{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:21:35Z","timestamp":1772864495413,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FortisBC and MITACS","award":["IT17226"],"award-info":[{"award-number":["IT17226"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff\u2013Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.<\/jats:p>","DOI":"10.3390\/s22249586","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T05:50:52Z","timestamp":1670392252000},"page":"9586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Abdullah","family":"Zayat","sequence":"first","affiliation":[{"name":"School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-255X","authenticated-orcid":false,"given":"Mohanad","family":"Obeed","sequence":"additional","affiliation":[{"name":"School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada"}]},{"given":"Anas","family":"Chaaban","sequence":"additional","affiliation":[{"name":"School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97426","DOI":"10.1109\/ACCESS.2019.2928487","article-title":"Pipeline Leak Detection Systems and Data Fusion: A Survey","volume":"7","author":"Baroudi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1177\/1475921719837718","article-title":"Inspection and monitoring systems subsea pipelines: A review paper","volume":"19","author":"Ho","year":"2020","journal-title":"Struct. 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