Computational Methods for Estimating Rail Life

In American rail operations, rails fail due to the combined effects of rail wear due to repetitive wheel contact and the growth of surface and sub-surface cracks and flaws. Rail maintenance includes frequent uncoupled wear and ultrasonic inspections that determine the amount of wear that the rail has undergone and the presence of cracks and flaws. A rail is removed from service when its wear reaches a pre-determined wear limit or a flaw is detected in its cross section. In rail research, the life of a rail is typically estimated using fracture mechanic or fatigue methods and an assumed flaw geometry. Multiple models ranging from complex elastic-plastic finite element models to simplified representations of a beam on an elastic foundation have been developed to predict the life of a rail. The majority of rail failure models do not incorporate rail wear into their analysis, and assume an unworn rail geometry. In order to account for rail wear, certain models adopt simplified rail geometries that uncouple rail wear into top-wear and side-wear.

This thesis presents a rail failure model that describes the combined effects of rail wear and crack growth through the development of a functional relationship between input variables describing the geometry, loading, and material properties of a given rail and output variables describing the life characteristics of the rail. This relationship takes the form of multiple response surfaces estimating the desired output variables. Finite element models incorporating worn rail profiles and an assumed crack geometry corresponding to a detail fracture are combined to determine the state of stress and strain at the assumed flaw. Strain-life fatigue methods and fracture mechanic concepts are used to develop the output variables necessary to describe the life of the rail using the finite element model results. The goals of this research are to predict the remaining fatigue life and estimate the crack-growth rate of the rail based on the minimum number of geometry, loading, and material property independent variables. The outputs developed to describe the rail's remaining life are intended to be used for the decision making for rail removal.