Article
Engineering, Industrial
Yongsu Jung, Hwisang Jo, Jeonghwan Choo, Ikjin Lee
Summary: This paper proposes a statistical model calibration method using stochastic Kriging to handle the uncertainty of model parameters and model discrepancy, as well as the epistemic uncertainty caused by insufficient data. The proposed method can be applied in reliability analysis and design optimization.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Hardware & Architecture
Chunyan Ling, Way Kuo, Min Xie
Summary: This study reviews the advantages and disadvantages of using surrogate models to streamline reliability-based design optimization (RBDO), as well as discussing the problems that need to be solved.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Interdisciplinary Applications
Jolan Wauters, Ivo Couckuyt, Joris Degroote
Summary: This paper presents a novel scheme for reliability-based design optimization, utilizing surrogate-assisted asymptotic reliability analysis to obtain gradient and Hessian information. The sub-optimization problem is reformulated as a set of constraints using the Karush-Kuhn-Tucker conditions, leading to an efficient single-loop approach.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Multidisciplinary
Wanyi Tian, Weiwei Chen, Bingyu Ni, Chao Jiang
Summary: The paper introduces an approach for handling uncertainties in reliability-based design optimization, proposing a probability-interval hybrid model and a single-loop solution algorithm. The efficiency and accuracy of the method are demonstrated through practical engineering problems.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Mechanical
Haichao An, Byeng D. Youn, Heung Soo Kim
Summary: This paper proposes a reliability-based design framework that considers both delamination and material property uncertainties in composite structures during manufacturing process. By utilizing a new reliability analysis method and surrogate modeling approach for handling mixed continuous-discrete random variables, the accuracy and efficiency of the design are improved.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Shuang Zhou, Jianguo Zhang, Qingyuan Zhang, Ying Huang, Meilin Wen
Summary: This paper proposes an efficient uncertainty theory-based reliability analysis and design method to address the trade-offs between safety and cost in the early stage of structural design. It introduces the concept of uncertain measure and uses URI to estimate the reliable degree of structure. A URI-based design optimization model (URBDO) is constructed to tackle the problem of insufficient data in reliability analysis.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Shuang Zhou, Jianguo Zhang, Qingyuan Zhang, Meilin Wen
Summary: This paper proposes a methodology of hybrid reliability analysis and optimization based on chance theory to control aleatory and epistemic uncertainties in the preliminary design phase of engineering structures. It uses random variables to describe aleatory uncertainty and uncertain variables to quantify epistemic uncertainty. The chance measure and chance reliability indicator (CRI) are introduced to model structural reliability in the presence of hybrid uncertainty. Two CRI estimation methods and two solving strategies are developed for mixed reliability assessment and design optimization. The performance and feasibility of the proposed analysis model and solution technique are verified through four engineering applications.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Multidisciplinary
Yubing Chen, Meilin Wen, Qingyuan Zhang, Rui Kang
Summary: In this paper, a new belief reliability-based design optimization (BRBDO) method is established to handle the impact of epistemic uncertainty on product reliability design optimization. A quantile index is proposed to quantify belief reliability level based on uncertainty theory, and a rapid analysis method called first order belief reliability analysis (FOBRA) is developed. Two types of design optimization models are established according to different trade-off strategies, and corresponding FOBRA-based computation methods are also demonstrated. Several case applications are conducted to verify the effectiveness of the proposed analysis and design optimization methods.
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
(2023)
Article
Materials Science, Multidisciplinary
Khalil Dammak, Ahmad Baklouti, Abdelkhalak El Hami
Summary: In this article, structural and thermal analysis are conducted on the disk to optimize the design by minimizing volume while meeting stress and temperature constraints. The Kriging meta-model is employed to provide a more accurate approximation of the original model with 50 LHS points. The optimal design results in a weight reduction of 32.67% compared to the initial model.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Civil
Mohammad Javad Ghasemi Rad, Sima Ohadi, Jafar Jafari-Asl, Arash Vatani, Sanaz Afzali Ahmadabadi, Jose A. F. O. Correia
Summary: This study proposed an efficient framework for probabilistic design optimization of gravity dams, using a new reliability-based design optimization approach. By coupling a hybrid Support Vector Regression-based generalized normal distribution optimization model to Monte Carlo Simulation, the research successfully optimized the design of a concrete gravity dam, increasing safety level and significantly reducing failure probability.
Article
Mechanics
Sang Min Baek, Won Jun Lee
Summary: Functional composites with nanofillers are used to enhance radar absorbing structures (RAS), with the relative permittivity of RAS substrate measured under various environmental conditions. Maximum likelihood estimation is used to determine probability distributions of dielectric materials for RAS, and RBDO is applied for reliability-based design optimization. RBDO results in a significant reduction in failure probability despite a slight increase in total thickness.
COMPOSITE STRUCTURES
(2021)
Article
Engineering, Civil
Pinghe Ni, Jun Li, Hong Hao, Hongyuan Zhou
Summary: This paper presents a reliability-based design optimization method for bridge structures, which considers uncertainties of material parameters and bridge-vehicle interaction. The proposed approach is validated through numerical studies on simply supported beam and box-section bridge, demonstrating its efficiency and accuracy in determining the minimum required cross-section area under probability constraints.
ENGINEERING STRUCTURES
(2021)
Article
Construction & Building Technology
Deepthi Mary Dilip, G. L. Sivakumar Babu
Summary: Recognizing the importance of sustainable and durable structures, reliability-based designs are increasingly being adopted in pavement engineering. This study proposes the Reliability Based Design Optimization (RBDO) to create cost-effective flexible pavements considering uncertainties. Two meta-modelling techniques, second-order adaptive Response Surface Model (RSM) and adaptive Polynomial-Chaos based Kriging (PC-Kriging), are considered. The study highlights the need for adaptive meta-modelling approaches to address epistemic uncertainties. The proposed System Reliability Based Design Optimization (SRBDO) method integrates economic analysis and the Mechanistic-Empirical procedure to optimize pavement layer thicknesses and moduli.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Review
Computer Science, Interdisciplinary Applications
Z. Zhang, C. Jiang
Summary: Epistemic uncertainty is common in the early design stage of engineering structures and needs to be effectively quantified and managed. Evidence theory provides a promising model for dealing with epistemic uncertainty in structural reliability analysis.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Dongjin Lee, Sharif Rahman
Summary: This article introduces a new computational method, the MPSS-GPCE method, for reliability-based design optimization of complex mechanical systems. The method allows for simultaneous computation of failure probability and design sensitivities from a single stochastic simulation, making it applicable for solving industrial-scale problems with large design spaces.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)