Access the full text.
Sign up today, get DeepDyve free for 14 days.
Over time, the traffic density increases and the anti-collision capacity of the bridge decreases, the ship-bridge collision risk also changes. In order to dynamically predict the time-dependent ship-bridge collision risk, providing the basis for the design, management and maintenance of the bridge, taking Pinghai Bridge as the engineering background, introducing time factor, a dynamic ship-bridge collision risk decision method based on improved AASHTO model is proposed for the application of risk assessment during the bridge design stage and service stage. Based on AASHTO model, the time factor is introduced. In accordance with the navigation conditions in the bridge area, the annual ship traffic volume and the annual ship traffic densities are statistically counted. In accordance with the variations of the ship volume and ship traffic densities over time, the dynamic aberrance probability is calculated. The dynamic annual impact frequency is obtained by combining the above ship traffic volume, aberrance probability and geometric collision probability. In accordance with the probability of the acceptable criterion, the pier ultimate bridge strength is determined based on the design of the annual collision damage frequency. Considering the ultimate bridge strength attenuation result from concrete carbonation, steel corrosion and foundation scouring, the time-dependent annual collision damage frequency can be estimated. The method proposed in this paper can dynamically predict the collision damage probability of the bridge pier in future service times and has scientific significance and application prospect for the anti-collision design of bridges.
Journal of The Institution of Engineers (India): Series A – Springer Journals
Published: Jan 15, 2021
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.