{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T20:59:34Z","timestamp":1782593974920,"version":"3.54.5"},"reference-count":63,"publisher":"American Society of Civil Engineers (ASCE)","issue":"6","content-domain":{"domain":["ascelibrary.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Civ. Eng."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1061\/jccee5.cpeng-6119","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T16:50:52Z","timestamp":1753980652000},"update-policy":"https:\/\/doi.org\/10.1061\/do.news.20190416.0001","source":"Crossref","is-referenced-by-count":1,"title":["Explainable Machine-Learning Leak Identification Framework for Water Distribution Networks"],"prefix":"10.1061","volume":"39","author":[{"given":"Rongsheng","family":"Liu","sequence":"first","affiliation":[{"name":"Hong Kong Polytechnic Univ.","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek","family":"Zayed","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ.","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ.","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"30","reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.08.024"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140305595"},{"key":"e_1_3_4_4_1","first-page":"200233","article-title":"Using feature maps to unpack the CNN \u2018Black box\u2019 theory with two medical datasets of different modality","volume":"18","author":"Azam S.","year":"2023","unstructured":"Azam, S., S. Montaha, K. U. Fahim, A. R. H. Rafid, M. S. H. Mukta, and M. Jonkman. 2023. \u201cUsing feature maps to unpack the CNN \u2018Black box\u2019 theory with two medical datasets of different modality.\u201d Intell. Syst. Appl. 18 (May): 200233. https:\/\/doi.org\/10.1016\/j.iswa.2023.200233.","journal-title":"Intell. Syst. Appl."},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000881"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2023.109798"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106787"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"e_1_3_4_9_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40713-017-0007-9","article-title":"Collective thinking approach for improving leak detection systems","volume":"2","author":"El-Zahab S.","year":"2017","unstructured":"El-Zahab, S., A. Asaad, E. Mohammed Abdelkader, and T. Zayed. 2017. \u201cCollective thinking approach for improving leak detection systems.\u201d Smart Water 2 (Dec): 3. https:\/\/doi.org\/10.1186\/s40713-017-0007-9.","journal-title":"Smart Water"},{"key":"e_1_3_4_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.12.018"},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)WR.1943-5452.0001317"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101187"},{"key":"e_1_3_4_14_1","doi-asserted-by":"crossref","unstructured":"He K. X. Zhang S. Ren and J. Sun. 2016. \u201cDeep residual learning for image recognition.\u201d In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 770\u2013778. New York: IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2023.109492"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.107994"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.088"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)PS.1949-1204.0000646"},{"key":"e_1_3_4_19_1","unstructured":"Krizhevsky A. I. Sutskever and G. E. Hinton. 2012. \u201cImageNet classification with deep convolutional neural networks.\u201d In Vol.\u00a025 of Proc. Advances in Neural Information Processing Systems. San Diego: Neural Information Processing Systems Foundation."},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.06.055"},{"issue":"1","key":"e_1_3_4_22_1","first-page":"153","article-title":"Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis","volume":"19","author":"Li S.","year":"2009","unstructured":"Li, S., T. Li, C. Sun, R. Yan, and X. Chen. 2009. \u201cMultilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis.\u201d Digital Signal Process. 19 (1): 153\u2013183. https:\/\/doi.org\/10.1016\/j.jmsy.2023.05.027.","journal-title":"Digital Signal Process."},{"key":"e_1_3_4_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.08.042"},{"key":"e_1_3_4_24_1","doi-asserted-by":"crossref","unstructured":"Lim J. 2014. \u201cUnderground pipeline leak detection using acoustic emission and crest factor technique.\u201d In Proc. Advances in Acoustic Emission Technology: Proc. of the World Conf. on Acoustic Emission\u20132013 445\u2013450. New York: Springer.","DOI":"10.1007\/978-1-4939-1239-1_41"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110235"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.101963"},{"key":"e_1_3_4_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101687"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.02.011"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)PS.1949-1204.0000287"},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110710"},{"key":"e_1_3_4_31_1","doi-asserted-by":"publisher","DOI":"10.2166\/ws.2021.109"},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.020"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2014.02.157"},{"key":"e_1_3_4_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.101877"},{"key":"e_1_3_4_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510413"},{"key":"e_1_3_4_36_1","doi-asserted-by":"crossref","unstructured":"Scherer D. A. M\u00fcller and S. Behnke. 