A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A norm...A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.展开更多
The interval numbers are used to types and observation of sensors, a new fusion represent the characteristic values of object method for multi-sensor object recognition is proposed from the viewpoint of decision makin...The interval numbers are used to types and observation of sensors, a new fusion represent the characteristic values of object method for multi-sensor object recognition is proposed from the viewpoint of decision making theory. The method defines the distance matrix and grey association matrix between all object types and unknown object. After solving the optimization problem of maximizing the standard deviations for all attributes, the weights of the attributes are obtained. Thus, the result of recognition for the unknown object is given by the grey association degree. This method avoids the subjectivity of selecting attributes weights. It is straightforward and can be performed on computer easily. The simulated example demonstrates the feasibility and effectiveness of the proposed method.展开更多
文摘A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.
基金This project is supported by National Natural Science Foundation of China （10626029） Jiangxi Province Natural Science Foundation of China （0611082） Science and Technology Project of Jiangxi province educational department in China （GJJ08350）
文摘The interval numbers are used to types and observation of sensors, a new fusion represent the characteristic values of object method for multi-sensor object recognition is proposed from the viewpoint of decision making theory. The method defines the distance matrix and grey association matrix between all object types and unknown object. After solving the optimization problem of maximizing the standard deviations for all attributes, the weights of the attributes are obtained. Thus, the result of recognition for the unknown object is given by the grey association degree. This method avoids the subjectivity of selecting attributes weights. It is straightforward and can be performed on computer easily. The simulated example demonstrates the feasibility and effectiveness of the proposed method.
