump and compressive strength of self-compacted concrete modified with fly ash. Sustainability. 2023;15(15):11554.

    Article  Google Scholar 

  16. Neville AM, Brooks JJ. Concrete technology. England: Longman Scientific & Technical; 1987. p. 242–6.

    Google Scholar 

  17. Neville AM. Properties of concrete, vol. 4. London: Longman; 1995.

    Google Scholar 

  18. Douglas RP, Bui VK, Akkaya Y, Shah SP. Properties of self-consolidating concrete containing class F fly ash: with a verification of the minimum paste volume method. Spec Publ. 2006;233:45–64.

    Google Scholar 

  19. Pathak N, Siddique R. Properties of self-compacting-concrete containing fly ash subjected to elevated temperatures. Constr Build Mater. 2012;30:274–80.

    Article  Google Scholar 

  20. Ghezal A, Khayat KH. Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods. Mater J. 2002;99:264–72.

    Google Scholar 

  21. Dinakar P, Babu KG, Santhanam M. Mechanical properties of high-volume fly ash self-compacting concrete mixtures. Struct Concr. 2008;9:109–16.

    Article  Google Scholar 

  22. Bui VK, Akkaya Y, Shah SP. Rheological model for self-consolidating concrete. Mater J. 2002;99:549–59.

    Google Scholar 

  23. Sonebi M. Applications of statistical models in proportioning medium-strength self-consolidating concrete. Mater J. 2004;101:339–46.

    Google Scholar 

  24. Sonebi M. Medium strength self-compacting concrete containing fly ash: modelling using factorial experimental plans. Cem Concr Res. 2004;34:1199–208.

    Article  Google Scholar 

  25. Dinakar P. Design of self-compacting concrete with fly ash. Mag Concr Res. 2012;64:401–9.

    Article  Google Scholar 

  26. Bouzoubaâ N, Lachemi M. Self-compacting concrete incorporating high volumes of class F fly ash: preliminary results. Cem Concr Res. 2001;31:413–20.

    Article  Google Scholar 

  27. Patel R, Hossain KMA, Shehata M, Bouzoubaa N, Lachemi M. Development of statistical models for mixture design of high-volume fly ash self-consolidating concrete. Mater J. 2004;101:294–302.

    Google Scholar 

  28. Farooq F, Czarnecki S, Niewiadomski P, Aslam F, Alabduljabbar H, Ostrowski KA, Malazdrewicz S. A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials. 2021;14(17):4934.

    Article  Google Scholar 

  29. Hemalatha T, Ramaswamy A, Chandra Kishen JM. Micromechanical analysis of self compacting concrete. Mater Struct. 2015;48:3719–34.

    Article  Google Scholar 

  30. Sun ZJ, Duan WW, Tian ML, Fang YF. Experimental research on self-compacting concrete with different mixture ratio of fly ash. In: Advanced materials research, vol 236. Bäch, Switzerland: Trans Tech Publications Ltd; 2011. p. 490–5.

  31. Sukumar B, Nagamani K, Raghavan RS. Evaluation of strength at early ages of self-compacting concrete with high volume fly ash. Constr Build Mater. 2008;22:1394–401.

    Article  Google Scholar 

  32. Jalal M, Mansouri E. Effects of fly ash and cement content on rheological, mechanical, and transport properties of highperformance self-compacting concrete. Sci Eng Compos Mater. 2012;19:393–405.

    Article  Google Scholar 

  33. Boel V, Audenaert K, De Schutter G, Heirman G, Vandewalle L, Desmet B, Vantomme J. Transport properties of self compacting concrete with limestone filler or fly ash. Mater Struct. 2007;40:507–16.

    Article  Google Scholar 

  34. Ramanathan P, Baskar I, Muthupriya P, Venkatasubramani R. Performance of self-compacting concrete containing different mineral admixtures. KSCE J Civil Eng. 2013;17:465–72.

    Article  Google Scholar 

  35. Venkatakrishnaiah R, Sakthivel G. Bulk utilization of flyash in self compacting concrete. KSCE J Civil Eng. 2015;19:2116–20.

    Article  Google Scholar 

  36. Barbhuiya S. Effects of fly ash and dolomite powder on the properties of self-compacting concrete. Constr Build Mater. 2011;25:3301–5.

