Buckling-restrained braces(BRBs)have shown their capability to provide building structures with stiffness,strength,and ductility.Estimating the seismic drifts of buckling-restrained braced frames(BRBFs)is an important...Buckling-restrained braces(BRBs)have shown their capability to provide building structures with stiffness,strength,and ductility.Estimating the seismic drifts of buckling-restrained braced frames(BRBFs)is an important design step to control structural and non-structural damage.In current practice of seismic design,the estimation of seismic drifts of BRBFs is performed by using empirical calculations that are independent upon either the type of the structural system or the design level of seismicity.In these empirical calculations,the seismic drifts are estimated by amplifying the reduced elastic drifts obtained under design lateral loading with a displacement amplification factor(DAF).The value of DAF is considered equal to the product of the response modification factor R and the inelastic displacement ratioρ.The goal of the current research is to assess the value ofρfor low-to mid-rise BRBFs designed under low and high levels of seismicity.This goal has been achieved by conducting a series of elastic and inelastic time-history analyses pertaining to an ensemble of earthquake records on 3-,6-and 9-story BRBFs.The results indicate that theρ-ratio increases with an increase in design seismic intensity and an increase in experienced inelasticity.The range ofρfor low seismicity designs ranges from 0.63 to 0.9,while for high seismicity designs this range stretches from 0.83 to 1.29.It has been found that the consideration of a generalρ-ratio of 1.0 is a reasonable estimation for the design of the BRBFs considered in this study.展开更多
Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
为研究不同侧风和静电电压对静电喷雾雾滴飘移的影响规律,设计不同侧风(恒速风1、2、4 m/s及0~4 m/s变化的模拟自然风)及静电电压(0,2,4,6,8 k V),进行喷杆式静电喷雾机的雾滴飘移试验,测定不同静电电压下的雾滴粒径与荷质比,并对比分...为研究不同侧风和静电电压对静电喷雾雾滴飘移的影响规律,设计不同侧风(恒速风1、2、4 m/s及0~4 m/s变化的模拟自然风)及静电电压(0,2,4,6,8 k V),进行喷杆式静电喷雾机的雾滴飘移试验,测定不同静电电压下的雾滴粒径与荷质比,并对比分析雾滴飘移质量中心距和飘失率。结果表明:随着静电电压的增大,雾滴粒径减小,雾滴荷质比增大,0~8 k V电压下电极干燥和电极打湿对雾滴荷质比没有显著影响。在侧风风速为1 m/s时,0~8 k V静电喷雾的雾滴飘移中心距小于0.55 m,雾滴飘失率低于15%。在侧风风速2 m/s时,非静电喷雾的雾滴飘失率为11.9%,6~8 k V静电喷雾的雾滴飘失率超过20%,其中静电电压8 k V的雾滴飘失率(23.9%)比非静电喷雾增加100.8%。在侧风风速4 m/s时,4~8 k V静电喷雾的雾滴飘移中心距在0.9 m以上,雾滴飘失率在30%以上,其中静电电压8 k V下的雾滴飘移中心距为967.2 mm比非静电喷雾下增加了13.7%,雾滴飘失率为35.4%比非静电喷雾下增加了59.5%。相同静电电压下,2 m/s的恒速风和0~4 m/s变化的模拟自然风之间对雾滴飘失率无显著差异。该研究为优化喷雾技术参数和提高雾滴抗飘移的能力提供参考。展开更多
文摘Buckling-restrained braces(BRBs)have shown their capability to provide building structures with stiffness,strength,and ductility.Estimating the seismic drifts of buckling-restrained braced frames(BRBFs)is an important design step to control structural and non-structural damage.In current practice of seismic design,the estimation of seismic drifts of BRBFs is performed by using empirical calculations that are independent upon either the type of the structural system or the design level of seismicity.In these empirical calculations,the seismic drifts are estimated by amplifying the reduced elastic drifts obtained under design lateral loading with a displacement amplification factor(DAF).The value of DAF is considered equal to the product of the response modification factor R and the inelastic displacement ratioρ.The goal of the current research is to assess the value ofρfor low-to mid-rise BRBFs designed under low and high levels of seismicity.This goal has been achieved by conducting a series of elastic and inelastic time-history analyses pertaining to an ensemble of earthquake records on 3-,6-and 9-story BRBFs.The results indicate that theρ-ratio increases with an increase in design seismic intensity and an increase in experienced inelasticity.The range ofρfor low seismicity designs ranges from 0.63 to 0.9,while for high seismicity designs this range stretches from 0.83 to 1.29.It has been found that the consideration of a generalρ-ratio of 1.0 is a reasonable estimation for the design of the BRBFs considered in this study.
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
文摘为研究不同侧风和静电电压对静电喷雾雾滴飘移的影响规律,设计不同侧风(恒速风1、2、4 m/s及0~4 m/s变化的模拟自然风)及静电电压(0,2,4,6,8 k V),进行喷杆式静电喷雾机的雾滴飘移试验,测定不同静电电压下的雾滴粒径与荷质比,并对比分析雾滴飘移质量中心距和飘失率。结果表明:随着静电电压的增大,雾滴粒径减小,雾滴荷质比增大,0~8 k V电压下电极干燥和电极打湿对雾滴荷质比没有显著影响。在侧风风速为1 m/s时,0~8 k V静电喷雾的雾滴飘移中心距小于0.55 m,雾滴飘失率低于15%。在侧风风速2 m/s时,非静电喷雾的雾滴飘失率为11.9%,6~8 k V静电喷雾的雾滴飘失率超过20%,其中静电电压8 k V的雾滴飘失率(23.9%)比非静电喷雾增加100.8%。在侧风风速4 m/s时,4~8 k V静电喷雾的雾滴飘移中心距在0.9 m以上,雾滴飘失率在30%以上,其中静电电压8 k V下的雾滴飘移中心距为967.2 mm比非静电喷雾下增加了13.7%,雾滴飘失率为35.4%比非静电喷雾下增加了59.5%。相同静电电压下,2 m/s的恒速风和0~4 m/s变化的模拟自然风之间对雾滴飘失率无显著差异。该研究为优化喷雾技术参数和提高雾滴抗飘移的能力提供参考。
