A Hybrid FEM-CNN for Image-Based Severity Prediction of Corroded Offshore Pipelines
The combination of the Finite Element Method (FEM) with Convolutional Neural Networks (CNNs) presents a key breakthrough in the assessment of the structural integrity of offshore pipelines. The advantage of the standard FEM is in stress visualization, but it is time-consuming due to high computation...
Saved in:
Main Authors: | Mohammad Fadzil Najwa, Muda Mohd Fakri, Abdul Shahid Muhammad Daniel, Aziz Norheliena, Mohd Mohd Hairil, Mohd Amin Norliyati, Mohd Hashim Mohd Hisbany |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2025-01-01
|
Series: | E3S Web of Conferences |
Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/12/e3sconf_aere2025_04003.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Impact of Movement Control Order (MCO) during Pandemic COVID-19 on Local Air Quality in an Urban Area of Klang Valley, Malaysia
by: Mohd Shahrul Mohd Nadzir, et al.
Published: (2020-05-01) -
Fatwa-Related Research and Writing: A Bibliometric Analysis of Malaysian Thesis Online Over Four Decades
by: Abdul Azib Hussain, et al.
Published: (2024-11-01) -
The Concept of Ḥiyāl in Marriage: A Preliminary Review
by: Ahmad Syakir Mohd Nassuruddin, et al.
Published: (2024-11-01) -
The Issue of Grooms Wearing Henna on Their Fingers: An Analysis Based on the Syafii School and Fatwa in Malaysia
by: Mohd Azhar Abdullah, et al.
Published: (2023-05-01) -
Contribution of Aerosol Species to the 2019 Smoke Episodes over the East Coast of Peninsular Malaysia
by: Nur Nazmi Liyana Mohd Napi, et al.
Published: (2022-05-01)