دانشگاه مازندران

 آمار

تعداد نشریات31
تعداد شماره‌ها499
تعداد مقالات4,851
تعداد مشاهده مقاله7,496,406
تعداد دریافت فایل اصل مقاله5,595,947
Farrokh Ghatte, Hamid, Nazari, Ali. (1404). Prediction of Rice Husk Ash-Based SCC Compressive Strength: Data-Driven Framework. پژوهشنامه مدیریت اجرایی, 1(3), 17-28. doi: 10.22080/ceas.2025.29625.1025
Hamid Farrokh Ghatte; Ali Nazari. "Prediction of Rice Husk Ash-Based SCC Compressive Strength: Data-Driven Framework". پژوهشنامه مدیریت اجرایی, 1, 3, 1404, 17-28. doi: 10.22080/ceas.2025.29625.1025
Farrokh Ghatte, Hamid, Nazari, Ali. (1404). 'Prediction of Rice Husk Ash-Based SCC Compressive Strength: Data-Driven Framework', پژوهشنامه مدیریت اجرایی, 1(3), pp. 17-28. doi: 10.22080/ceas.2025.29625.1025
Farrokh Ghatte, Hamid, Nazari, Ali. Prediction of Rice Husk Ash-Based SCC Compressive Strength: Data-Driven Framework. پژوهشنامه مدیریت اجرایی, 1404; 1(3): 17-28. doi: 10.22080/ceas.2025.29625.1025

Prediction of Rice Husk Ash-Based SCC Compressive Strength: Data-Driven Framework

