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1篇 您的检索式：作者名="Pourya ALIDOUST"

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		题名	作者	年代	出处	被引量
	1	Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine显示文摘Plastic concrete is an engineering material,which is commonly used for construction of cut-offwalls to prevent water seepage under the dam.This paper aims to explore two machine learning algorithms including artificial neural network (ANN)and support vector machine (SVM)to predict the compressive strength of bentonite/sepiolite plastic concretes.For this purpose,two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data)were prepared by conducting an experimental study.The results confirm the ability of ANN and SVM models in prediction processes.Also,Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength,respectively.In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount)and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE)of model, respectively.Finally,the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.	Ali Reza GHANIZADEH Hakime ABBASLOU Amir Tavana AMLASHI Pourya ALIDOUST	2019	Frontiers of Structural and Civil Engineering2019，13，1：	3

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