A5004834009- Probabilistic and Robust Engineering Design
- Advanced Multi-Objective Optimization Algorithms
- Optimal Experimental Design Methods
- Topology Optimization in Engineering
- Reliability and Maintenance Optimization
- Fatigue and fracture mechanics
- Structural Health Monitoring Techniques
- Statistical Distribution Estimation and Applications
- Manufacturing Process and Optimization
- Solar Thermal and Photovoltaic Systems
- Composite Structure Analysis and Optimization
- Acoustic Wave Phenomena Research
- Heat Transfer and Optimization
- Electric Vehicles and Infrastructure
- Asphalt Pavement Performance Evaluation
- Wind and Air Flow Studies
- Infrastructure Maintenance and Monitoring
- Transportation and Mobility Innovations
- Transportation Planning and Optimization
- Concrete Corrosion and Durability
- solar cell performance optimization
- Photovoltaic System Optimization Techniques
- High-Velocity Impact and Material Behavior
- Building Energy and Comfort Optimization
- Nanofluid Flow and Heat Transfer
Korea Advanced Institute of Science and Technology
2016-2025
University of Iowa
2006-2024
Ministry of Land, Infrastructure and Transport
2020
Korea Institute of Science and Technology
2016
University of Connecticut
2012-2013
Soonchunhyang University
2013
Metamodeling has been widely used for design optimization by building surrogate models computationally intensive engineering application problems. Among all the metamodeling methods, kriging method gained significant interest its accuracy.However, in traditional krigingmethods, themean structure is constructed using a fixed set of polynomial basis functions, and methods to obtain optimal correlation parameter may not yield an accurate optimum. In this paper, new called dynamic proposed fit...
Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness adopting deep for generative (or exploration) area. work proposes an artificial intelligent (AI)-based automation framework that is capable generating numerous options which are not only aesthetic but also optimized engineering performance. The proposed integrates topology optimization models (e.g., adversarial networks (GANs)) in iterative manner explore...
First-order reliability method (FORM) has been mostly utilized for solving reliability-based design optimization (RBDO) problems efficiently. However, second-order (SORM) is required in order to estimate a probability of failure accurately highly nonlinear performance functions. Despite accuracy SORM, its application RBDO quite challenging due unaffordable numerical burden incurred by Hessian calculation. For reducing the efforts, quasi-Newton approach approximate introduced this study...
This paper proposes a novel second-order reliability method (SORM) using noncentral or general chi-squared distribution to improve the accuracy of analysis in existing SORM. Conventional SORM contains three types errors: (1) error due approximating nonlinear limit state function by quadratic at most probable point standard normal U-space, (2) U-space parabolic surface, and (3) calculation probability failure after making previous two approximations. The proposed first type only, which is...
This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are mean values of input correlated random variables. Assuming that an accurate surrogate model is available, proposed method calculates component reliability, system or statistical moments and their by applying Monte Carlo simulation model. Since used, computational cost sensitivity analysis affordable compared use actual models. The copula used joint distribution score...
This article presents reliability analysis and reliability-based optimization of roadway minimum radius design based on vehicle dynamics, mainly focusing exit ramps interchanges. The performance functions are formulated as failure modes rollover sideslip. To accurately describe the modes, analytical models for sideslip derived considering nonlinear characteristics behaviour using commercial software TruckSim. probability an accident is evaluated first-order method numerical studies conducted...
Recent advances in deep learning enable machines to learn existing designs by themselves and create new designs. Generative adversarial networks (GANs) are widely used generate images data unsupervised learning. Certain limitations exist applying GANs directly product It requires a large amount of data, produces uneven output quality, does not guarantee engineering performance. To solve these problems, this paper proposes design automation process combining topology optimization. The...