Reliability Sensitivity-based Correlation Coefficient Calculation in Structural Reliability Analysis

The correlation coefficients of random variables of mechanical structures are generally chosen with experience or even ignored,which cannot actually reflect the effects of parameter uncertainties on reliability.To discuss the selection problem of the correlation coefficients from the reliability-based sensitivity point of view,the theory principle of the problem is established based on the results of the reliability sensitivity,and the criterion of correlation among random variables is shown.The values of the correlation coefficients are obtained according to the proposed principle and the reliability sensitivity problem is discussed.Numerical studies have shown the following results：（1） If the sensitivity value of correlation coefficient ρ is less than（at what magnitude 0.000 01）,then the correlation could be ignored,which could simplify the procedure without introducing additional error.（2） However,as the difference between ρs,that is the most sensitive to the reliability,and ρR,that is with the smallest reliability,is less than 0.001,ρs is suggested to model the dependency of random variables.This could ensure the robust quality of system without the loss of safety requirement.（3） In the case of ｜Eabs｜ρ0.001 and also ｜Erel｜ρ0.001,ρR should be employed to quantify the correlation among random variables in order to ensure the accuracy of reliability analysis.Application of the proposed approach could provide a practical routine for mechanical design and manufactory to study the reliability and reliability-based sensitivity of basic design variables in mechanical reliability analysis and design.

Spatial-temporal Variation of Air Temperature for the Recent 40 Years in Lhasa

It was all known that Lhasa went through a high temperature of 30.8℃in late June 2019,which hit record highs.To better understand the reasons,based on observations recorded at automatic weather stations in Lhasa,the characteristics of temperature variation at multiple time scales were studied using the linear trend method,Mann-Kendall mutation test,Morlet wavelet analysis,R/S analysis and so on.The results showed that:(a)the annual mean temperature(AMT)is rising at a rate of 0.5℃/10 a,and the average temperature for different seasons also increased significantly,especially in winter.(b)Although there was an intersection in 1995,it was found that AMT,did not pass the reliability test of significance levelα=0.05,this means there are no abrupt changes for AMT,the values are 7.97 and 9.15℃respectively before and after the intersection point.(c)AMT has a periodic oscillation for 18-25 and 25-32 a based on a mass of data and the wavelet variance diagrams in Lhasa.AMT has a main cycle of 28 a,cyclic patterns of temperature changes in spring,summer and autumn is similar to AMT,but it is relatively complex in winter.(d)The Hurst index of AMT and different seasons demonstrates that the temperature is likely to continue to rise in the future in Lhasa.

Structural characterization of steam-heat treated Tectona grandis wood analyzed by FT-IR and 2D-IR correlation spectroscopy

The properties of wood can be improved through steam-heat treatment.There are many studies about mechanical properties of steam-heat treated wood,but very few studies are on the aspects of chemical modifications.In this study,FT-IR spectra combined with SD-IR spectra,correlation coefficients and 2DIR spectra are employed to analyze the chemical modifications of teak[Tectona grandis L.F.） wood during steam-heat treatment under treatment temperatures from 120 ℃ to 220 ℃ at intervals of 20 ℃.Acetic acid,which is produced during steam-heat treatment,acts as a catalyst of condensation and degradation reactions of wood components.The changes of wood components are more and more intense with increasing the treatment temperature.The sensitivity of wood samples to thermal perturbation rises initially with increasing treatment temperature before falling back.The steam-heat treated wood under180℃ is the most sensitive.