This paper presents a method for seismic vulnerability analysis of bridge structures based on vector-valued intensity measure （viM）, which predicts the limit-state capacities efficiently with multi-intensity measure...This paper presents a method for seismic vulnerability analysis of bridge structures based on vector-valued intensity measure （viM）, which predicts the limit-state capacities efficiently with multi-intensity measures of seismic event. Accounting for the uncertainties of the bridge model, ten single-bent overpass bridge structures are taken as samples statistically using Latin hypercube sampling approach. 200 earthquake records are chosen randomly for the uncertainties of ground motions according to the site condition of the bridges. The uncertainties of structural capacity and seismic demand are evaluated with the ratios of demand to capacity in different damage state. By comparing the relative importance of different intensity measures, Sa（T1） and Sa（T2） are chosen as viM. Then, the vector-valued fragility functions of different bridge components are developed. Finally, the system-level vulnerability of the bridge based on viM is studied with Duunett- Sobel class correlation matrix which can consider the correlation effects of different bridge components. The study indicates that an increment IMs from a scalar IM to viM results in a significant reduction in the dispersion of fragility functions and in the uncertainties in evaluating earthquake risk. The feasibility and validity of the proposed vulnerability analysis method is validated and the bridge is more vulnerable than any components.展开更多
In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to thei...In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.展开更多
基金National Program on Key Basic Research Project of China(973)under Grant No.2011CB013603National Natural Science Foundation of China under Grant Nos.51378341,91315301Tianjin Municipal Natural Science Foundation under Grant No.13JCQNJC07200
文摘This paper presents a method for seismic vulnerability analysis of bridge structures based on vector-valued intensity measure （viM）, which predicts the limit-state capacities efficiently with multi-intensity measures of seismic event. Accounting for the uncertainties of the bridge model, ten single-bent overpass bridge structures are taken as samples statistically using Latin hypercube sampling approach. 200 earthquake records are chosen randomly for the uncertainties of ground motions according to the site condition of the bridges. The uncertainties of structural capacity and seismic demand are evaluated with the ratios of demand to capacity in different damage state. By comparing the relative importance of different intensity measures, Sa（T1） and Sa（T2） are chosen as viM. Then, the vector-valued fragility functions of different bridge components are developed. Finally, the system-level vulnerability of the bridge based on viM is studied with Duunett- Sobel class correlation matrix which can consider the correlation effects of different bridge components. The study indicates that an increment IMs from a scalar IM to viM results in a significant reduction in the dispersion of fragility functions and in the uncertainties in evaluating earthquake risk. The feasibility and validity of the proposed vulnerability analysis method is validated and the bridge is more vulnerable than any components.
基金supported by the National Basic Research Program of China (973 Program) (No. 2013CB733600)the National Natural Science Foundation of China (No. 21176073)+1 种基金the Program for New Century Excellent Talents in University (No. NCET-09-0346)the Fundamental Research Funds for the Central Universities, China
文摘In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.
