{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/43980"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/43980","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Active damage control using artificial intelligence: initial studies into identification and mitigation","abstract":"This thesis presents an initial investigation into Active Damage Control (AD C) using Artificial Intelligence (AI). AI can alleviate the sometimes complicated task of modelling the system and also provides an adaptable solution process. The two research areas of ADC, damage identification and damage control, are studied in separate investigations. An AI technique called \"rule induction\" is used for the damage identification study. Velocity data from three plates (one without damage, one with damage at the center, and one with damage at the edge) are acquired using a laser data acquisition system. A set of rules is then induced from these data which accurately identifies which plates have damage and where the damage is located. With regard to the damage control, a real-time, machine-learning technique called \"BOXES\" is used to locally control the vibration of various systems by identifying their vibrational patterns. Using this technique, it is shown that the computer successfully learns an effective control law for various simulations using its trials and failures as the only learning information. It is also seen that the learning algorithm is somewhat less effective when experimentally applying this method to a pin-pin, aluminum beam. A discussion of possible improvements are presented in the future work section.","abstract_html":"This thesis presents an initial investigation into Active Damage Control (AD C) using Artificial Intelligence (AI). AI can alleviate the sometimes complicated task of modelling the system and also provides an adaptable solution process. The two research areas of ADC, damage identification and damage control, are studied in separate investigations. An AI technique called "rule induction" is used for the damage identification study. Velocity data from three plates (one without damage, one with damage at the center, and one with damage at the edge) are acquired using a laser data acquisition system. A set of rules is then induced from these data which accurately identifies which plates have damage and where the damage is located. With regard to the damage control, a real-time, machine-learning technique called "BOXES" is used to locally control the vibration of various systems by identifying their vibrational patterns. Using this technique, it is shown that the computer successfully learns an effective control law for various simulations using its trials and failures as the only learning information. It is also seen that the learning algorithm is somewhat less effective when experimentally applying this method to a pin-pin, aluminum beam. 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Using this technique, it is shown that the computer successfully learns an effective control law for various simulations using its trials and failures as the only learning information. It is also seen that the learning algorithm is somewhat less effective when experimentally applying this method to a pin-pin, aluminum beam. 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Using this technique, it is shown that the computer successfully learns an effective control law for various simulations using its trials and failures as the only learning information. It is also seen that the learning algorithm is somewhat less effective when experimentally applying this method to a pin-pin, aluminum beam. 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