Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.
","abstract_html":"<p>Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.</p>","abstract_has_math":false,"creators":["Shi, Arthur Q.X."],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Helen Yu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-12-01T08:00:00Z","date_published":"2015-12-01T08:00:00Z","updated_at":"2026-07-24T01:31:35Z","subjects":["Structural Health Monitoring","Wavelet Decomposition","Artificial Neural Network","Negative Selection","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2015.152"],"render_values":[{"text":"10.15368/theses.2015.152","href":"https://doi.org/10.15368/theses.2015.152","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1499","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Helen Yu"]},{"key":"dc:creator","label":"Author","values":["Shi, Arthur Q.X."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-12-08T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Structural Health Monitoring","Wavelet Decomposition","Artificial Neural Network","Negative Selection","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1499","10.15368/theses.2015.152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.
"]},{"key":"dc:title","label":"Title","values":["Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems"]}]}],"canonical_facts":{"dc:contributor":["Helen Yu"],"dc:creator":["Shi, Arthur Q.X."],"dc:date.available":["2018-12-08T08:00:00Z"],"dc:description.abstract":["Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1499","10.15368/theses.2015.152"],"dc:subject":["Structural Health Monitoring","Wavelet Decomposition","Artificial Neural Network","Negative Selection","Electrical and Computer Engineering"],"dc:title":["Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:31:35Z"}