Gin Boay Chai

Associate Professor

Nanyang Technological University (NTU)

Singapore

Research Interests

Explore related searches

Professors in Materials Science

Contact this professor

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

About

Dr. Gin Boay Chai is an Associate Professor at Nanyang Technological University in Singapore. His research primarily focuses on the mechanical properties and performance analysis of composite materials, with recent publications exploring topics such as tensile failure in bolted composite laminates, the effects of graphene oxide in nylon composites, and the fatigue behavior of hygrothermal aged composite laminates. Additionally, he conducts probabilistic structural reliability analyses and multiscale finite element simulations to advance the understanding of composite material behaviors.

Articles (10)

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (1)

Sunil Chandrakant Joshi

Nanyang Technological University (NTU)

SINGAPORE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Gin Boay Chai

Research Interests

Explore related searches

Contact this professor

About

Articles (10)

Numerical analysis of tensile failure of bolted composite laminates

Compression properties of carbon fiber reinforced polymer grid sandwich structure

Effect of GO agglomeration on the mechanical properties of graphene oxide and nylon 66 composites and micromechanical analysis

Multiscale finite element simulation of mechanical properties of pillared graphene sheet reinforced composites

Quasi-static indentation characteristics of sandwich composites with hybrid facesheets: Experimental and numerical approach

Study of statistically significant strength degradation of hygrothermal aged CFRP and its weibull analysis

Probabilistic structural reliability analysis of a horizontal axis tidal turbine blade by considering the moisture effects on the blade material

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble-Based Machine Learning Model

Influence of stress ratio and stress concentration on the fatigue behaviour of hygrothermal aged multidirectional CFRP composite laminate

Probabilistic bending behaviour of a symmetric multi-directional composite laminate subjected to moisture induced material property asymmetry

Collaborators (1)

Sunil Chandrakant Joshi

Make your next move.