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To study the seismic behavior of high strength concrete fi lled double-tube(CFDT) columns,each consisting of an external square steel tube and an internal circular steel tube,quasi-static tests on eight CFDT column specimens were conducted.The test variables included the width-to-thickness ratio(β1) and the area ratio(β2) of the square steel tube,the wall thickness of the circular steel tube,and the axial force(or the axial force ratio) applied to the CFDT columns.The test results indicate that for CFDT columns with a square steel tube with β1 of 50.1 and 24.5,local buckling of the specimen was found at a drift ratio of 1/150 and 1/50,respectively.The lateral force-displacement hysteretic loops of all specimens were plump and stable.Reducing the width-to-thickness ratio of the square steel tube,increasing its area ratio,or increasing the wall thickness of the internal circular steel tube,led to an increased fl exural strength and deformation capacity of the specimens.Increasing the design value of the axial force ratio from 0.8 to 1.0 may increase the fl exural strength of the specimens,while it may also decrease the ultimate deformation capacity of the specimen with β1 of 50.1. 相似文献
734.
??С?????????????????????Σ??÷?????????????????????档????????????????????Jacobi????????????????????任???????????????????????????????????????????????????÷??????????????????ж???????????????????????????????????????????????????? 相似文献
735.
阐述了最小二乘配置基本原理,给出在吉洪诺夫条件下的最小二乘解和一种新的确定经验协方差的方法,并通过实例讨论信号与噪声权的选择对GPS高程拟合精度的影响。结果表明,随着信号与噪声权比的增大,内符合精度明显提高,检核精度也有一定的提高。 相似文献
736.
Potential of support vector regression for prediction of monthly streamflow using endogenous property 总被引:2,自引:0,他引:2
In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kernel‐based machine learning approaches have been found to be more popular due to their inherent advantages over traditional modelling techniques including artificial neural networks(ANNs ). In this paper, a kernel‐based learning approach is investigated for its suitability to capture the monthly variation of streamflow time series. Its performance is compared with that of the traditional approaches. Support vector machines (SVMs) are one such kernel‐based algorithm that has given promising results in hydrology and associated areas. In this paper, the application of SVMs to regression problems, known as support vector regression (SVR), is presented to predict the monthly streamflow of the Mahanadi River in the state of Orissa, India. The results obtained are compared against the results derived from the traditional Box–Jenkins approach. While the correlation coefficient between the observed and predicted streamflows was found to be 0·77 in case of SVR, the same for different auto‐regressive integrated moving average (ARIMA) models ranges between 0·67 and 0·69. The superiority of SVR as compared to traditional Box‐Jenkins approach is also explained through the feature space representation. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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738.
Zdeněk Mikulášek Marek Wolf Miloslav Zejda Petra Pecharová 《Astrophysics and Space Science》2006,304(1-4):363-365
We present two methods for the determination of moments of extrema and their errors appropriate for the analysis of light variations of variable objects. The method I is suitable for determination of times of extrema of non-periodical variables or objects, whose light curves vary. The method II is apt for O-C analyses of objects whose light curves are more or less repeating. Both methods are displayed on the analysis of BL Cam light variations and compared with the Kwee-van Woerden method. 相似文献
739.
自2001年底以来,在冬季室温过低时,地电阻率随着时间的推移其内在质量越来越低。2004年1月底到2月初,通过对仪器进行升温是否会对地电阻率产生影响,以及进行观测室不同温度下对地电阻率观测数据的对比分析。揭示出观测室温度是影响新沂台地电阻率内在质量的重要原因,对今后提高新沂台地电阻率观测数据的内在质量有一定意义。 相似文献
740.