Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50% of the theoretical ultimate load for further analysis.
A Kohonen self-organizing map was utilized to separate each individual data point (hit) into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0% error. A second analysis was performed utilizing a back propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30-100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5% error. Thus, the back propagation neural network produced far better results than the SOM/multiple linear regression, probably because of nonlinearities in the data and unwanted noise.
","abstract_html":"<p>Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50% of the theoretical ultimate load for further analysis.</p> <p>A Kohonen self-organizing map was utilized to separate each individual data point (hit) into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0% error. A second analysis was performed utilizing a back propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30-100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5% error. Thus, the back propagation neural network produced far better results than the SOM/multiple linear regression, probably because of nonlinearities in the data and unwanted noise.</p>","abstract_has_math":false,"creators":["Fatzinger, Edward C., Jr."],"institution":null,"degree_name":"Master of Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Eric v. K. Hill","Frank J. Radosta","Ashok H. Gurjar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2001,"date_issued":"2001-10-01T07:00:00Z","date_published":"2001-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:23Z","subjects":["load","fiberglass","epoxy","I-beams","acoustic emission","Aerospace Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/57","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Eric v. K. Hill","Frank J. Radosta","Ashok H. Gurjar"]},{"key":"dc:creator","label":"Author","values":["Fatzinger, Edward C., Jr."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["load","fiberglass","epoxy","I-beams","acoustic emission","Aerospace Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/57"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50% of the theoretical ultimate load for further analysis.
A Kohonen self-organizing map was utilized to separate each individual data point (hit) into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0% error. A second analysis was performed utilizing a back propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30-100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5% error. Thus, the back propagation neural network produced far better results than the SOM/multiple linear regression, probably because of nonlinearities in the data and unwanted noise.
"]},{"key":"dc:title","label":"Title","values":["Low Proof Load Prediction of Ultimate Strengths of Fiberglass/Epoxy I-Beams Using Acoustic Emission"]}]}],"canonical_facts":{"dc:contributor":["Eric v. K. Hill","Frank J. Radosta","Ashok H. Gurjar"],"dc:creator":["Fatzinger, Edward C., Jr."],"dc:description.abstract":["Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50% of the theoretical ultimate load for further analysis.
A Kohonen self-organizing map was utilized to separate each individual data point (hit) into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0% error. A second analysis was performed utilizing a back propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30-100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5% error. Thus, the back propagation neural network produced far better results than the SOM/multiple linear regression, probably because of nonlinearities in the data and unwanted noise.
"],"dc:identifier":["https://commons.erau.edu/db-theses/57"],"dc:subject":["load","fiberglass","epoxy","I-beams","acoustic emission","Aerospace Engineering"],"dc:title":["Low Proof Load Prediction of Ultimate Strengths of Fiberglass/Epoxy I-Beams Using Acoustic Emission"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:23Z"}