NDLI: Artificial Neural Network Prediction for Seismic Response of Bridge Structure

Content Provider	IEEE Xplore Digital Library
Author	Wang Ying Li Hui Wang Chong Zhao Renda
Copyright Year	2009
Abstract	Based on identification and prediction ability of neural networks for nonlinear systems, an improved BP network was adopted to predict the seismic responses of the bridge structures. First, a multi-player BP networks based on Levenberg-Marquardt algorithm was formed. Then, the improved neural network was trained by the imitated seismic responses of the first 4 seconds which were obtained from artificial earthquake waves by finite element method. Thirdly, the seismic responses of 1st to 8th seconds for the same bridge structure were predicted use the neural network which has been trained, and the predict responses were compared with the calculation data. The error curves between the prediction and the calculation results show that the BP network combined with is Levenberg-Marquardt algorithm has very good convergence rate, and the artificial neural network can predict the dynamic response of bridge structures well enough.
Starting Page	503
Ending Page	506
File Size	270060
Page Count	4
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.303
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges Concurrent computing Neurons Artificial neural networks Computer architecture Computer networks Force control Artificial intelligence Biological neural networks Civil engineering
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A bridge between two paradigms for parallelism: neural networks and general purpose MIMD computers

Inverse kinematics learning by modular architecture neural networks with performance prediction networks

A neural network architecture for path planning

Identifying Prestress Loss of Long Span Bridge Based on Genetic Algorithm Combined with Neural Network

On neural network hardware and programming paradigms

Using NPN for neural network models

Dedicated deep neural network architectures and methods for their training

The Application of BP Neural Network in Cable-Stayed Bridge Construction Monitoring

Back-Propagation Neural Network Architecture for Solving the Double Dummy Bridge Problem in Contract Bridge

Artificial Neural Network Prediction for Seismic Response of Bridge Structure

Similar Documents

A bridge between two paradigms for parallelism: neural networks and general purpose MIMD computers

Inverse kinematics learning by modular architecture neural networks with performance prediction networks

A neural network architecture for path planning

Identifying Prestress Loss of Long Span Bridge Based on Genetic Algorithm Combined with Neural Network

On neural network hardware and programming paradigms

Using NPN for neural network models

Dedicated deep neural network architectures and methods for their training

The Application of BP Neural Network in Cable-Stayed Bridge Construction Monitoring

Back-Propagation Neural Network Architecture for Solving the Double Dummy Bridge Problem in Contract Bridge

Artificial Neural Network Prediction for Seismic Response of Bridge Structure