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contributor authorM. Obadat
contributor authorHosin “David” Lee
contributor authorM. Asghar Bhatti
contributor authorBrian Maclean
date accessioned2017-05-08T21:16:08Z
date available2017-05-08T21:16:08Z
date copyrightJuly 2003
date issued2003
identifier other%28asce%290893-1321%282003%2916%3A3%28100%29.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/44984
description abstractThe 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.
publisherAmerican Society of Civil Engineers
titleFull-Scale Field Evaluation of Microelectromechanical System-Based Biaxial Strain Transducer and Its Application in Fatigue Analysis
typeJournal Paper
journal volume16
journal issue3
journal titleJournal of Aerospace Engineering
identifier doi10.1061/(ASCE)0893-1321(2003)16:3(100)
treeJournal of Aerospace Engineering:;2003:;Volume ( 016 ):;issue: 003
contenttypeFulltext


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