Shehata, HM, Mohamed, YS, Abdellatif, M orcid.org/0000-0002-7641-4723 et al. (1 more author) (2018) Crack Width Estimation Using Feed and Cascade Forward Back Propagation Artificial Neural Networks. Key Engineering Materials, 786. pp. 293-301. ISSN 1013-9826
Abstract
Automatic crack inspection techniques that limit the necessity of human have the potential to lower the cost and time of the process. In this study, a maximum crack width estimation approach is presented. Seventy nine segments of cracks are used for training the neural networks and twenty six segments are used for examination. The maximum width for each segment is measured using laser scanning microscope and segment image is captured and magnified using the microscope camera in order to obtain the extracted crack profile number of pixels. Feed and cascade forward back propagation artificial neural networks are designed and constructed. The input and output for the networks are the crack width in terms of number of pixels and the maximum estimated crack width respectively. It is shown that, the artificial neural networks technique can effectively be used to estimate the crack width. The feedforward back propagation structure which is designed with two layers and training function TRAINLM gives the best results in examination.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jul 2020 14:29 |
Last Modified: | 21 Jul 2020 10:08 |
Status: | Published |
Publisher: | Trans Tech Publications, Ltd. |
Identification Number: | 10.4028/www.scientific.net/kem.786.293 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162298 |