{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:55:30Z","timestamp":1769208930947,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,26]]},"DOI":"10.1145\/3624288.3624289","type":"proceedings-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T17:04:04Z","timestamp":1702573444000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Generalized additive model (GAM) based corrosion growth prediction model using mass in-line inspection (ILI) data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9879-2170","authenticated-orcid":false,"given":"Hewei","family":"Zhang","sequence":"first","affiliation":[{"name":"Space Engineering University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9972-387X","authenticated-orcid":false,"given":"Longlong","family":"Yang","sequence":"additional","affiliation":[{"name":"Universial Translink (Beijing) Supply Chain, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"American Society of Mechanical Engineers. 2016. ASME. B31.8S: Managing System Integrity of Gas Pipelines. American National Standard.  American Society of Mechanical Engineers. 2016. ASME. B31.8S: Managing System Integrity of Gas Pipelines. American National Standard."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-013-1174-0"},{"key":"e_1_3_2_1_3_1","volume-title":"Bayesian reasoning and machine learning","author":"Barber D.","unstructured":"Barber , D. 2012. Bayesian reasoning and machine learning . Cambridge University Press . Barber, D. 2012. Bayesian reasoning and machine learning. Cambridge University Press."},{"key":"e_1_3_2_1_4_1","volume-title":"Bill C-46: An Act to amend the National Energy Board Act and the Canada Oil and Gas Operations Act","author":"Becklumb P.","unstructured":"Becklumb , P. , & Zakzouk , M. 2015. Bill C-46: An Act to amend the National Energy Board Act and the Canada Oil and Gas Operations Act . Library of Parliament = Biblioth\u00e8que du Parlement. Becklumb, P., & Zakzouk, M. 2015. Bill C-46: An Act to amend the National Energy Board Act and the Canada Oil and Gas Operations Act. Library of Parliament= Biblioth\u00e8que du Parlement."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/1234714.1234717"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0951-8320(99)00045-9"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.engfailanal.2016.03.013","article-title":"Case analysis of catastrophic underground pipeline gas explosion in Taiwan","volume":"65","author":"Chen C. H.","year":"2016","unstructured":"Chen , C. H. , Sheen , Y. N. , & Wang , H. Y. 2016 . Case analysis of catastrophic underground pipeline gas explosion in Taiwan . Engineering Failure Analysis , 65 , 39 - 47 . Chen, C. H., Sheen, Y. N., & Wang, H. Y. 2016. Case analysis of catastrophic underground pipeline gas explosion in Taiwan. Engineering Failure Analysis, 65, 39-47.","journal-title":"Engineering Failure Analysis"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.agsy.2019.02.009","article-title":"A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool","volume":"173","author":"Chen K.","year":"2019","unstructured":"Chen , K. , O'Leary , R. A. , & Evans , F. H. 2019 . A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool . Agricultural Systems , 173 , 140 - 150 . Chen, K., O'Leary, R. A., & Evans, F. H. 2019. A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool. Agricultural Systems, 173, 140-150.","journal-title":"Agricultural Systems"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ress.2018.07.012","article-title":"Stochastic corrosion growth modeling for pipelines using mass inspection data","volume":"180","author":"Dann M. R.","year":"2018","unstructured":"Dann , M. R. , & Maes , M. A. 2018 . Stochastic corrosion growth modeling for pipelines using mass inspection data . Reliability Engineering & System Safety , 180 , 245 - 254 . Dann, M. R., & Maes, M. A. 2018. Stochastic corrosion growth modeling for pipelines using mass inspection data. Reliability Engineering & System Safety, 180, 245-254.","journal-title":"Reliability Engineering & System Safety"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Djeundje V. B. & Crook J. 2019. Identifying hidden patterns in credit risk survival data using Generalised Additive Models. European Journal of Operational Research.  Djeundje V. B. & Crook J. 2019. Identifying hidden patterns in credit risk survival data using Generalised Additive Models. European Journal of Operational Research.","DOI":"10.1016\/j.ejor.2019.02.006"},{"key":"e_1_3_2_1_12_1","unstructured":"Eckert R. B. 2017.Internal Corrosion Failures: Are We Learning from the Past? Retrieved from [http:\/\/www.materialsperformance.com\/articles\/chemical-treatment\/2017\/01\/ internal-corrosion-failures-are-we-learning-from-the-past]  Eckert R. B. 2017.Internal Corrosion Failures: Are We Learning from the Past? Retrieved from [http:\/\/www.materialsperformance.com\/articles\/chemical-treatment\/2017\/01\/ internal-corrosion-failures-are-we-learning-from-the-past]"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000067"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2018.11.044"},{"key":"e_1_3_2_1_15_1","volume-title":"Markov chain Monte Carlo: stochastic simulation for Bayesian inference","author":"Gamerman D.","unstructured":"Gamerman , D. , & Lopes , H. F. 2006. Markov chain Monte Carlo: stochastic simulation for Bayesian inference . Chapman and Hall\/CRC. Gamerman, D., & Lopes, H. F. 2006. Markov chain Monte Carlo: stochastic simulation for Bayesian inference. Chapman and Hall\/CRC."},{"key":"e_1_3_2_1_16_1","volume-title":"Pipeline rupture: Alberta resident unaware of 2009 blast. CBS News","author":"Gollom M.","year":"2014","unstructured":"Gollom , M. ( 2014 ). Pipeline rupture: Alberta resident unaware of 2009 blast. CBS News , http:\/\/www. cbc. ca\/news\/pipeline-rupture-alberta-resident-unaware-of-2009-blast-1.2525030. Gollom, M. (2014). Pipeline rupture: Alberta resident unaware of 2009 blast. CBS News, http:\/\/www. cbc. ca\/news\/pipeline-rupture-alberta-resident-unaware-of-2009-blast-1.2525030."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/SIS.2003.1202255","volume-title":"Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706)","author":"Gudise V. G.","year":"2003","unstructured":"Gudise , V. G. , & Venayagamoorthy , G. K. 2003 . Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks . In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706) (pp. 110 - 117 ). IEEE. Gudise, V. G., & Venayagamoorthy, G. K. 2003. Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706) (pp. 110-117). IEEE."},{"key":"e_1_3_2_1_18_1","volume-title":"The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.","author":"Hassan S.","year":"2013","unstructured":"Hassan , S. , Khosravi , A. , & Jaafar , J. 2013 . Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting . In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE. Hassan, S., Khosravi, A., & Jaafar, J. 2013. Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE."},{"key":"e_1_3_2_1_19_1","volume-title":"Statistical models in S (pp. 249-307)","author":"Hastie T. J.","unstructured":"Hastie , T. J. 2017. Generalized additive models . In Statistical models in S (pp. 249-307) . Routledge . Hastie, T. J. 2017. Generalized additive models. In Statistical models in S (pp. 249-307). Routledge."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Jiang Y. Gao W. W. Zhao J. L. Chen Q. Liang D. Xu C. ... & Ruan L. M. 2018. Analysis of influencing factors on soil Zn content using generalized additive model. Scientific reports 8(1) 15567.  Jiang Y. Gao W. W. Zhao J. L. Chen Q. Liang D. Xu C. ... & Ruan L. M. 2018. Analysis of influencing factors on soil Zn content using generalized additive model. Scientific reports 8(1) 15567.","DOI":"10.1038\/s41598-018-33745-9"},{"key":"e_1_3_2_1_21_1","first-page":"236","article-title":"An introduction to statistical modelling. Research methods in the social sciences","author":"Jones K.","year":"2004","unstructured":"Jones , K. 2004 . An introduction to statistical modelling. Research methods in the social sciences . Sage, London , 236 - 251 . Jones, K. 2004. An introduction to statistical modelling. Research methods in the social sciences. Sage, London, 236-251.","journal-title":"Sage, London"},{"issue":"11","key":"e_1_3_2_1_22_1","first-page":"499","article-title":"Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction","volume":"8","author":"Lee K. E.","year":"2017","unstructured":"Lee , K. E. , Aziz , I. B. A. , & bin Jaafar , J. 2017 . Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction . INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS , 8 ( 11 ), 499 - 508 . Lee, K. E., Aziz, I. B. A., & bin Jaafar, J. 2017. Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 8(11), 499-508.","journal-title":"INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.2307\/2344614"},{"key":"e_1_3_2_1_24_1","series-title":"Vol. 698","volume-title":"Bayesian modeling using WinBUGS","author":"Ntzoufras I.","unstructured":"Ntzoufras , I. 2011. Bayesian modeling using WinBUGS ( Vol. 698 ) . John Wiley & Sons . Ntzoufras, I. 2011. Bayesian modeling using WinBUGS (Vol. 698). John Wiley & Sons."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ress.2015.08.007","article-title":"Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data","volume":"144","author":"Qin H.","year":"2015","unstructured":"Qin , H. , Zhou , W. , & Zhang , S. 2015 . Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data . Reliability Engineering & System Safety , 144 , 334 - 342 . Qin, H., Zhou, W., & Zhang, S. 2015. Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data. Reliability Engineering & System Safety, 144, 334-342.","journal-title":"Reliability Engineering & System Safety"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-017-9799-6"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.3389\/fnins.2019.00257","article-title":"Catching latrophilin with Lasso: a universal mechanism for axonal attraction and synapse formation","volume":"13","author":"Ushkaryov Y. A.","year":"2019","unstructured":"Ushkaryov , Y. A. , Lelianova , V. , & Vysokov , N. V. 2019 . Catching latrophilin with Lasso: a universal mechanism for axonal attraction and synapse formation . Frontiers in Neuroscience , 13 , 257 . Ushkaryov, Y. A., Lelianova, V., & Vysokov, N. V. 2019. Catching latrophilin with Lasso: a universal mechanism for axonal attraction and synapse formation. Frontiers in Neuroscience, 13, 257.","journal-title":"Frontiers in Neuroscience"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.corsci.2012.09.005","article-title":"Reliability assessment of buried pipelines based on different corrosion rate models","volume":"66","author":"Valor A.","year":"2013","unstructured":"Valor , A. , Caleyo , F. , Hallen , J. M. , & Vel\u00e1zquez , J. C. 2013 . Reliability assessment of buried pipelines based on different corrosion rate models . Corrosion Science , 66 , 78 - 87 . Valor, A., Caleyo, F., Hallen, J. M., & Vel\u00e1zquez, J. C. 2013. Reliability assessment of buried pipelines based on different corrosion rate models. Corrosion Science, 66, 78-87.","journal-title":"Corrosion Science"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Waldmann P. Feren\u010dakovi\u0107 M. M\u00e9sz\u00e1ros G. Khayatzadeh N. Curik I. & S\u00f6lkner J. 2019. AUTALASSO: an automatic adaptive LASSO for genome-wide prediction. BMC bioinformatics 20(1) 167.  Waldmann P. Feren\u010dakovi\u0107 M. M\u00e9sz\u00e1ros G. Khayatzadeh N. Curik I. & S\u00f6lkner J. 2019. AUTALASSO: an automatic adaptive LASSO for genome-wide prediction. BMC bioinformatics 20(1) 167.","DOI":"10.1186\/s12859-019-2743-3"},{"issue":"10","key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1080\/15732479.2015.1113300","article-title":"Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines","volume":"12","author":"Wang H.","year":"2016","unstructured":"Wang , H. , Yajima , A. , Liang , R. Y. , & Castaneda , H. 2016 . Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines . Structure and Infrastructure Engineering , 12 ( 10 ), 1281 - 1294 . Wang, H., Yajima, A., Liang, R. Y., & Castaneda, H. 2016. Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines. Structure and Infrastructure Engineering, 12(10), 1281-1294.","journal-title":"Structure and Infrastructure Engineering"},{"issue":"525","key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1080\/01621459.2017.1411268","article-title":"Partially linear functional additive models for multivariate functional data","volume":"114","author":"Wong R. K.","year":"2019","unstructured":"Wong , R. K. , Li , Y. , & Zhu , Z. 2019 . Partially linear functional additive models for multivariate functional data . Journal of the American Statistical Association , 114 ( 525 ), 406 - 418 . Wong, R. K., Li, Y., & Zhu, Z. 2019. Partially linear functional additive models for multivariate functional data. Journal of the American Statistical Association, 114(525), 406-418.