Arch bridge suspender tension identification by using neural network

Dongsheng Li; Jinping Ou

doi:10.1117/12.846724

8 April 2010 Arch bridge suspender tension identification by using neural network

Dongsheng Li, Jinping Ou

Proceedings Volume 7650, Health Monitoring of Structural and Biological Systems 2010; 765026 (2010) https://doi.org/10.1117/12.846724
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

The measurement of arch bridge suspender tension using vibration method is mostly considered that the suspender is idealized as taut strings. This idealization simplifies the analysis but may introduce unacceptable errors in many applications by ignoring the boundary condition and bending stiffness effects. The neural network intelligent methodology is proposed to compute suspenders tension, and design steps and optimization methods of neural network are given. In order to get correct neural network predictive model, 200 data of 20 bridges is used to train the neural network. The applicability of the proposed intelligent methodology is verified by comparison with the others method through a case. The result shows that the error isn't beyond percent of 6.

Citation Download Citation

Dongsheng Li and Jinping Ou "Arch bridge suspender tension identification by using neural network", Proc. SPIE 7650, Health Monitoring of Structural and Biological Systems 2010, 765026 (8 April 2010); https://doi.org/10.1117/12.846724

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available