Civil Engineering and Applied Solutions
دوره 1، شماره 3، آبان 2025، صفحه 17-28    اصل مقاله (1.37 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2025.29625.1025
نویسندگان
Hamid Farrokh Ghatte* 1؛ Ali Nazari2
1Department of Civil Engineering, University of Antalya Bilim, Antalya, Turkey
2Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
تاریخ دریافت: 16 تیر 1404،  تاریخ بازنگری: 18 تیر 1404،  تاریخ پذیرش: 22 تیر 1404 
چکیده
The construction and upkeep of concrete structures have posed significant technical and financial challenges over the past decade. In response, self-compacting concrete (SCC) has gained attention due to its superior mechanical performance and reduced environmental footprint. This study investigates the application of gene expression programming (GEP) in developing a predictive model for estimating the compressive strength (CS) of self-compacting concrete incorporating rice husk ash (RHA). To assess the model’s reliability, its predictions were benchmarked against those from two well-established machine learning methods: multiple linear regression (MLR) and artificial neural networks (ANN). A total of 651 experimental records related to RHA-based SCC were gathered from trustworthy references. The model’s performance was then quantified using key statistical measures, including the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE). The GEP model outperformed the ANN and MLR approaches, delivering greater accuracy and lower error levels. Additionally, the study introduced a gene expression-based formula derived from the GEP model for estimating compressive strength at different curing ages, achieving a correlation coefficient of 0.49 and error values ranging from 5 to 9 MPa, which highlights its strong predictive ability. This equation provides a practical tool for preliminary mix design and the quick assessment of SCC mixtures. Sensitivity analysis revealed that binder content was the most significant parameter influencing CS prediction.
کلیدواژه‌ها
Prediction؛ Self-compacting concrete (SCC)؛ Gene expression programming (GEP)؛ Machine learning algorithms
مراجع
  1. Sandhu, R. K., Siddique, R. Influence of rice husk ash (RHA) on the properties of self-compacting concrete: A review. Construction and Building Materials, 2017; 153: 751-764. doi:10.1016/j.conbuildmat.2017.07.165.
  2. Ameri, F., Shoaei, P., Bahrami, N., Vaezi, M., Ozbakkaloglu, T. Optimum rice husk ash content and bacterial concentration in self-compacting concrete. Construction and Building Materials, 2019; 222: 796-813. doi:10.1016/j.conbuildmat.2019.06.190.
  3. Sutas, J., Mana, A., Pitak, L. Effect of Rice Husk and Rice Husk Ash to Properties of Bricks. Procedia Engineering, 2012; 32: 1061-1067. doi:10.1016/j.proeng.2012.02.055.
  4. Qu, J., Li, B., Wei, T., Li, C., Liu, B. Effects of rice-husk ash on soil consistency and compactibility. CATENA, 2014; 122: 54-60. doi:10.1016/j.catena.2014.05.016.
  5. Chen, L., Pai, T. Y. An ERAFNN Prediction of Concrete Compressive Strength Based on Physical Properties of Electric Arc Furnace Oxidizing Slag. Applied Mechanics and Materials, 2014; 627: 111-114. doi:10.4028/scientific.net/AMM.627.111.
  6. Sonebi, M., Grünewald, S., Cevik, A., Walraven, J. Modelling fresh properties of self-compacting concrete using neural network technique. Computers and Concrete, 2016; 18: 903-921. doi:10.12989/cac.2016.18.4.903.
  7. Kalman Šipoš, T., Miličević, I., Siddique, R. Model for mix design of brick aggregate concrete based on neural network modelling. Construction and Building Materials, 2017; 148: 757-769. doi:10.1016/j.conbuildmat.2017.05.111.
  8. Wang, B., Man, T., Jin, H. Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems. Construction and Building Materials, 2015; 84: 184-191. doi:10.1016/j.conbuildmat.2015.03.059.
  9. Rezaei, F., Memarzadeh, A., Davoodi, M.-R., Dashab, M.-A., Nematzadeh, M. Mechanical features and durability of concrete incorporating recycled coarse aggregate and nano-silica: Experimental study, prediction, and optimization. Journal of Building Engineering, 2023; 73: 106715. doi:10.1016/j.jobe.2023.106715.
  10. Gholampour, A., Gandomi, A. H., Ozbakkaloglu, T. New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Construction and Building Materials, 2017; 130: 122-145. doi:10.1016/j.conbuildmat.2016.10.114.
  11. Nematzadeh, M., Nazari, A., Tayebi, M. Post-fire impact behavior and durability of steel fiber-reinforced concrete containing blended cement–zeolite and recycled nylon granules as partial aggregate replacement. Archives of Civil and Mechanical Engineering, 2021; 22: 5. doi:10.1007/s43452-021-00324-1.
  12. Nematzadeh, M., Tayebi, M., Samadvand, H. Prediction of ultrasonic pulse velocity in steel fiber-reinforced concrete containing nylon granule and natural zeolite after exposure to elevated temperatures. Construction and Building Materials, 2021; 273: 121958. doi:10.1016/j.conbuildmat.2020.121958.
  13. Sobhani, J., Najimi, M., Pourkhorshidi, A. R., Parhizkar, T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construction and Building Materials, 2010; 24: 709-718. doi:10.1016/j.conbuildmat.2009.10.037.
  14. Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 1958; 65: 386. doi:10.1037/h0042519.
  15. Augustino, D. S. Parametric Modelling of Fibre-Concrete Interface and Prediction of Compressive Strength of Concrete With Waste Tyre Steel Fibres Using Artificial Neural Network Model. Advances in Civil Engineering, 2025; 2025: 7678622. doi:10.1155/adce/7678622.
  16. Marimuthu, V., Ramasamy, A. Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN. Environmental Science and Pollution Research, 2024; 31: 28474-28493. doi:10.1007/s11356-024-33099-y.
  17. Ferreira, C. Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027, 2001; doi:10.48550/arXiv.cs/0102027.
  18. Shahmansouri, A. A., Akbarzadeh Bengar, H., Jahani, E. Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm. Construction and Building Materials, 2019; 229: 116883. doi:10.1016/j.conbuildmat.2019.116883.
  19. Sakr, K. Effects of silica fume and rice husk ash on the properties of heavy weight concrete. Journal of materials in civil engineering, 2006; 18: 367-376. doi:10.1061/(ASCE)0899-1561(2006)18:3(367).
  20. Ganesan, K., Rajagopal, K., Thangavel, K. Rice husk ash blended cement: Assessment of optimal level of replacement for strength and permeability properties of concrete. Construction and Building Materials, 2008; 22: 1675-1683. doi:10.1016/j.conbuildmat.2007.06.011.
  21. Bui, D. D., Hu, J., Stroeven, P. Particle size effect on the strength of rice husk ash blended gap-graded Portland cement concrete. Cement and Concrete Composites, 2005; 27: 357-366. doi:10.1016/j.cemconcomp.2004.05.002.
  22. Rodríguez de Sensale, G. Strength development of concrete with rice-husk ash. Cement and Concrete Composites, 2006; 28: 158-160. doi:10.1016/j.cemconcomp.2005.09.005.
  23. Shahmansouri, A. A., Yazdani, M., Hosseini, M., Akbarzadeh Bengar, H., Farrokh Ghatte, H. The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network. Construction and Building Materials, 2022; 317: 125876. doi:10.1016/j.conbuildmat.2021.125876.
  24. Shahmansouri, A. A., Yazdani, M., Ghanbari, S., Akbarzadeh Bengar, H., Jafari, A., Farrokh Ghatte, H. Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite. Journal of Cleaner Production, 2021; 279: 123697. doi:10.1016/j.jclepro.2020.123697.
  25. Ghatte, H. F. Evaluation of reinforcing bars ratio effects on SCC beam-column joint performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 2019; 24: 141-152. doi:10.17482/uumfd.587505.
  26. Farrokh Ghatte, H. External steel ties and CFRP jacketing effects on seismic performance and failure mechanisms of substandard rectangular RC columns. Composite Structures, 2020; 248: 112542. doi:10.1016/j.compstruct.2020.112542.
  27. Farrokh Ghatte, H. Failure mechanisms and cracking performance of T-shaped SCC beam-column connections at top floor: Test results and FE modeling. Structures, 2020; 28: 1009-1018. doi:10.1016/j.istruc.2020.09.051.
  28. Belalia Douma, O., Boukhatem, B., Ghrici, M., Tagnit-Hamou, A. Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, 2017; 28: 707-718. doi:10.1007/s00521-016-2368-7.
  29. Kaveh, A., Bakhshpoori, T., Hamze-Ziabari, S. M. M5'and Mars based prediction models for properties of self-compacting concrete containing fly ash. Periodica Polytechnica Civil Engineering, 2018; 62: 281-294. doi:10.3311/PPci.10799.
  30. Inqiad, W. B., Siddique, M. S., Ali, M., Najeh, T. Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP. Scientific Reports, 2024; 14: 17293. doi:10.1038/s41598-024-65905-5.
  31. Siddique, R., Aggarwal, P., Aggarwal, Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011; 42: 780-786. doi:10.1016/j.advengsoft.2011.05.016.
  32. Zhang, W. G., Goh, A. T. C. Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics, 2013; 48: 82-95. doi:10.1016/j.compgeo.2012.09.016.
  33. Bluman, A. G. Elementary statistics: a step by step approach. 11th ed. New York (NY): McGram-Hill; 2019.
  34. Najafzadeh, M., Rezaie Balf, M., Rashedi, E. Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. Journal of Hydroinformatics, 2016; 18: 867-884. doi:10.2166/hydro.2016.212.
آمار
تعداد مشاهده مقاله: 92
تعداد دریافت فایل اصل مقاله: 72


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب