{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:20:21Z","timestamp":1776385221870,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52178115"],"award-info":[{"award-number":["52178115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%).<\/jats:p>","DOI":"10.3390\/s23083887","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T02:08:11Z","timestamp":1681265291000},"page":"3887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm\u2014A Comprehensive Numerical Study"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-1187","authenticated-orcid":false,"given":"Wael A.","family":"Altabey","sequence":"first","affiliation":[{"name":"International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China"},{"name":"Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt"}]},{"given":"Zhishen","family":"Wu","sequence":"additional","affiliation":[{"name":"International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-5194","authenticated-orcid":false,"given":"Mohammad","family":"Noori","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA"},{"name":"School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1146-8384","authenticated-orcid":false,"given":"Hamed","family":"Fathnejat","sequence":"additional","affiliation":[{"name":"Basque Center for Applied Mathematics, 48001 Bilbao, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"ref_1","unstructured":"Murad, M. (2010). 4th European-American Workshop on Reliability of NDE-Th.2.A.1, NDT.net."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s00477-008-0259-x","article-title":"The Selection of Corrosion prior Distributions for Risk Based-Integriy Modeling","volume":"23","author":"Thodi","year":"2009","journal-title":"Stoch. Environ. Res. Risk Assess"},{"key":"ref_3","unstructured":"Papavinasam, S., Revie, R., Attard, M., Demoz, A., and Michaelian, K. (2002, January 7\u201311). Comparison of Techniques for Monitoring Corrosion Inhibitors in Oil and Gas Pipelines. Proceedings of the CORROSION\/2002, Denver, CO, USA."},{"key":"ref_4","unstructured":"Sinha, D. (2005). Ultrasonic Sensor for Pipeline Monitoring Technology Report; Gas Technology Management Division Strategic Center for Natural Gas and Oil National Energy Technology Laboratory, LA-UR-05-6025."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jawhar, I., Mohamed, N., Mohamed, M., and Aziz, J. (2008, January 5\u20137). A routing protocol and addressing scheme for oil, gas, and water pipeline monitoring using wireless sensor networks. Proceedings of the 2008 5th IFIP International Conference on Wireless and Optical Communications Networks (WOCN \u201908), Surabaya, Indonesia.","DOI":"10.1109\/WOCN.2008.4542530"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ceravolo, R., Civera, M., Lenticchia, E., Miraglia, G., and Surace, C. (2021). Detection and Localization of Multiple Damages through Entropy in Information Theory. Appl. Sci., 11.","DOI":"10.3390\/app11135773"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105963","DOI":"10.1016\/j.engappai.2023.105963","article-title":"A deep-learning approach for predicting water absorption in composite pipes by extracting the material\u2019s dielectric features","volume":"121","author":"Altabey","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Altabey, W.A., Noori, M., Wang, T., Ghiasi, R., Kuok, S., and Wu, Z. (2021). Deep learning-based crack identification for steel pipelines by extracting features from 3d shadow modeling. Appl. Sci., 11.","DOI":"10.3390\/app11136063"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Altabey, W.A., Kouritem, S.A., Abouheaf, M.I., and Nahas, N. (2022, January 16\u201318). Research in Image Processing for Pipeline Crack Detection Applications. Proceedings of the 2nd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME-2022), Male, Maldives.","DOI":"10.1109\/ICECCME55909.2022.9988417"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Altabey, W.A., Kouritem, S.A., Abouheaf, M.I., and Nahas, N. (2022, January 16\u201318). A Deep Learning-Based Approach for Pipeline Cracks Monitoring. Proceedings of the 2nd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME-2022), Male, Maldives.","DOI":"10.1109\/ICECCME55909.2022.9987998"},{"key":"ref_11","unstructured":"Morison, D. (2008, January 16\u201318). Remote Monitoring of Pipeline Corrosion Using Fiber Optic Sensors. Proceedings of the NACE International Conference Corrosion, New Orleans, LA, USA. Paper No. 08290."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1177\/05831024030356001","article-title":"Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward","volume":"35","author":"Park","year":"2003","journal-title":"Shock Vib. Dig."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Stoianov, I., Nachman, L., and Madden, S. (2007, January 25\u201327). A Wireless Sensor Network for Pipeline Monitoring. Proceedings of the 2007 6th International Symposium on Information Processing in Sensor Networks IPSN\u201907, Cambridge, MA, USA.","DOI":"10.1145\/1236360.1236396"},{"key":"ref_14","first-page":"33","article-title":"Health Monitoring of Pipeline Systems using Macro-fiber Composite Active-Sensors","volume":"7","author":"Thien","year":"2007","journal-title":"Steel Struct."},{"key":"ref_15","unstructured":"Jin, Y., and Eydgahi, A. (2008, January 17\u201319). Monitoring of Distributed Pipeline Systems by Wireless Sensor Networks. Proceedings of the 2008 IAJC-IJME International Conference, Nashville, TN, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, Y., Kong, F., Zhu, D., Tosun, A., and Deng, Q. (2010, January 13\u201315). Sensor Placement for Lifetime Maximization in Monitoring Oil Pipelines. Proceedings of the ICCPS \u201910, Stockholm, Sweden.","DOI":"10.1145\/1795194.1795204"},{"key":"ref_17","first-page":"012104","article-title":"Low Cost Impedance Monitoring Using Smart Materials","volume":"429","author":"Peairs","year":"2010","journal-title":"Mater. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lopes Junior, V., Steffen, V., and Savi, M. (2016). Dynamics of Smart Systems and Structures, Springer.","DOI":"10.1007\/978-3-319-29982-2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nikles, M., and Briffod, F. (2005, January 12\u201317). Greatly Extended Distance Pipeline Monitoring Using Fibre Optics. Proceedings of the 24th International Conference on Offshore Mechanics and Arctic Engineering, Halkidiki, Greece.","DOI":"10.1115\/OMAE2005-67369"},{"key":"ref_20","unstructured":"Inaudi, D., and Glisic, B. (2007, January 13\u201316). Distributed Fibre-Optic Sensing for Long-Range Monitoring of Pipelines. Proceedings of the 3rd International Conference on Structural Health Monitoring of Intelligent Infrastructure, Vancouver, BC, Canada."},{"key":"ref_21","unstructured":"Meinert, D., Gorny, M., Pollmann, A., Chen, J., and Garbi, A. (October, January 29). Monitoring Ultrasonic Noise in Steel Pipeline. Proceedings of the 7th International Pipeline Conference, Calgary, AB, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.yofte.2010.01.001","article-title":"Performance enhancement of BOTDR fiber optic sensor for oil and gas pipeline monitoring","volume":"16","author":"Yan","year":"2010","journal-title":"Opt. Fiber Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1177\/1475921710361328","article-title":"Development and performance evaluation of non-slippage optical fiber as Brillouin scattering-based distributed sensors","volume":"9","author":"Wu","year":"2010","journal-title":"Struct. Health Monit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y.H., Xiao, X.L., Yan, H., Shi, G.S., and Wang, Q.R. (2008, January 6\u20138). A fiber-sensor-based long-distance safety monitoring system for buried oil pipeline. Proceedings of the IEEE International Conference on Networking, Sensing and Control, Sanya, China.","DOI":"10.1109\/ICNSC.2008.4525259"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115311","DOI":"10.1016\/j.engstruct.2022.115311","article-title":"A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations","volume":"276","author":"Fathnejat","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Silik, A., Noori, M., Ghiasi, R., Wang, T., Kuok, S., Farhan, N.S.D., Dang, J., Wu, Z., and Altabey, W.A. (2023). Dynamic wavelet neural network model for damage features extraction and patterns recognition. Civ. Struct. Health Monit.","DOI":"10.1007\/s13349-023-00683-8"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s11803-021-2049-0","article-title":"Reaching Law Based Sliding Mode Control for a Frame Structure under Seismic Load","volume":"20","author":"Zhao","year":"2021","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, H., Sun, Z., Qian, Y., Zhang, T., and Rao, Y. (2015, January 1). A hydrostatic leak test for water pipeline by using distributed optical fiber vibration sensing system. Proceedings of the Fifth Asia-Pacific Optical Sensors Conference, International Society for Optics and Photonics, Jeju, Republic of Korea.","DOI":"10.1117\/12.2185184"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"085023","DOI":"10.1088\/1361-665X\/aacc99","article-title":"Fatigue Damage Identification for Composite Pipeline Systems Using Electrical Capacitance Sensors","volume":"27","author":"Zhao","year":"2018","journal-title":"Smart Mater. Struct."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1177\/0967391120921701","article-title":"Nondestructive health monitoring techniques for composite materials: A review","volume":"29","author":"Senthilkumar","year":"2021","journal-title":"Polym. Polym. Compos."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.compstruct.2006.02.013","article-title":"An energy- based fatigue damage parameter for off-axis unidirectional fibre reinforced composites","volume":"79","author":"Haftchenari","year":"2007","journal-title":"Compos. Struct."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Noori, M., Altabey, W.A., Ghiasi, R., and Zhishen, W. (2018). Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations. Appl. Sci., 8.","DOI":"10.3390\/app8122564"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Altabey, W.A., Noori, M., Wu, Z., Al-Moghazy, M.A., and Kouritem, S.A. (2022). Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm. Materials, 15.","DOI":"10.3390\/ma15228071"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115576","DOI":"10.1016\/j.engstruct.2022.115576","article-title":"Seismic response prediction of structures based on Runge-Kutta recurrent neural network with prior knowledge","volume":"279","author":"Wang","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Altabey, W.A., and Noori, M. (2022). Artificial-Intelligence-Based Methods for Structural Health Monitoring. Appl. Sci., 12.","DOI":"10.3390\/app122412726"},{"key":"ref_36","unstructured":"Silik, A., Noori, M., Altabey, W.A., Ji, D., and Ghiasi, R. (2022). Lifeline 2022: Advancing Lifeline Engineering for Community Resilience, ASCE Library."},{"key":"ref_37","unstructured":"Ghiasi, R., Noori, M., Altabey, W.A., Wang, T., and Wu, Z. (2022). Lifeline 2022: Advancing Lifeline Engineering for Community Resilience, ASCE Library."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"683374","DOI":"10.3389\/fmats.2021.683374","article-title":"Dynamic Performance Detection of CFRP Composite Pipes based on Quasi-Distributed Optical Fiber Sensing Techniques","volume":"8","author":"Wang","year":"2021","journal-title":"Front. Mater."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3887\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:13:55Z","timestamp":1760123635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,11]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23083887"],"URL":"https:\/\/doi.org\/10.3390\/s23083887","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,11]]}}}