A Genetic-Based Iterative Quantile Regression Algorithm for Analyzing Fatigue Curves
- Authors
- Park, Jong In; Kim, Norman; Bae, Suk Joo
- Issue Date
- Dec-2012
- Publisher
- WILEY
- Keywords
- fatigue curves; iterative quantile regression; genetic algorithms; structural risk minimization; censored data; general approximate cross-validation error
- Citation
- QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, v.28, no.8, pp.897 - 909
- Indexed
- SCIE
SCOPUS
- Journal Title
- QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
- Volume
- 28
- Number
- 8
- Start Page
- 897
- End Page
- 909
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164101
- DOI
- 10.1002/qre.1280
- ISSN
- 0748-8017
- Abstract
- Accurate prediction of fatigue failure times of materials such as fracture and plastic deformation at various stress ranges has a strong bearing on practical fatigue design of materials. In this study, we propose a novel genetic-based iterative quantile regression (GA-IQR) algorithm for analyzing fatigue curves that represent a nonlinear relationship between a given stress amplitude and fatigue life. We reduce the problem to a linear framework and develop the iterative algorithm for determining the model coefficients including unknown fatigue limits. The procedure keeps updating the estimates in a direction to reduce its resulting error. Also, our approach benefits from the population-based stochastic search of the genetic algorithms so that the algorithm becomes less sensitive to its initialization. Compared with conventional approaches, the proposed GA-IQR requires fewer assumptions to develop fatigue model, capable of exploring the data structure in a relatively flexible manner. All procedures and calculations are quite straightforward, such that the proposed quantile regression model has a high potential value in a wide range of applications for exploring nonlinear relationships with lifetime data. Computational results for real data sets found in the literature present good evidences to support the argument.
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