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Engineering - Neural Network Damage Detect in Bridges

Infohash:

CA01ABEA3967278DAABAF9CE9C23D58C843BA61E

Type:

Books

Title:

Engineering - Neural Network Damage Detect in Bridges

Category:

Other/E-books

Uploaded:

2011-02-21 (by simon50827)

Description:

Damage Detection of Bridge Decks Using Artificial Neural Networks: The implementation of the Back-Propagation Algorithm to predict the location and severity of the damage in a bridge deck Editorial Reviews Product Description This book analyses the feasibility and the practicality of a structural damage detection system for bridges using artificial intelligence. The artificial intelligence used is known as an Artificial Neural Network, a computer program loosely based on the design of the human brain and known for its learning capability. Several damage cases are analysis and tested to train the Neural Network after which, given a set of inputs, it has the capability of predicting the state and location of the damage in a bridge deck. This book represents a perfect introduction to the basic theory and practicality of using Neural Networks in engineering applications. It furthers this concept by applying the mathematical theory into a feasible and practical tool for practicing engineers. About the Author Simon Mitton studied Structural Engineering with Architecture in University College Dublin. His main field of interest lies in bridge engineering where he has already undertaken 2 major research projects by the age of 23, one of which is contain in the book and the other in the dynamics of cables of cable-stayed structures. Product Details Paperback: 64 pages Publisher: LAP LAMBERT Academic Publishing (February 13, 2011) Language: English ISBN-10: 3838388836 ISBN-13: 978-3838388830 Product Dimensions: 8.7 x 5.9 x 0.2 inches http://www.amazon.com/Damage-Detection-Bridge-Artificial-Networks/dp/3838388836/ref=sr_1_8?s=books&ie=UTF8&qid=1298321733&sr=1-8

Tags:

  1. Engineer
  2. Structural
  3. Network
  4. book
  5. damage
  6. detection

Files count:

1

Size:

992.29 Kb

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