In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression an...In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression analysis and fuzzy inference system, this paper tries to develop predictive models to estimate overbreak caused by blasting at the Alborz Tunnel. To develop the models, 202 datasets were utilized, out of which 182 were used for constructing the models. To validate and compare the obtained results,determination coefficient(R2) and root mean square error(RMSE) indexes were chosen. For the fuzzy model, R2 and RMSE are equal to 0.96 and 0.55 respectively, whereas for regression model, they are 0.41 and 1.75 respectively, proving that the fuzzy predictor performs, significantly, better than the statistical method. Using the developed fuzzy model, the percentage of overbreak was minimized in the Alborz Tunnel.展开更多
In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one toward...In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one towards the byper-plane method and random selection method, are proposed for investigation on minimization of classification samples for supercritical and subcritical patterns of supersonic inlet. The study has been carried out to analyze wind tunnel test data and to compare the classification accuracy based on those three methods with or without priori knowledge. Those three methods are different from each other by different selecting methods for samples. The results show that one of the optimization methods needs the minimization samples to get the highest classification accuracy without priori knowledge. Meanwhile, the number of minimization samples needed to get highest classification accuracy can be further reduced by introducing priori knowledge. Furthermore, it demonstrates that the best optimization method has been found by comparing all cases studied with or without introducing priori knowledge. This method can be applied to reduce the number of wind tunnel tests to obtain the inlet performance and to identify the supercritical/subcritical modes for supersonic inlet.展开更多
文摘In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression analysis and fuzzy inference system, this paper tries to develop predictive models to estimate overbreak caused by blasting at the Alborz Tunnel. To develop the models, 202 datasets were utilized, out of which 182 were used for constructing the models. To validate and compare the obtained results,determination coefficient(R2) and root mean square error(RMSE) indexes were chosen. For the fuzzy model, R2 and RMSE are equal to 0.96 and 0.55 respectively, whereas for regression model, they are 0.41 and 1.75 respectively, proving that the fuzzy predictor performs, significantly, better than the statistical method. Using the developed fuzzy model, the percentage of overbreak was minimized in the Alborz Tunnel.
基金Academy of Fundamental and Interdisciplinary Sciences,Harbin Institute of Technology
文摘In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one towards the byper-plane method and random selection method, are proposed for investigation on minimization of classification samples for supercritical and subcritical patterns of supersonic inlet. The study has been carried out to analyze wind tunnel test data and to compare the classification accuracy based on those three methods with or without priori knowledge. Those three methods are different from each other by different selecting methods for samples. The results show that one of the optimization methods needs the minimization samples to get the highest classification accuracy without priori knowledge. Meanwhile, the number of minimization samples needed to get highest classification accuracy can be further reduced by introducing priori knowledge. Furthermore, it demonstrates that the best optimization method has been found by comparing all cases studied with or without introducing priori knowledge. This method can be applied to reduce the number of wind tunnel tests to obtain the inlet performance and to identify the supercritical/subcritical modes for supersonic inlet.
