This dissertation challenges the US Army’s reliance on a single readiness metric to manage its aviation fleet. Using daily operational data from AH-64 Apache helicopters, it develops a framework that interprets readiness and utilization as joint outcomes of a coupled system rather than independent objectives. Three studies characterize how units actually make flying decisions, identify peer-based pathways toward more efficient operations, and determine when investment in predictive maintenance technology stops yielding meaningful returns.
Available in the NC State University Libraries repository.
Thomas DM, Collins E, Hood K, Redman LM, Falkenhain K, Berry S, Bermingham K, Lee A, Mastroberardino A, Semmel AD, et al. “GVC-Calc enables large-scale analysis of continuous glucose monitoring data.” Nature Metabolism, 2026. doi:10.1038/s42255-026-01611-y · Code
Semmel AD, Heese HS, McConnell BM. “Evaluating the implementation of operational readiness and maintenance policies in US Army aviation.” Journal of Defense Modeling and Simulation, 2025. doi:10.1177/15485129251328044
Van Dam D, Ferreira S, Semmel AD, Morogiello J, Gidaro R. “Assessing Inter-Rater Reliability and Intra-Rater Reliability During the 2-Minute Hand-Release Push-Up Event in the Army Fitness Test.” Journal of Strength and Conditioning Research, forthcoming.
grassr: Context-Conditioned Reporting for Binary Rater Reliability — R package on CRAN, version 0.7.4, published July 2026. With Rachel Gidaro. Generates a report card for rater reliability on binary outcomes, positioning each agreement coefficient on a reference surface calibrated to the study’s rater count, sample size, and prevalence. doi:10.32614/CRAN.package.grassr · Documentation · GitHub