Gun Jin Yun

Bio: 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.

An evolutionary approach for modeling of shear strength of RC deep beams

Abstract: In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. The model was developed using 214 experimental test results obtained from previously published papers. A comparative study was conducted between the results obtained by the proposed model and those of the American Concrete Institute (ACI) and Canadian Standard Association (CSA) models, as well as an Artificial Neural Network (ANN)-based model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via some previous laboratory studies. The results indicate that the GEP model gives precise estimations of the shear strength of RC deep beams. The prediction performance of the model is significantly better than the ACI and CSA models and has a very good agreement with the ANN results. The derived design equation provides a valuable analysis tool accessible to practicing engineers.

Optimum design of tower structures using Firefly Algorithm

Abstract: SUMMARY The Firefly Algorithm (FA) as a recent new meta-heuristic optimization algorithm is developed for determining optimum design of tower shaped structures. The FA mimics the social behavior of fireflies, which communicate, search for pray and find mates using bioluminescence with varied flashing patterns. In this paper, an adaptive FA is presented that utilizes the feasible-based method to handle constraints. This method is effective in improving the convergence and also suitable for expensive optimization tasks such as large-scale structures. Three tower structures are selected to evaluate the performance of the algorithm. The results are better than the other results proposed in the literature and confirm the validity of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

Catalyst free self-healable vitrimer/graphene oxide nanocomposites

Abstract: Catalyst free graphene oxide (GO) promoted self-healing vitrimer nanocomposites are designed, where the synthesized vitrimer nanocomposites displays self-healing properties via disulfide exchange based covalent adaptive network behavior. This study found that GO based nanofiller enhance the self-healing properties, including the shape memory and flexural strength of the materials. The GO induced lower glass transition was helpful to achieve low temperature self-healing: when compared to epoxy vitrimers (73% and 60% self-healing) the vitrimeric nanocomposites demonstrates a 88% and 80% self-healing for the first and second cycle, respectively.

A new neural network‐based model for hysteretic behavior of materials

Abstract: Cyclic behavior of materials is complex and difficult to model. A combination of hardening rules in classical plasticity is one possibility for modeling this complex material behavior. Neural network (NN) constitutive models have been shown in the past to have the capability of modeling complex material behavior directly from the results of material tests. In this paper, we propose a novel approach for NN-based modeling of the cyclic behavior of materials. The proposed NN material model uses new internal variables that facilitate the learning of the hysteretic behavior of materials. The same approach can also be used in modeling of the hysteretic behavior of structural systems or structural components under cyclic loadings. The proposed model is shown to be superior to the earlier versions of NN material models. Although the earlier versions of the NN material models were effective in capturing the multi-axial material behavior, they were only tested under cyclic uni-axial state of stress. The proposed NN material model is capable of learning the hysteretic behavior of materials under even non-uniform stress state in multi-dimensional stress space. The performance of the proposed model is demonstrated through a series of examples using actual experimental data and simulated testing data.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Self-healing polymers and composites

Abstract: Inspired by the unique and efficient wound healing processes in biological systems, several approaches to develop synthetic polymers that can repair themselves with complete, or nearly complete, autonomy have recently been developed. This review aims to survey the rapidly expanding field of self-healing polymers by reviewing the major successful autonomic repairing mechanisms developed over the last decade. Additionally, we discuss several issues related to transferring these self-healing technologies from the laboratory to real applications, such as virgin polymer property changes as a result of the added healing functionality, healing in thin films v. bulk polymers, and healing in the presence of structural reinforcements.

A comprehensive review of firefly algorithms

Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

Nature-Inspired Optimization Algorithms

Abstract: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm