A Genetic-Based Iterative Quantile Regression Algorithm for Analyzing Fatigue Curves

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. Copyright (c) 2012 John Wiley & Sons, Ltd.