{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140838"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140838","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Assessment of Multiple Third-Party Risk Models Incorporating Uncertainty Quantification and Sensitivity Analysis","abstract":"This study compares three UAS third-party risk (TPR) assessment tools—Quantitative Approach and Departure Risk Assessment (QUADRA), Autonomous Planning and Replanning (AutoPRep), and the Ground Risk Assessment Service Provider (GRASP)—under controlled mission parameters, similar population datasets, and equivalent failure-mode assumptions. Statistical equivalence between QUADRA and AutoPRep is assessed through several means: the two one-sided tests (TOST), null-hypothesis significance testing, effect size (Hedge's g), and post-hoc power analysis results; which show that QUADRA and AutoPRep produce statistically equivalent expected fatality rates when inputs are aligned, while GRASP exhibits non-equivalent behavior due to its difference in population consideration and sampling, discrete lethal crash area structure, and differing treatments of exposure. Uncertainty quantification is integrated into collective risk calculation in QUADRA by calculating gradient-based variance to evaluate sensitivity to stochastic inputs, of which include probability of loss of aircraft, probability of fatality, skid distance, and human height. The analysis demonstrates that although probability of loss drives risk magnitude, overall variance is dominated by uncertainty in probability of fatality and skid distance, clarifying both the equivalence limits and uncertainty drivers of UAS ground-risk assessments.","abstract_html":"This study compares three UAS third-party risk (TPR) assessment tools—Quantitative Approach and Departure Risk Assessment (QUADRA), Autonomous Planning and Replanning (AutoPRep), and the Ground Risk Assessment Service Provider (GRASP)—under controlled mission parameters, similar population datasets, and equivalent failure-mode assumptions. Statistical equivalence between QUADRA and AutoPRep is assessed through several means: the two one-sided tests (TOST), null-hypothesis significance testing, effect size (Hedge's g), and post-hoc power analysis results; which show that QUADRA and AutoPRep produce statistically equivalent expected fatality rates when inputs are aligned, while GRASP exhibits non-equivalent behavior due to its difference in population consideration and sampling, discrete lethal crash area structure, and differing treatments of exposure. Uncertainty quantification is integrated into collective risk calculation in QUADRA by calculating gradient-based variance to evaluate sensitivity to stochastic inputs, of which include probability of loss of aircraft, probability of fatality, skid distance, and human height. The analysis demonstrates that although probability of loss drives risk magnitude, overall variance is dominated by uncertainty in probability of fatality and skid distance, clarifying both the equivalence limits and uncertainty drivers of UAS ground-risk assessments.","abstract_has_math":false,"creators":["Conde, Giacomo"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Aerospace Engineering","degree_department":"Aerospace and Ocean Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Canfield, Robert Arthur"],"committee_members":["Briggs, Robert Clayton","Woolsey, Craig A.","Richards, Nathan"],"year":2026,"date_issued":"2026-01-15","date_published":"2026-01-15","updated_at":"2026-07-22T22:19:35Z","subjects":["Third-Party Risk","Collective Risk","Unmanned Aerial Systems","Path Assessment","Failure Uncertainty Quantification"],"languages":["en"],"rights":["Creative Commons Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45569"],"render_values":[{"text":"vt_gsexam:45569","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140838","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Canfield, Robert Arthur"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Briggs, Robert Clayton","Woolsey, Craig A.","Richards, Nathan"]},{"key":"dc:contributor.department","label":"Department","values":["Aerospace and Ocean Engineering"]},{"key":"dc:creator","label":"Author","values":["Conde, Giacomo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-16T09:00:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-16T09:00:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Third-Party Risk","Collective Risk","Unmanned Aerial Systems","Path Assessment","Failure Uncertainty Quantification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45569"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140838"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study compares three UAS third-party risk (TPR) assessment tools—Quantitative Approach and Departure Risk Assessment (QUADRA), Autonomous Planning and Replanning (AutoPRep), and the Ground Risk Assessment Service Provider (GRASP)—under controlled mission parameters, similar population datasets, and equivalent failure-mode assumptions. Statistical equivalence between QUADRA and AutoPRep is assessed through several means: the two one-sided tests (TOST), null-hypothesis significance testing, effect size (Hedge's g), and post-hoc power analysis results; which show that QUADRA and AutoPRep produce statistically equivalent expected fatality rates when inputs are aligned, while GRASP exhibits non-equivalent behavior due to its difference in population consideration and sampling, discrete lethal crash area structure, and differing treatments of exposure. Uncertainty quantification is integrated into collective risk calculation in QUADRA by calculating gradient-based variance to evaluate sensitivity to stochastic inputs, of which include probability of loss of aircraft, probability of fatality, skid distance, and human height. The analysis demonstrates that although probability of loss drives risk magnitude, overall variance is dominated by uncertainty in probability of fatality and skid distance, clarifying both the equivalence limits and uncertainty drivers of UAS ground-risk assessments."