Mayorkinos Papaelias

Professor in NDT and Condition Monitoring

University of Birmingham

United Kingdom

Research Interests

Explore related searches

Professors in United Kingdom

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Mayorkinos Papaelias is a Professor in NDT and Condition Monitoring at the University of Birmingham, UK. His research focuses on life cycle cost analysis, damage assessment in composite materials, and the application of artificial intelligence in marine science and engineering. Recent publications include work on autonomous underwater vehicles, machine condition monitoring, and structural health assessment of railway systems.

Articles (12)

Year:

2025

Year:

2025

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Collaborators (4)

Fausto Pedro García Márquez

Full Professor

University of Castilla-La Mancha

SPAIN

View details

Farzad Hayati

University of Birmingham

UNITED KINGDOM

View details

Wenxian Yang

Senior Lecturer in Offshore Renewable Energy

Newcastle University

UNITED KINGDOM

View details

Isaac Segovia Ramírez

Assistant Professor

Universidad Autónoma de Madrid

SPAIN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Mayorkinos Papaelias

Research Interests

Explore related searches

Contact this professor

About

Articles (12)

Life cycle cost analysis of unmanned surface vehicle from ENDURUNS and MERLIN projects powered by renewable energies: A real case study

Multi-Variant Damage Assessment in Composite Materials Using Acoustic Emission

Life cycle assessment of an autonomous underwater vehicle that employs hydrogen fuel cell

Review on additive manufacturing and non-destructive testing

Advances in Machine Condition Monitoring and Fault Diagnosis

Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle

A Novel Hybrid Method for Predicting Vertical Levelling Loss of Railway Track Geometry Under Dynamic Cyclic Loadings

Artificial Intelligence in Marine Science and Engineering

Fatigue damage assessment of complex railway turnout crossings via Peridynamics-based digital twin

Quantitative analysis of the structural health of railway turnouts using the acoustic emission technique

Train-track interactions over vulnerable railway turnout systems exposed to flooding conditions

Autonomous underwater vehicles and field of view in underwater operations

Collaborators (4)

Fausto Pedro García Márquez

Farzad Hayati

Wenxian Yang

Isaac Segovia Ramírez

Make your next move.