The roundabouts are widely used in China,some of which have central islands as scenic spots.The crosswalks connecting to the central islands,normally full of pedestrians,have negative impact on roundabout capability a...The roundabouts are widely used in China,some of which have central islands as scenic spots.The crosswalks connecting to the central islands,normally full of pedestrians,have negative impact on roundabout capability and pedestrian safety.Therefore,this study proposes a fuzzy cellular automata(FCA)model to explore the safety and efficiency impacts of pedestrian-vehicle conflicts at a two-lane roundabout.To reason the decision-making process of individual drivers before crosswalks,membership functions in the fuzzy inference system were calibrated with field data conducted in Changsha,China.Using specific indicators of efficiency and safety performance,it was shown that circulating vehicles can move smoothly in low traffic flow,but the roundabout system is prone to the traffic congestion if traffic flow reaches to a certain level.Also,the high yielding rate of drivers has a negative impact on the traffic efficiency but can improve pedestrian safety.Furthermore,a pedestrian restriction measure was deduced for the roundabout crosswalk from the FCA model and national guideline of setting traffic lights.展开更多
This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-E...This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.展开更多
A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the ten...A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.展开更多
基金Project(2020YFB1600400)supported by the National Key Research and Development Program of ChinaProject(2019JJ50837)supported by the Natural Science Foundation of Hunan Province,ChinaProject(71801227)supported by the National Natural Science Foundation of China。
文摘The roundabouts are widely used in China,some of which have central islands as scenic spots.The crosswalks connecting to the central islands,normally full of pedestrians,have negative impact on roundabout capability and pedestrian safety.Therefore,this study proposes a fuzzy cellular automata(FCA)model to explore the safety and efficiency impacts of pedestrian-vehicle conflicts at a two-lane roundabout.To reason the decision-making process of individual drivers before crosswalks,membership functions in the fuzzy inference system were calibrated with field data conducted in Changsha,China.Using specific indicators of efficiency and safety performance,it was shown that circulating vehicles can move smoothly in low traffic flow,but the roundabout system is prone to the traffic congestion if traffic flow reaches to a certain level.Also,the high yielding rate of drivers has a negative impact on the traffic efficiency but can improve pedestrian safety.Furthermore,a pedestrian restriction measure was deduced for the roundabout crosswalk from the FCA model and national guideline of setting traffic lights.
文摘This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.
基金Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram, Tamilnadu for funding this research as a university minor research project
文摘A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.
