Systems-Modelling of Compact Tension Energy in High Strength Pipeline Steel

Zhang, Guangrui and Mahfouf, Mahdi and Zhang, Qian and Gaffour, Sidahmed and Yates, John R. and Ayvar-Soberanis, Sabino and Pinna, Christophe and Panoutsos, George (2011) Systems-Modelling of Compact Tension Energy in High Strength Pipeline Steel. In: World Congress. pp. 12126-12131. (doi:https://doi.org/10.3182/20110828-6-IT-1002.01466) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://doi.org/10.3182/20110828-6-IT-1002.01466

Abstract

The research in this paper aims at linking the steel crack propagation process to the prediction of released flat fracture energy. In this work, an adaptive fuzzy modelling approach and a neural network model with double loop training process were developed in order to build the model for the prediction of flat fracture energy which is normally released during the compact tension (CT) test of X100 pipeline steel. Using the proposed modelling technique, a rule-based fuzzy prediction model as well as a Back-Propagation (BP) neural network model were constructed and optimized automatically from the experimental data. These two models related the load, crack mouth opening displacement (CMOD), and crack length to the released flat fracture energy. The relationship between the fracture propagation and the flat fracture energy was investigated in this paper in detail. The results showed that it is able to predict the flat fracture energy using the proposed modelling approach, and the resulting model can be a useful part of a more comprehensive modelling structure in the future.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:50 UTC
Last Modified: 22 Sep 2015 08:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50545 (The current URI for this page, for reference purposes)
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