Abstract
The objective of this research is to develop a microelectromechanical system (MEMS)-based intelligent hybrid Biaxial Strain Transducer (BiAST) sensor for predicting railroad fatigue life based on strain history. The developed BiAST prototype was deployed to collect real-time strain data from the full-scale test track at the Transportation Technology Center (TTCI), near Pueblo, Colorado. The collected strain data were analyzed using the “Binner” fatigue analysis program for counting the load cycles and estimating the fatigue life of a rail structure. Field-testing results of the BiAST were used to evaluate the BiAST prototype with respect to its repeatability, accuracy, and hybridization. BiAST was effective in detecting the dynamic response of a particular wheel and spurious overload events. BiAST can be used to detect passing wheels, train speed, and track condition.
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