G
Gun Jin Yun
Researcher at Seoul National University
Publications - 135
Citations - 2366
Gun Jin Yun is an academic researcher from Seoul National University. The author has contributed to research in topics: Finite element method & Structural health monitoring. The author has an hindex of 24, co-authored 119 publications receiving 1779 citations. Previous affiliations of Gun Jin Yun include University of Akron & Washington University in St. Louis.
Papers
More filters
Journal ArticleDOI
Chaos-enhanced accelerated particle swarm optimization
TL;DR: This study introduces chaos into the APSO in order to further enhance its global search ability, and shows that the CAPSO with an appropriate chaotic map can clearly outperform standard APSO, with very good performance in comparison with other algorithms and in application to a complex problem.
Journal ArticleDOI
An evolutionary approach for modeling of shear strength of RC deep beams
TL;DR: In this paper, a new variant of genetic programming, namely gene expression programming (GEP), is utilized to predict the shear strength of reinforced concrete (RC) deep beams, and a constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters.
Journal ArticleDOI
Optimum design of tower structures using Firefly Algorithm
TL;DR: In this article, an adaptive Firefly Algorithm (FA) was presented that utilizes the feasible-based method to handle constraints, which is effective in improving the convergence and also suitable for expensive optimization tasks such as large-scale structures.
Journal ArticleDOI
Catalyst free self-healable vitrimer/graphene oxide nanocomposites
Balaji Krishnakumar,R. V. Siva Prasanna Sanka,Wolfgang H. Binder,Chanwook Park,Jiwon Jung,Vijay Parthasarthy,Sravendra Rana,Gun Jin Yun +7 more
TL;DR: In this article, a catalysts free graphene oxide (GO) promoted self-healing vitrimer nanocomposites are designed, where the synthesized vitrimers displays selfhealing properties via disulfide exchange based covalent adaptive network behavior.
Journal ArticleDOI
A new neural network‐based model for hysteretic behavior of materials
TL;DR: In this article, the authors proposed a novel approach for NN-based modeling of the cyclic behavior of materials, which uses new internal variables that facilitate the learning of the hysteretic behavior of material.