Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

• 作者: ARamachandra Murthy Dookie Kim Pijush Samui Susom Dutta
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Self COMPACTING Concrete(SCC) Compressive Strength Extreme Learning Machine(ELM) ADAPTIVE Neuro Fuzzy INFERENCE System(ANFIS) Multi ADAPTIVE Regression Spline(MARS).
• 年，卷（期）: 2017,（2）
• 页码范围: 157-174
• 页数: 18页
• 分类号: TG1

摘要

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.