Crack analysis by using Fuzzy Logic

Pankaj Charan Jena

ISBN 10: 6139875285 / ISBN 13: 9786139875283

Published by LAP LAMBERT Academic Publishing Jul 2018, 2018

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book attempts to address artificial intelligence technique application to detect the pre-crack in engineering structures in the way that is useful for researchers and academicians alike who are interested in state of the art. It also attempts to help practitioners, particularly mechanical engineers, civil engineers, who are working in various machine structures. The first Chapter provides an overview of crack analysis by using the Sugeno Fuzzy Technique. The Second Chapter deals with the crack analysis of structural beam by using Neural Network Technique. Chapter 3 covers the crack analysis of composite beam structure by using a hybrid of Sugeno Fuzzy-Neural Network Techniques. 80 pp. Englisch.

Seller Inventory # 9786139875283

Bibliographic Details

• Title: Crack analysis by using Fuzzy Logic
• Publisher: LAP LAMBERT Academic Publishing Jul 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 6139875285
• ISBN 13: 9786139875283
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 137 grams

About this title

Synopsis

This book attempts to address artificial intelligence technique application to detect the pre-crack in engineering structures in the way that is useful for researchers and academicians alike who are interested in state of the art. It also attempts to help practitioners, particularly mechanical engineers, civil engineers, who are working in various machine structures. The first Chapter provides an overview of crack analysis by using the Sugeno Fuzzy Technique. The Second Chapter deals with the crack analysis of structural beam by using Neural Network Technique. Chapter 3 covers the crack analysis of composite beam structure by using a hybrid of Sugeno Fuzzy-Neural Network Techniques.