Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with li...Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.展开更多
Objective： To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Methods： Based on the Chinese medicine theory of Yunqi, the des...Objective： To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Methods： Based on the Chinese medicine theory of Yunqi, the descriptive statistics, single-factor correlation analysis and back-propagation artificial neural net-work were conducted using data on five basic meteorological factors and data on incidence of bacillary dysentery in Beijing, China, for the period 1970-2004. Results： The incidence of bacillary dysentery showed significant positive correlation relationship with the precipitation, relative humidity, vapor pressure, and temperature, respectively. The incidence of bacillary dysentery showed a negatively correlated relationship with the wind speed and the change trend of average wind speed. The results of medical-meteorological forecast model showed a relatively high accuracy rate. Conclusions： There is a close relationship between the meteorological factors and the incidence of bacillary dysentery, but the contributions of which to the onset of bacillary dysentery are different to each other.展开更多
基金Acknowledgements This work was sponsored by the National Natural Science Foundation of China （Grant No. 61272264）.
文摘Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.
基金Supported by the National Natural Science Foundation of China(No.81072896)Beijing University of Chinese Medicine(No. 2009JYZZ-JS001)
文摘Objective： To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Methods： Based on the Chinese medicine theory of Yunqi, the descriptive statistics, single-factor correlation analysis and back-propagation artificial neural net-work were conducted using data on five basic meteorological factors and data on incidence of bacillary dysentery in Beijing, China, for the period 1970-2004. Results： The incidence of bacillary dysentery showed significant positive correlation relationship with the precipitation, relative humidity, vapor pressure, and temperature, respectively. The incidence of bacillary dysentery showed a negatively correlated relationship with the wind speed and the change trend of average wind speed. The results of medical-meteorological forecast model showed a relatively high accuracy rate. Conclusions： There is a close relationship between the meteorological factors and the incidence of bacillary dysentery, but the contributions of which to the onset of bacillary dysentery are different to each other.
