Seismic Reliability Assessment of Inelastic SDOF Systems Subjected to Near-Fault Ground Motions Considering Pulse Occurrence

The ground motions in the orientation corresponding to the strongest pulse energy impose more serious demand on structures than that of ordinary ground motions.Moreover,not all near-fault ground motion records present distinct pulses in the velocity time histories.In this paper,the parameterized stochastic model of near-fault ground motion with the strongest energy and pulse occurrence probability is suggested,and the Monte Carlo simulation(MSC)and subset simulation are utilized to calculate the first excursion probability of inelastic single-degree-of-freedom(SDOF)systems subjected to these types of near-fault ground motion models,respectively.Firstly,the influences of variation of stochastic pulse model parameters on structural dynamic reliability with different fundamental periods are explored.It is demonstrated that the variation of pulse period,peak ground velocity and pulse waveform number have significant effects on structural reliability and should not be ignored in reliability analysis.Then,subset simulation is verified to be unbiased and more efficient for computing small reliable probabilities of structures compared to MCS.Finally,the reliable probabilities of the SDOF systems with different fundamental periods subjected to impulsive,non-pulse ground motions and the ground motions with pulse occurrence probability are performed,separately.It is indicated that the ground motion model with the pulse occurrence probability can give a rational estimate on structural reliability.The impulsive and ordinary ground motion models may overestimate and underestimate the reliability of structures with fundamental period much less than the mean pulse period of earthquake ground motions.

Desulphurization Characteristic of Industry Alkaline Wastes during Coal Combustion

The desulphurization characteristics of four sorts of industry alkaline wastes and one sort of limestone were studied by means of flue gas analyzer and the high temperature tube reactor. Pore structure and desulphurization product char-acteristic were investigated respectively by mercury porosimeter and XRD diffraction technology. The reasons why wastes and limestone hold the different desulphurization capability were deeply discussed. The result shows that white clay and carbide slag could capture the release of sulfur at 800-1100℃. Salt slurry and red mud could capture the re-lease of sulfur at first stage at 800-900℃. But when the experimental temperature rises to 1000℃, the sulfur capture abilities of them depress. Pore structures of waste are higher than that of limestone. This makes the sulfation reaction goes further. To sum up, wastes have better sulfur capture ability.

Detection of cotton waterlogging stress based on hyperspectral images and convolutional neural network

Waterlogging in the early stage of cotton will reduce the number of bolls and do harm to yield.Early detection of waterlogging will help farmers to adjust cotton management and save the loss.To evaluate the application of deep learning for the detection of early waterlogging,this study applied a convolutional neural network(CNN)to classify different durations of waterlogging stress(0,2,4,6,8,10 d)based on hyperspectral images(HSIs)of cotton leaves.An experiment was designed to simulate the situation of cotton under waterlogging stress and collect HSIs of visible and near-infrared(VNIR 450-950 nm)spectra with 126 bands 66 d after cotton sowing(66 DAS).It was found the spectral curve reflectance of waterlogging cotton was higher than that of non-waterlogging cotton.Especially near 550 nm and 750 nm,and the spectral curve increased with durations of waterlogging stress and there were‘blue shift’phenomena for the position of the red edge of the spectra.The first principal components of HSIs after band randomly discarding and principal component analysis(PCA)were used to build a dataset.GoogLeNet Inception-v3(GLNI-v3)and VGG-16 models were selected to detect cotton waterlogging stress with the dataset.The results showed that the average time for a round training for GLNI-v3 was 13.337 s,with a classification accuracy of 96.95%and a loss value of 0.09431.The average time for a round training for VGG-16 was 21.470 s,with a classification accuracy of 97.00%and a loss value of 0.17912.Though these two models had similar classification accuracy and loss value,GLNI-v3 achieved a high accuracy with fewer training iterations.The durations of waterlogging stress of cotton in a short-term can be detected by HSIs of cotton leaves and CNN models are suitable for the classification of HSIs,and this method can provide support for cotton yield estimation and loss assessment after waterlogging.