    Article  Google Scholar 

  37. Bingöl AF, Tohumcu İ. Effects of different curing regimes on the compressive strength properties of self compacting concrete incorporating fly ash and silica fume. Mater Des. 2013;51:12–8.

    Article  Google Scholar 

  38. Güneyisi E, Gesoğlu M, Özbay E. Strength and drying shrinkage properties of self-compacting concretes incorporating multi-system blended mineral admixtures. Constr Build Mater. 2010;24:1878–87.

    Article  Google Scholar 

  39. Nehdi M, Pardhan M, Koshowski S. Durability of self-consolidating concrete incorporating high-volume replacement composite cements. Cem Concr Res. 2004;34:2103–12.

    Article  Google Scholar 

  40. Krishnapal P, Yadav RK, Rajeev C. Strength characteristics of self compacting concrete containing fly ash. Res J Eng Sci ISSN. 2013;2278:9472.

    Google Scholar 

  41. Dhiyaneshwaran S, Ramanathan P, Baskar I, Venkatasubramani R. Study on durability characteristics of self-compacting concrete with fly ash. Jordan J Civ Eng. 2013;7:342–53.

    Google Scholar 

  42. Siddique R. Properties of self-compacting concrete containing class F fly ash. Mater Des. 2011;32:1501–7.

    Article  Google Scholar 

  43. Mahalingam B, Nagamani K. Effect of processed fly ash on fresh and hardened properties of self compacting concrete. Int J Earth Sci Eng. 2011;4:930–40.

    Google Scholar 

  44. Muthupriya P, Sri PN, Ramanathan MP, Venkatasubramani R. Strength and workability character of self compacting concrete with GGBFS, FA and SF. Int J Emerg Trends Eng Dev. 2012;2:424–34 (Sustainability 2023, 15, 11554 39 of 40).

    Google Scholar 

  45. Siddique R, Aggarwal P, Aggarwal Y. Influence of water/powder ratio on strength properties of self-compacting concrete containing coal fly ash and bottom ash. Constr Build Mater. 2012;29:73–81.

    Article  Google Scholar 

  46. Gesoğlu M, Güneyisi E, Özbay E. Properties of self-compacting concretes made with binary, ternary, and quaternary cementitious blends of fly ash, blast furnace slag, and silica fume. Constr Build Mater. 2009;23:1847–54.

    Article  Google Scholar 

  47. Nepomuceno MC, Pereira-de-Oliveira LA, Lopes SMR. Methodology for the mix design of self-compacting concrete using different mineral additions in binary blends of powders. Constr Build Mater. 2014;64:82–94.

    Article  Google Scholar 

  48. Gettu R, Izquierdo J, Gomes PCC, Josa A. Development of high-strength self-compacting concrete with fly ash: a four-step experimental methodology. In: Tam CT, Ho DW, Paramasivam P, Tan TH, editors. Proceedings of the 27th conference on our world in concrete & structures, Singapore, 29–30 August 2002. Singapore: Premier Pte. Ltd.; 2002. p. 217–24.

  49. Sahmaran M, Yaman İÖ, Tokyay M. Transport and mechanical properties of self consolidating concrete with high volume fly ash. Cem Concr Compos. 2009;31:99–106.

    Article  Google Scholar 

  50. Liu M. Self-compacting concrete with different levels of pulverized fuel ash. Constr Build Mater. 2010;24:1245–52.

    Article  Google Scholar 

  51. Jawahar JG, Sashidhar C, Reddy IR, Peter JA. Micro and macrolevel properties of fly ash blended self compacting concrete. Mater Des. 2013;1980–2015(46):696–705.

    Article  Google Scholar 

  52. Jaf DKI, Abdulrahman PI, Mohammed AS, Kurda R, Qaidi SM, Asteris PG. Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete. Constr Build Mater. 2023;400:132604.

    Article  Google Scholar 

  53. Abdalla A, Salih A. Microstructure and chemical characterizations with soft computing models to evaluate the influence of calcium oxide and silicon dioxide in the fly ash and cement kiln dust on the compressive strength of cement mortar. Resour Conser Recycling Adv. 2022;15:200090.

    Google Scholar 

  54. Ali R, Muayad M, Mohammed AS, Asteris PG. Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches. Struct Concr. 2023;24(3):4161–84.

