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Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

Computer Modeling in Engineering & Sciences 2024年第3期138卷 2925-2954页

作者： Rabar H.Faraj Hemn Unis Ahmed Hardi Saadullah Fathullah alan saeed abdulrahm.. Farid Abed Civil Engineering Department University of HalabjaHalabjaIraq University of Sulaimani College of EngineeringCivil Engineering DepartmentSulaimaniIraq Civil Engineering Department Komar University of Science and TechnologySulaimaniIraq Department of Engineering Kurdistan Institution for Strategic Studies and Scientific ResearchesSulaimaniIraq Department of Civil Engineering American University of SharjahSharjah26666UAE

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is *** order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional *** concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched *** a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes ***,the effort required for laboratory trial batches to determine the TSC is *** achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(MLR),and M5P-tree model(M5P),are investigated and employed to predict the TSCof ECCmixtures containing fly *** from115 mixtures are gathered and analyzed to develop a new *** input variables include mixture proportions,fiber length and diameter,and the time required for curing the various *** model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(MAE),scatter index(SI),root mean squared error(RMSE),and objective function(OBJ)***,the ANN model outperforms the others in predicting the TSC of the ECC,with RMSE,MAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.

关键词： Engineered cementitious composites fly ash curing time tensile strain capacity modeling

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