Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on U-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show U-Net capability to extract meaningful features for accurate crack detection and segmentation.
Pre-clinical screening of cemented implant systems could be improved by modeling the longer-term response of the implant/cement/bone construct to cyclic loading. We formulated bone cement with degraded fatigue fracture properties (Sub-cement) such that long-term fatigue could be simulated in short-term cadaver tests. Sub-cement was made by adding a chain-transfer agent to standard polymethylmethacrylate (PMMA) cement. This reduced the molecular weight of the inter-bead matrix without changing reaction-rate or handling characteristics. Static mechanical properties were approximately equivalent to normal cement. Over a physiologically reasonable range of stress-intensity factor, fatigue crack propagation rates for Sub-cement were higher by a factor of 25+/-19. When tested in a simplified 2 1/2-D physical model of a stem-cement-bone system, crack growth from the stem was accelerated by a factor of 100. Sub-cement accelerated both crack initiation and growth rate. Sub-cement is now being evaluated in full stem/cement/femur models.
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