Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks. Linear accelerometer, angular accelerometer, rate gyro, and strain gage signals were collected in flight using DAQBook portable data acquisition system for dutch-roll, roll, sideslip left, sideslip right, stabilized g turn left, stabilized g turn right, and push-pull maneuvers at airspeeds of 65 KIAS, 80 KIAS, and 95 KIAS. The sensor signals were filtered and used to train the neural networks. Modular Neural Networks were used to predict the strains. The horizontal tail neural network was trained with CGNz and x-, y-, and z-axis angular accelerometer signals and predicted 93% of all strains to within 50 :, of the measured value. The vertical tail neural network predicted 100% of all strains to within 50 :, of the measured value.
","abstract_html":"<p>Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks. Linear accelerometer, angular accelerometer, rate gyro, and strain gage signals were collected in flight using DAQBook portable data acquisition system for dutch-roll, roll, sideslip left, sideslip right, stabilized g turn left, stabilized g turn right, and push-pull maneuvers at airspeeds of 65 KIAS, 80 KIAS, and 95 KIAS. The sensor signals were filtered and used to train the neural networks. Modular Neural Networks were used to predict the strains. The horizontal tail neural network was trained with CGNz and x-, y-, and z-axis angular accelerometer signals and predicted 93% of all strains to within 50 :, of the measured value. The vertical tail neural network predicted 100% of all strains to within 50 :, of the measured value.</p>","abstract_has_math":false,"creators":["Marciniak, Maciej"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Graduate Studies","degree_department":null,"school":null,"contributors":["David Kim","Eric v. K. Hill","James G. Ladesic"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1996,"date_issued":"1996-10-01T07:00:00Z","date_published":"1996-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:23Z","subjects":["Backpropagation","neural networks","empennage","general aviation","Aerospace Engineering","Aviation"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/260","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David Kim","Eric v. K. Hill","James G. Ladesic"]},{"key":"dc:creator","label":"Author","values":["Marciniak, Maciej"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Graduate Studies"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Backpropagation","neural networks","empennage","general aviation","Aerospace Engineering","Aviation"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/260"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks. Linear accelerometer, angular accelerometer, rate gyro, and strain gage signals were collected in flight using DAQBook portable data acquisition system for dutch-roll, roll, sideslip left, sideslip right, stabilized g turn left, stabilized g turn right, and push-pull maneuvers at airspeeds of 65 KIAS, 80 KIAS, and 95 KIAS. The sensor signals were filtered and used to train the neural networks. Modular Neural Networks were used to predict the strains. The horizontal tail neural network was trained with CGNz and x-, y-, and z-axis angular accelerometer signals and predicted 93% of all strains to within 50 :, of the measured value. The vertical tail neural network predicted 100% of all strains to within 50 :, of the measured value.
"]},{"key":"dc:title","label":"Title","values":["A Methodology for the Prediction of the Empennage In-Flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks"]}]}],"canonical_facts":{"dc:contributor":["David Kim","Eric v. K. Hill","James G. Ladesic"],"dc:creator":["Marciniak, Maciej"],"dc:description.abstract":["Backpropagation neural networks have been used to predict strain resulting from the maneuver in-flight loads in the empennage structure of a Cessna 172P. The purpose of this research was to develop a methodology for the prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks. Linear accelerometer, angular accelerometer, rate gyro, and strain gage signals were collected in flight using DAQBook portable data acquisition system for dutch-roll, roll, sideslip left, sideslip right, stabilized g turn left, stabilized g turn right, and push-pull maneuvers at airspeeds of 65 KIAS, 80 KIAS, and 95 KIAS. The sensor signals were filtered and used to train the neural networks. Modular Neural Networks were used to predict the strains. The horizontal tail neural network was trained with CGNz and x-, y-, and z-axis angular accelerometer signals and predicted 93% of all strains to within 50 :, of the measured value. The vertical tail neural network predicted 100% of all strains to within 50 :, of the measured value.
"],"dc:identifier":["https://commons.erau.edu/db-theses/260"],"dc:subject":["Backpropagation","neural networks","empennage","general aviation","Aerospace Engineering","Aviation"],"dc:title":["A Methodology for the Prediction of the Empennage In-Flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks"],"thesis:degree_discipline":["Graduate Studies"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:23Z"}