Compressive strength modeling of the eco-friendly self-compacting rubberized concrete with fly ash and ground granulated blast furnace slag additives

被引:1
作者
Sherwani, Aryan Far H. [1 ]
Abdulrahman, Payam Ismael [2 ,3 ]
Younis, Khaleel H. [4 ]
Mohammed, Ahmed Salih [5 ,6 ]
Zrar, Yarivan J. [7 ]
机构
[1] Soran Univ, Sci Res Ctr, Soran, Kurdistan, Iraq
[2] Erbil Polytech Univ, Sci Res Ctr, Erbil, Kurdistan, Iraq
[3] Tishk Int Univ, Fac Engn, Civil Engn Dept, Erbil, Iraq
[4] Erbil Polytech Univ, Erbil Technol Coll, Rd Construct Dept, Erbil, Iraq
[5] Univ Sulaimani, Coll Engn, Civil Engn Dept, Kurdistan, Iraq
[6] Sulaimani AUIS, Amer Univ Iraq, Sulaimani, Kurdistan, Iraq
[7] Soran Univ, Fac Engn, Dept Civil & Environm Engn, Soran, Kurdistan, Iraq
关键词
Crumb rubber; Additives; Modeling; Self-compacted concrete; Sensitivity analysis; WASTE TIRE RUBBER; CONSOLIDATING CONCRETE; MECHANICAL-PROPERTIES; DURABILITY PROPERTIES; HARDENED PROPERTIES; BEHAVIOR; FRESH; SILICA; CRUMB; PERFORMANCE;
D O I
10.1007/s41062-024-01811-z
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This study addresses the urgent need for sustainable alternatives in the construction industry, which significantly contributes to global carbon emissions, particularly from cement production. The research focuses on assessing the impact of crumb rubber (CR) on the compressive strength (CS) of self-compacting concrete when combined with eco-friendly materials like fly ash and ground granulated blast furnace slag. Utilizing a dataset of 439 data points, the study employs four predictive models-Nonlinear Regression, Interaction, Pure Quadratic, and Artificial Neural Networks (ANN)-to evaluate CS under various mix ratios and curing periods. The findings highlight the ANN model as the most accurate for predicting CS, with CR content being a critical factor. Additionally, the study develops a power function model for predicting splitting-tensile strength, offering a valuable tool for future research and industry applications.
引用
收藏
页数:28
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