京都大学 · 工学
Bach Do教授の研究室は、構造物の疲労亀裂修復や地震応答最適設計を対象とし、主にファイバー強化ポリマー（FRP）パッチによる修復技術と、多目的最適化・多忠実度最適化に基づくシミュレーション駆動型設計手法の開発を進めています。有限要素法（FE）と遺伝的プログラミング（GP）、ベイズ最適化（BO）を統合した知的最適化アプローチを用い、コストと信頼性の両立を実現する設計意思決定支援技術の構築が主な研究テーマです。特に、疲労限界を下回る応力集中係数の低減と、材料・幾何寸法の最適化を同時に達成するための新規補修設計手法の確立を目指しています。
Figures are computed from collected data and may differ slightly.
A practical design optimization of fiber-reinforced polymer (FRP) patches for repairing fatigue cracks in metallic structures is presented. The design procedure combines finite-element (FE), genetic programming (GP), and genetic algorithm (GA) approaches. An optimum patch design is defined as the combination of design parameters that simultaneously minimizes the patch volume and reduces the stress intensity factor (SIF) range below the fatigue threshold range. A patching correction factor, which
Abstract Bayesian optimization (BO) has become a powerful tool for solving simulation-based engineering optimization problems thanks to its ability to integrate physical and mathematical understandings, consider uncertainty, and address the exploitation–exploration dilemma. Thompson sampling (TS) is a preferred solution for BO to handle the exploitation–exploration tradeoff. While it prioritizes exploration by generating and minimizing random sample paths from probabilistic models—a fundamental
Abstract This work presents a novel sequential sampling approach to the multi‐objective reliability‐based design optimization of moment‐resisting steel frames subjected to earthquake excitation. The optimization problem is formulated with two objective functions, namely, the total mass and the energy dissipated by beam members of the frame, and subject to uncorrelated probabilistic constraints on dynamic responses under the effects of correlated random parameters of floor masses, external loads,
Resided at the intersection of multifidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation–exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive revi
This paper presents an approach that combines the finite element (FE) modeling and genetic programming (GP) to provide accurate empirical stress intensity factor (SIF) equations for center-cracked steel plates repaired with adhesive-bonded double-sided fiber-reinforced polymer (FRP) patches. Several past studies in recent years independently showed that the reduction on the SIF of cracked structures after the patch repair is dependent on many factors such as bonding techniques, material paramete
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive rev
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