Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction.However,regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics,especially at tunnel portals with complex geological conditions.This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis,with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals.The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages.To improve the accuracy of the models,a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation.Finally,a regional-scale model and an engineering-scale model are built,providing a clear insight into geological phenomena and supporting numerical calculation.In addition,the proposed framework is applied to a case study,the Long-tou mountain tunnel project in Guangzhou,China,where the dominant rock type is granite.The results show that the data integration and modeling methods effectively improve model structure refinement.The improved model’s calculation deviation is reduced by about 10%to 20%in the mechanical analysis.This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling.

Progressive prediction method for failure data with small sample size

The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and confidence limits formula of the progressive auto regressive(PAR) method were discussed in great detail.PAR model not only inherits the simple linear features of auto regressive(AR) model,but also has applicability for nonlinear systems.An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors.Forecasting results of PAR model were compared with auto regressive moving average(ARMA) model,and it can be seen that the PAR method can be considered good and shows a promise for future applications.

Multiple sclerosis：integration of modeling with biology,clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome

Multiple Sclerosis（MS） is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.

Progress on the Key Technology Development in Application of Ensemble Prediction Products Associated with TIGGE

This project is supported by the 2007 R ＆ D Special Fund for Public Welfare by Ministry of Science and Technology and Ministry of Finance.Research tasks in this project are proposed based on the implementation plan of the＂THORPEX（The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble（TIGGE）＂,a sub-project of the THORPEX international program.

PROGRESS OF NUMERICAL WEATHER PREDICTION IN CHINA IN THE PAST DECADE

Review and analysis of NWP in China in the past decade have been made.Also comparisons have been done between NWP ten years ago and that of today from different aspects.From them it can be seen how rapid the progress was made during that period.Finally the differences between the advanced world level and ours in areas of NWP are estimated and the steps we should take are suggested.