[Objective] The objective of this project was to evaluate and compare spa- tial estimation accuracy by ordinary kriging and regression kriging with MODIS data, predicting SOM contents using limited available data in S...[Objective] The objective of this project was to evaluate and compare spa- tial estimation accuracy by ordinary kriging and regression kriging with MODIS data, predicting SOM contents using limited available data in Shimen County, Hunan Province, China. [Method] Terrain parameters （derived from DEM） and Normalized differential vegetation index （NDVI）, Land surface temperature （LST） （derived from MODIS data） were used as auxiliary data to predict the SOM spatial distribution. The mean error （ME） and mean square error （RMSE） were adopted to validate the SOM prediction accuracy. The descriptive statistics and data transformation were conducted by using computer technology. [Result] Regression kriging with terrain and remotely sensed data was superior to ordinary kriging in the case of limited available samples; even the linear relationship between environmental variables and SOM content was moderate. The accuracy assessment showed that the regression kriging method combining with environmental factors obtained a lower mean predication error and root mean square prediction error. The relative improvement was 6.03% compared with ordinary kriging. [Conclusion] Remotely sensed data such as MODIS im- age have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction in the hilly regions.展开更多
A stochastic finite element computational methodology for probabilistic durability assessment of deteriorating reinforced concrete（RC） bridges by considering the time-and space-dependent variabilities is presented.F...A stochastic finite element computational methodology for probabilistic durability assessment of deteriorating reinforced concrete（RC） bridges by considering the time-and space-dependent variabilities is presented.First,finite element analysis with a smeared cracking approach is implemented.The time-dependent bond-slip relationship between steel and concrete,and the stress-strain relationship of corroded steel bars are considered.Secondly,a stochastic finite element-based computational framework for reliability assessment of deteriorating RC bridges is proposed.The spatial and temporal variability of several parameters affecting the reliability of RC bridges is considered.Based on the data reported by several researchers and from field investigations,the Monte Carlo simulation is used to account for the uncertainties in various parameters,including local and general corrosion in rebars,concrete cover depth,surface chloride concentration,chloride diffusion coefficient,and corrosion rate.Finally,the proposed probabilistic durability assessment approach and framework are applied to evaluate the time-dependent reliability of a girder of a RC bridge located on the Tianjin Binhai New Area in China.展开更多
基金Supported by National Natural Science Foundation of China(41071204)Hunan Provincial Innovation Foundation for Postgraduate(CX2011B310)~~
文摘[Objective] The objective of this project was to evaluate and compare spa- tial estimation accuracy by ordinary kriging and regression kriging with MODIS data, predicting SOM contents using limited available data in Shimen County, Hunan Province, China. [Method] Terrain parameters （derived from DEM） and Normalized differential vegetation index （NDVI）, Land surface temperature （LST） （derived from MODIS data） were used as auxiliary data to predict the SOM spatial distribution. The mean error （ME） and mean square error （RMSE） were adopted to validate the SOM prediction accuracy. The descriptive statistics and data transformation were conducted by using computer technology. [Result] Regression kriging with terrain and remotely sensed data was superior to ordinary kriging in the case of limited available samples; even the linear relationship between environmental variables and SOM content was moderate. The accuracy assessment showed that the regression kriging method combining with environmental factors obtained a lower mean predication error and root mean square prediction error. The relative improvement was 6.03% compared with ordinary kriging. [Conclusion] Remotely sensed data such as MODIS im- age have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction in the hilly regions.
基金The National Natural Science Foundation of China (No.50708065)the National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z113)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070056125)
文摘A stochastic finite element computational methodology for probabilistic durability assessment of deteriorating reinforced concrete（RC） bridges by considering the time-and space-dependent variabilities is presented.First,finite element analysis with a smeared cracking approach is implemented.The time-dependent bond-slip relationship between steel and concrete,and the stress-strain relationship of corroded steel bars are considered.Secondly,a stochastic finite element-based computational framework for reliability assessment of deteriorating RC bridges is proposed.The spatial and temporal variability of several parameters affecting the reliability of RC bridges is considered.Based on the data reported by several researchers and from field investigations,the Monte Carlo simulation is used to account for the uncertainties in various parameters,including local and general corrosion in rebars,concrete cover depth,surface chloride concentration,chloride diffusion coefficient,and corrosion rate.Finally,the proposed probabilistic durability assessment approach and framework are applied to evaluate the time-dependent reliability of a girder of a RC bridge located on the Tianjin Binhai New Area in China.
