Fatigue is a primary concern for railroad bridge owners because railroad bridges typically have high live load to dead load ratios and high stress cycle frequencies. However, existing inspection and post-inspection analysis methods are unable to accurately consider the full influence of bridge behavior on the fatigue life of bridge components. Reliability-based fatigue analysis methods have emerged to account for uncertainties in analysis parameters such as environmental and mechanical properties. While existing literature proposes probabilistic fatigue assessment of bridge components, this body of work relies on train parameter estimates, finite element model simulations, or controlled loading tests to augment monitoring data. This article presents a probabilistic fatigue assessment of monitored railroad bridge components using only continuous, long-term response data in a purely data-driven reliability framework that is compatible with existing inspection methods. As an illustrative example, this work quantifies the safety profile of a fracture-critical assembly comprising of six parallel eyebars on the Harahan Bridge (Memphis, TN). The monitored eyebars are susceptible to accelerated fatigue damage because changes in the boundary conditions cause some eyebars to carry a greater proportion of the total assembly load than assumed during design and analysis; existing manual inspection practices aim to maintain an equal loading distribution across the eyebars. Consequently, the limit state function derived in this article accounts for the coupled behavior between fatigue and relative tautness of the parallel eyebars. The reliability index values for both the element (i.e. individual eyebars) and system (i.e. full eyebar assembly) reliability problems are assessed and indicate that under the conservative assumption that progressive failure is brittle, first failure within the parallel eyebar system is generally equivalent to system failure. The proposed method also serves as an intervention strategy that can quantify the influence of eyebar realignment on the future evolution of the reliability index.

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There is an urgent need to better understand vehicle-rail interaction dynamics to pave the way for more consistent and automated rail crack detection methodologies, as opposed to relying on periodic and manual detection via track circuits or dedicated track geometry cars. Designing an open-source hardware framework for a lab-scale rail testbed would open the doors to further data collection and analysis needed to understand the dynamic response of cracked rails. We present a framework and the corresponding open-source hardware and software (published to GitHub) for developing a laboratory-scale motorized railroad testbed, with a vehicle that is modularly tuned to the dynamics of an in-service rail car.

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