2010. \u201cEvaluation of pooling operations in convolutional architectures for object recognition.\u201d In Proc. Int. Conf. on Artificial Neural Networks\u2014ICANN 2010. Lecture Notes in Computer Science edited by K. Diamantaras W. Duch and L. S. Iliadis 92\u2013101. Berlin: Springer.","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"e_1_3_4_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2007.12.004"},{"key":"e_1_3_4_38_1","doi-asserted-by":"crossref","unstructured":"Selvaraju R. R. M. Cogswell A. Das R. Vedantam D. Parikh and D. Batra. 2017. \u201cGrad-CAM: Visual explanations from deep networks via gradient-based localization.\u201d In Proc. IEEE Int. Conf. on Computer Vision 618\u2013626. New York: IEEE.","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_4_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108354"},{"key":"e_1_3_4_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103256"},{"key":"e_1_3_4_41_1","unstructured":"Simonyan K. and A. Zisserman. 2014. \u201cVery deep convolutional networks for large-scale image recognition.\u201d Preprint submitted September 4 2014. https:\/\/arxiv.org\/abs\/1409.1556v6."},{"key":"e_1_3_4_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2690805"},{"key":"e_1_3_4_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102063"},{"key":"e_1_3_4_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119162"},{"key":"e_1_3_4_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.151110"},{"key":"e_1_3_4_46_1","doi-asserted-by":"crossref","unstructured":"Terao Y. and A. Mita. 2008. \u201cRobust water leakage detection approach using the sound signals and pattern recognition.\u201d In Vol. 6932 of Proc. Sensors and Smart Structures Technologies for Civil Mechanical and Aerospace Systems 2008. Bellingham WA: International Society for Optics and Photonics.","DOI":"10.1117\/12.775968"},{"key":"e_1_3_4_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110611"},{"key":"e_1_3_4_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106062"},{"key":"e_1_3_4_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2022.100557"},{"key":"e_1_3_4_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2021.104755"},{"key":"e_1_3_4_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112691"},{"key":"e_1_3_4_52_1","doi-asserted-by":"crossref","unstructured":"Wang Z. Z. Dai B. P\u00f3czos and J. Carbonell. 2019. \u201cCharacterizing and avoiding negative transfer.\u201d In Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition 11293\u201311302. New York: IEEE.","DOI":"10.1109\/CVPR.2019.01155"},{"key":"e_1_3_4_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102819"},{"key":"e_1_3_4_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04097-w"},{"key":"e_1_3_4_55_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)PS.1949-1204.0000619"},{"key":"e_1_3_4_56_1","doi-asserted-by":"publisher","DOI":"10.1177\/1478422X241278895"},{"key":"e_1_3_4_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2024.105958"},{"key":"e_1_3_4_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfailanal.2024.108266"},{"key":"e_1_3_4_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2021.07.024"},{"key":"e_1_3_4_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2024.123076"},{"key":"e_1_3_4_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2021.113543"},{"key":"e_1_3_4_62_1","doi-asserted-by":"crossref","unstructured":"Zhang Q. Y. N. Wu and S.-C. Zhu. 2018. \u201cInterpretable convolutional neural networks.\u201d In Proc. 2018 IEEE\/CVF Conf. on Computer Vision and Pattern Recognition 8827\u20138836. New York: IEEE.","DOI":"10.1109\/CVPR.2018.00920"},{"key":"e_1_3_4_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"e_1_3_4_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2548241"}],"container-title":["Journal of Computing in Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascelibrary.org\/doi\/pdf\/10.1061\/JCCEE5.CPENG-6119","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T16:50:55Z","timestamp":1753980655000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascelibrary.org\/doi\/10.1061\/JCCEE5.CPENG-6119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1061\/JCCEE5.CPENG-6119"],"URL":"https:\/\/doi.org\/10.1061\/jccee5.cpeng-6119","relation":{},"ISSN":["0887-3801","1943-5487"],"issn-type":[{"value":"0887-3801","type":"print"},{"value":"1943-5487","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"2024-04-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"04025090"}}