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Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%. [ABSTRACT FROM AUTHOR]

Bridge wear and deterioration occur over time under typical operations. Inspections can be resource intensive, infrequent, and sometimes require bridge closure. Instrumented passing vehicles may be used to record vibration responses and extract damage‐sensitive bridge frequency response characteristics. This paper presents a methodology for extracting bridge frequencies from crowdsourced dynamic response data recorded within passing vehicles. Detected bridge frequency changes and a novel point cloud methodology are used to identify critical damage scenarios and estimate remaining life. The use of a novel crowdsourced Welch methodology allows for spectra averaging that reduces the influence of noise and transient events. Numerical and experimental studies illustrate that bridge frequencies can be accurately estimated and that changes in bridge frequency caused by different damage scenarios can be characterized. Numerical examples for the damage scenario analysis, including estimation of remaining life, are provided for two bridge types and three specific damage scenarios. [ABSTRACT FROM AUTHOR]

Congenital stationary night blindness (CSNB) is an ocular disorder characterized by nyctalopia. An autosomal recessive missense mutation in glutamate metabotropic receptor 6 (GRM6 c.533C>T, p.(Thr178Met)), called CSNB2, was previously identified in one Tennessee Walking Horse and predicted to reduce binding affinity of the neurotransmitter glutamate, impacting the retinal rod ON‐bipolar cell signaling pathway. Thus, the first aim was to identify the allele frequency (AF) of CSNB2 in breeds with reported cases of CSNB and breeds closely related to the Tennessee Walking Horse. The second aim was to perform ocular examinations in multiple breeds to confirm the link between genotype and CSNB phenotype. In evaluating 3518 horses from 14 breeds, the CSNB2 allele was identified in nine previously unreported breeds. The estimated AF was highest in pacing Standardbreds (0.17) and lowest in American Quarter Horses (0.0010). Complete ophthalmic examinations and electroretinograms (ERG) were performed on 19 horses from three breeds, including one CSNB2 homozygote from each breed. All three CSNB2/CSNB2 horses had an electronegative ERG waveform under scotopic light conditions consistent with CSNB. The remaining 16 horses (seven CSNB2/N and nine N/N) had normal scotopic ERG results. All horses had normal photopic ERGs. This study provides additional evidence that GRM6 c.533C>T homozygosity is likely causal to CSNB in Tennessee Walking Horses, Standardbreds, and Missouri Fox Trotting Horses. Genetic testing is recommended for breeds with the CSNB2 allele to limit the production of affected horses. This study represents the largest across‐breed identification of CSNB in the horse and suggests that this disorder is likely underdiagnosed. [ABSTRACT FROM AUTHOR]