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The increasing use of unmanned aerial systems (UAS) in defense and commercial aviation necessitates reliable methods for quantifying third-party risk to uninvolved populations. This work evaluates three risk assessment tools; QUADRA, AutoPRep, and GRASP by comparing estimated casualties or fatalities per flight hour under equivalent mission conditions. When population data, severity and casualty models, and failure-mode assumptions are aligned, QUADRA and AutoPRep produce statistically equivalent risk estimates, while GRASP exhibits systematic differences attributable to its sparse population sampling, distinct treatment of exposure, and differing lethal crash area assumptions. In order to evaluate the uncertainty of under-defined parameters, such as the probability of aircraft loss, the probability of fatality, skid distance, and human height, as well as their propagation through the collective risk calculation, the study also employs uncertainty quantification and gradient-based sensitivity analysis within the QUADRA framework. The findings underscore the need for uncertainty-aware TPR models in UAS operations by showing that incorporating uncertainty can materially change predicted risk, depending on which inputs are treated as uncertain and how they are characterized."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Assessment of Multiple Third-Party Risk Models Incorporating Uncertainty Quantification and Sensitivity Analysis"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Canfield, Robert Arthur"],"dc:contributor.committeemember":["Briggs, Robert Clayton","Woolsey, Craig A.","Richards, Nathan"],"dc:contributor.department":["Aerospace and Ocean Engineering"],"dc:creator":["Conde, Giacomo"],"dc:date.accessioned":["2026-01-16T09:00:26Z"],"dc:date.available":["2026-01-16T09:00:26Z"],"dc:date.issued":["2026-01-15"],"dc:description.abstract":["This study compares three UAS third-party risk (TPR) assessment tools—Quantitative Approach and Departure Risk Assessment (QUADRA), Autonomous Planning and Replanning (AutoPRep), and the Ground Risk Assessment Service Provider (GRASP)—under controlled mission parameters, similar population datasets, and equivalent failure-mode assumptions. Statistical equivalence between QUADRA and AutoPRep is assessed through several means: the two one-sided tests (TOST), null-hypothesis significance testing, effect size (Hedge's g), and post-hoc power analysis results; which show that QUADRA and AutoPRep produce statistically equivalent expected fatality rates when inputs are aligned, while GRASP exhibits non-equivalent behavior due to its difference in population consideration and sampling, discrete lethal crash area structure, and differing treatments of exposure. Uncertainty quantification is integrated into collective risk calculation in QUADRA by calculating gradient-based variance to evaluate sensitivity to stochastic inputs, of which include probability of loss of aircraft, probability of fatality, skid distance, and human height. The analysis demonstrates that although probability of loss drives risk magnitude, overall variance is dominated by uncertainty in probability of fatality and skid distance, clarifying both the equivalence limits and uncertainty drivers of UAS ground-risk assessments."],"dc:description.abstractgeneral":["The increasing use of unmanned aerial systems (UAS) in defense and commercial aviation necessitates reliable methods for quantifying third-party risk to uninvolved populations. This work evaluates three risk assessment tools; QUADRA, AutoPRep, and GRASP by comparing estimated casualties or fatalities per flight hour under equivalent mission conditions. When population data, severity and casualty models, and failure-mode assumptions are aligned, QUADRA and AutoPRep produce statistically equivalent risk estimates, while GRASP exhibits systematic differences attributable to its sparse population sampling, distinct treatment of exposure, and differing lethal crash area assumptions. In order to evaluate the uncertainty of under-defined parameters, such as the probability of aircraft loss, the probability of fatality, skid distance, and human height, as well as their propagation through the collective risk calculation, the study also employs uncertainty quantification and gradient-based sensitivity analysis within the QUADRA framework. The findings underscore the need for uncertainty-aware TPR models in UAS operations by showing that incorporating uncertainty can materially change predicted risk, depending on which inputs are treated as uncertain and how they are characterized."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45569"],"dc:identifier.uri":["https://hdl.handle.net/10919/140838"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Third-Party Risk","Collective Risk","Unmanned Aerial Systems","Path Assessment","Failure Uncertainty Quantification"],"dc:title":["Assessment of Multiple Third-Party Risk Models Incorporating Uncertainty Quantification and Sensitivity Analysis"],"dc:type":["Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:35Z"}