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 artificia...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.展开更多
The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final p...The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement (R2 is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.展开更多
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.
文摘The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement (R2 is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.
