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<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="Picture_x0020_2" src="cid:image001.png@01DBB837.F455B010" alt="Thesis Defense Announcement at the Cullen College of Engineering"></span><span style="font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E">Percussion-Based Single and Multi-Bolt Looseness Detection and Composite Plate Delamination Detection Using Machine Learning<o:p></o:p></span></b></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none"><o:p> </o:p></span></b></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none">Nikolas Austin Reuter</span></b><span style="font-size:11.0pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">April 29, 2025, 10 a.m. to 11 a.m. (CST)<br>
Location: </span><span style="font-size:11.0pt;font-family:"Aptos",sans-serif;color:black"><a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_Yzg5ZjU2NDEtZmFhOS00ZTYyLWE4ZjMtOTAyOThhZmM5OWI5*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*22d7dc0bd2-599d-4bde-bbc2-ff72c9fb1000*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!Fj3eIkhhlIxqw2EGRD9cfAymgen69Qf5feDKyASctaxJvl4a8Ei1wO4Z2zP9pqZBTdft60IRqp3v1OKuyVupu1lCwg0$"><span style="color:#467886">T</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#467886;mso-ligatures:none">eams
Link</span></a></span><u><span style="font-size:11.0pt;font-family:"Aptos",sans-serif;color:#467886"><o:p></o:p></span></u></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Meeting ID: 234 784 269 480 0</span><span style="font-size:11.0pt;font-family:"Aptos",sans-serif"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Passcode: xE9g27os<o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p> </o:p></span></b></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Gangbing Song, Ph.D. </span><span style="font-size:11.0pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Weihang Zhu, Ph.D. | Zheng Chen, Ph.D. | Bradley Davis, Ph.D.</span><span style="font-size:10.5pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Bolted Flanges are commonly used in the energy industry. Bolt looseness is a common cause of leaks. Pipeline leakage monitoring is essential for ensuring safety and
operational efficiency. This study uses a percussion-based method to apply machine learning in the detection of bolt looseness in flanged pipeline connections. A weld neck flange with eight bolts is examined under two test cases. In the first case, the objective
is to identify a single loose bolt among eight, where seven bolts are torqued to 210 ft-lbs, and one remains loose. The second case investigates the ability to determine the number of loose bolts from four possible conditions: 0, 2, 4, or 8 loose bolts. A
hammer is used to strike between two bolts, and the resulting audio is recorded using an iPhone and applied to machine learning algorithms. Various machine learning techniques are employed, including the shallow learning method Support Vector Machine (SVM),
the deep learning method Recurrent Neural Network (RNN), and the clustering method Spectral Clustering.
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">With the use of machine learning becoming more common, so does the need to educate students in applying machine learning to real-world applications. Composite plates
made of carbon fiber and aluminum with a sandwiched layer of closed-cell foam and epoxy are used to simulate carbon fiber delamination for the purpose of educating students in machine learning topics. A grid system is implemented, where both healthy and delaminated
sections are manufactured to evaluate machine learning models for identifying delaminated regions. This study demonstrates the potential of machine learning for structural health monitoring and predictive maintenance in industrial applications.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><img border="0" width="600" height="82" style="width:6.25in;height:.8541in" id="Picture_x0020_1" src="cid:image002.png@01DBB837.F455B010" alt="Engineered For What's Next"></span><span style="font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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