    Article  Google Scholar 

  55. Emad W, Mohammed AS, Bras A, Asteris PG, Kurda R, Muhammed Z, Sihag P. Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures. Constr Build Mater. 2022;349:128737.

    Article  Google Scholar 

  56. Jaf DKI, Abdulrahman AS, Abdulrahman PI, Mohammed AS, Kurda R, Ahmed HU, Faraj RH. Effitioned soft computing models to evaluate the impact of silicon dioxide (SiO2) to calcium oxide (CaO) ratio in fly ash on the compressive strength of concrete. J Build Eng. 2023;74:106820.

    Article  Google Scholar 

  57. Piro NS, Salih A, Hamad SM, Kurda R. Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions. J Market Res. 2021;15:6506–27.

    Google Scholar 

  58. Quinlan JR. Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence, vol. 92; 1992. p. 343–8.

  59. Ahmad SA, Rafiq SK, Hilmi HDM, Ahmed HU. Mathematical modeling techniques to predict the compressive strength of pervious concrete modified with waste glass powders. Asian J Civ Eng. 2023;2023:1–13.

    Google Scholar 

  60. Barkhordari MS, Armaghani DJ, Mohammed AS, Ulrikh DV. Data-driven compressive strength prediction of fly ash concrete using ensemble learner algorithms. Buildings 2022;12(2):132.

    Article  Google Scholar 

  61. Abdalla AA, Salih Mohammed A. Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD). Arch Civ Mech Eng. 2022;22(3):105.

    Article  Google Scholar 

  62. Mohammed A, Rafiq S, Mahmood W, Noaman R, Hind AD, Ghafor K, Qadir W. Microstructure characterizations, thermal properties, yield stress, plastic viscosity and compression strength of cement paste modified with nanosilica. J Mater Res Technol. 2020;9(5):10941–56.

    Article  Google Scholar 

  63. Burhan L, Ghafor K, Mohammed A. Modeling the effect of silica fume on the compressive, tensile strengths and durability of NSC and HSC in various strength ranges. J Build Pathol Rehabil. 2019;4:1–19.

    Google Scholar 

  64. Abdalla AA, Mohammed AS, Rafiq S, Noaman R, Qadir WS, Ghafor K, Fairs R. Microstructure, chemical compositions, and soft computing models to evaluate the influence of silicon dioxide and calcium oxide on the compressive strength of cement mortar modified with cement kiln dust. Constr Build Mater. 2022;341:127668.

    Article  Google Scholar 

  65. Timur Cihan M. Prediction of concrete compressive strength and slump by machine learning methods. Adv Civ Eng. 2019;2019:1–11.

    Article  Google Scholar 

  66. Ahmed HU, Mohammed AA, Mohammed A (2022) Soft computing models to predict the compressive strength of GGBS/FA-geopolymer concrete. PloS one 17(5):e0265846.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Engineering, Department of Civil Engineering, University of Sulaimani, Sulaymaniyah, Kurdistan Region, 46001, Iraq

    Brwa Omer, Ahmed Salih Mohammed & Aso Abdalla

  2. Civil Engineering Department, College of Engineering, Salahaddin University-Erbil, Kurdistan, Kurdistan Region, Iraq

    Dilshad Kakasor Ismael Jaf & Sirwan Khuthur Malla

  3. Scientific Research Center of Soran University, Soran University, Erbil, Kurdistan Region, Iraq

    Payam Ismael Abdulrahman

  4. The American University of Iraq, Sulaimani (AUIS), Sulaimani, Kurdistan Region, Iraq

    Ahmed Salih Mohammed

  5. Department of Highway and Bridge Engineering, Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, 44001, Iraq

    Rawaz Kurda

  6. Department of Civil Engineering, College of Engineering, Nawroz University, Duhok, Kurdistan Region, 42001, Iraq

    Rawaz Kurda

  7. CERIS, Civil Engineering, Architecture and Georresources Department, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Rawaz Kurda

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  1. Brwa Omer
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Correspondence to Dilshad Kakasor Ismael Jaf or Payam Ismael Abdulrahman.

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Omer, B., Jaf, D.K.I., Malla, S.K. et al. Exploring the potential of soft computing for predicting compressive strength and slump flow diameter in fly ash-modified self-compacting concrete. Archiv.Civ.Mech.Eng 24, 95 (2024). https://doi.org/10.1007/s43452-024-00910-z

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