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_3_2_1_33_1","volume-title":"Generalized Additive Models: An Introduction with R","author":"Wood S.","unstructured":"Wood , S. 2006. Generalized Additive Models: An Introduction with R . CR Cpress, Boca Raton , London, New York . Wood,S. 2006. Generalized Additive Models: An Introduction with R. CR Cpress, Boca Raton, London, New York."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Yin P. Mao N. Zhao C. Wu J. Sun C. Chen L. & Hong N. 2019. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. European radiology 29(4) 1841-1847.  Yin P. Mao N. Zhao C. Wu J. Sun C. Chen L. & Hong N. 2019. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. European radiology 29(4) 1841-1847.","DOI":"10.1007\/s00330-018-5730-6"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Zhang J. R. Zhang J. Lok T. M. & Lyu M. R. 2007. A hybrid particle swarm optimization\u2013back-propagation algorithm for feedforward neural network training. Applied mathematics and computation 185(2) 1026-1037.  Zhang J. R. Zhang J. Lok T. M. & Lyu M. R. 2007. A hybrid particle swarm optimization\u2013back-propagation algorithm for feedforward neural network training. Applied mathematics and computation 185(2) 1026-1037.","DOI":"10.1016\/j.amc.2006.07.025"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.corsci.2013.04.020","article-title":"Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data","volume":"73","author":"Zhang S.","year":"2013","unstructured":"Zhang , S. , Zhou , W. , & Qin , H. 2013 . Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data . Corrosion Science , 73 , 309 - 320 . Zhang, S., Zhou, W., & Qin, H. 2013. Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data. Corrosion Science, 73, 309-320.","journal-title":"Corrosion Science"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ress.2014.04.001","article-title":"Bayesian dynamic linear model for growth of corrosion defects on energy pipelines","volume":"128","author":"Zhang S.","year":"2014","unstructured":"Zhang , S. , & Zhou , W. 2014 . Bayesian dynamic linear model for growth of corrosion defects on energy pipelines . Reliability Engineering & System Safety , 128 , 24 - 31 . Zhang, S., & Zhou, W. 2014. Bayesian dynamic linear model for growth of corrosion defects on energy pipelines. Reliability Engineering & System Safety, 128, 24-31.","journal-title":"Reliability Engineering & System Safety"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Zhang S. Zhou W. Al-Amin M. Kariyawasam S. & Wang H. 2014. Time-dependent corrosion growth modeling using multiple in-line inspection data. Journal of Pressure Vessel Technology 136(4) 041202.  Zhang S. Zhou W. Al-Amin M. Kariyawasam S. & Wang H. 2014. Time-dependent corrosion growth modeling using multiple in-line inspection data. Journal of Pressure Vessel Technology 136(4) 041202.","DOI":"10.1115\/1.4026798"},{"issue":"6","key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s12665-019-8202-7","article-title":"Extreme learning machine-based prediction of daily water temperature for rivers","volume":"78","author":"Zhu S.","year":"2019","unstructured":"Zhu , S. , Heddam , S. , Wu , S. , Dai , J. , & Jia , B. 2019 . Extreme learning machine-based prediction of daily water temperature for rivers . Environmental Earth Sciences , 78 ( 6 ), 202 . Zhu, S., Heddam, S., Wu, S., Dai, J., & Jia, B. 2019. Extreme learning machine-based prediction of daily water temperature for rivers. Environmental Earth Sciences, 78(6), 202.","journal-title":"Environmental Earth Sciences"}],"event":{"name":"ICBDC2023: 2023 8th International Conference on Big Data and Computing","location":"Shenzhen China","acronym":"ICBDC2023"},"container-title":["2023 8th International Conference on Big Data and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624288.3624289","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3624288.3624289","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:44Z","timestamp":1750268984000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624288.3624289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":39,"alternative-id":["10.1145\/3624288.3624289","10.1145\/3624288"],"URL":"https:\/\/doi.org\/10.1145\/3624288.3624289","relation":{},"subject":[],"published":{"date-parts":[[2023,5,26]]},"assertion":[{"value":"2023-12-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}