Accordingly the maintenance need

The results indicated the obtained ANN model can predict the condition rating of the bridge deck with an accuracy of 73.6%. If a margin error of ±1 was used, the accuracy of the proposed model reached a much higher value of 98.5%. Besides, a sensitivity analysis was conducted for individual input parameters revealed that Current Bridge Age was the most important predicting parameter of bridge deck rating. It was followed by the Design Load and Main Structure Design.

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The Department of Transportation, for example, does not fully consider how increased investment in passenger or freight railways might alleviate the need for additional road and highway expenditures, and does not coordinate the landside port improvements it funds with Army Corps of Engineers waterside invest- ments at the very same ports.

282p enterroi 02-02-2013 50 5   Download