Date of Award
Master of Science in Engineering (MSE)
Open-Deck (OD) steel truss railroad bridges are one of the most common types of railroad bridges in the United States. They are, however, significantly vulnerable to the dynamic effects of moving train load. In the long run, these dynamic effects cause fatigue damage to the structural members. The damage in most of the railroad bridges are monitored by visual inspections which are sometimes unreliable and inconsistent due to human error. This study explores the identification and classification of damage to an open deck railroad bridge from both time-domain (statistical features) and time-frequency domain features (Hilbert-Huang Transform) extracted through acceleration response using Machine Learning Classifier in MATLAB R2022a. A 3D finite element model of the bridge was created in SAP2000 and validated with the field-testing data. The changes in acceleration time-history responses obtained under the different damage cases are utilized to detect damage using two Machine Learning Classifiers: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. The extracted features are used to train and cross validate the algorithm and indicated high level of precision (more than 95%) in identifying and classifying the damage in the bridge and more than 85% in identifying defect in the rail.
Pokhrel, Mahesh, "A Data-Driven Method for Damage Detection in an Open Deck Steel Truss Railroad Bridge Under the Moving Train Load" (2023). Theses and Dissertations - UTRGV. 1277.
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