{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T01:56:50Z","timestamp":1770083810961,"version":"3.49.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006505","name":"Engineer Research and Development Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09382-3","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T17:02:32Z","timestamp":1706115752000},"page":"6531-6545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Estimating kinetic energy reduction for terminal ballistics using a hyperparameter-optimized neural network"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8173-1820","authenticated-orcid":false,"given":"Brianna","family":"Thompson","sequence":"first","affiliation":[]},{"given":"Jesse","family":"Sherburn","sequence":"additional","affiliation":[]},{"given":"James","family":"Ross","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"9382_CR1","unstructured":"(1961) The resistance of various metallic materials to perforation by steel fragments; empirical relationships for fragments residual velocity and residual weight. Technical report BAL-TR-47. Johns Hopkins University Ballistic Analysis Laboratory, Baltimore"},{"key":"9382_CR2","unstructured":"(2008) Unified facilities criteria (ufc) structures to resist the effects of accidental explosions. Technical report UFC 3-340-02. Department of Defense, Washington, DC"},{"key":"9382_CR3","unstructured":"(2018) Optuna: a hyperparameter optimization framework-Optuna 3.0.0 documentation. https:\/\/optuna.readthedocs.io\/en\/stable\/index.html"},{"key":"9382_CR4","doi-asserted-by":"publisher","unstructured":"Akiba T, Sano S, Yanase T, et\u00a0al (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25rd ACM SIGKDD international conference on knowledge discovery and data mining. https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"9382_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ijimpeng.2017.03.018","volume":"108","author":"C Anderson","year":"2017","unstructured":"Anderson C (2017) Analytical models for penetration mechanics: a review. Int J Impact Eng 108:3\u201326. https:\/\/doi.org\/10.1016\/j.ijimpeng.2017.03.018","journal-title":"Int J Impact Eng"},{"key":"9382_CR6","doi-asserted-by":"publisher","unstructured":"Auten JR, Hammell RJ (2014) Predicting the perforation capability of kinetic energy projectiles using artificial neural networks. In: 2014 IEEE symposium on computational intelligence for engineering solutions (CIES), pp 132\u2013139. https:\/\/doi.org\/10.1109\/CIES.2014.7011842","DOI":"10.1109\/CIES.2014.7011842"},{"key":"9382_CR7","doi-asserted-by":"publisher","unstructured":"Auten JR, Hammell RJ (2017) Predicting terminal ballistics using an iterative application of an artificial neural network. In: 2017 Computing conference. IEEE, pp 706\u2013715. https:\/\/doi.org\/10.1109\/SAI.2017.8252174","DOI":"10.1109\/SAI.2017.8252174"},{"issue":"1","key":"9382_CR8","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/mca27010006","volume":"27","author":"M Cerrada","year":"2022","unstructured":"Cerrada M, Trujillo L, Hern\u00e1ndez DE et al (2022) Automl for feature selection and model tuning applied to fault severity diagnosis in spur gearboxes. Math Comput Appl 27(1):6. https:\/\/doi.org\/10.3390\/mca27010006","journal-title":"Math Comput Appl"},{"key":"9382_CR9","unstructured":"Failla DP, Sherburn JA (2021) Parametric study of penetration simulations of various metallic targets and projectiles using epic (unpublished)"},{"key":"9382_CR10","doi-asserted-by":"publisher","first-page":"6369","DOI":"10.1016\/j.ijsolstr.2008.08.009","volume":"45","author":"D Fern\u00e1ndez-Fdz","year":"2008","unstructured":"Fern\u00e1ndez-Fdz D, Zaera R (2008) A new tool based on artificial neural networks for the design of lightweight ceramic-metal armour against high-velocity impact of solids. Int J Solids Struct 45:6369\u20136383. https:\/\/doi.org\/10.1016\/j.ijsolstr.2008.08.009","journal-title":"Int J Solids Struct"},{"key":"9382_CR11","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s00521-006-0050-1","volume":"16","author":"A Garc\u00eda-Crespo","year":"2007","unstructured":"Garc\u00eda-Crespo A, Ruiz-Mezcua B, Fern\u00e1ndez-Fdz D et al (2007) Prediction of the response under impact of steel armours using a multilayer perceptron. Neural Comput Appl 16:147\u2013154. https:\/\/doi.org\/10.1007\/s00521-006-0050-1","journal-title":"Neural Comput Appl"},{"key":"9382_CR12","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10489-009-0205-8","volume":"35","author":"I Gonzalez-Carrasco","year":"2011","unstructured":"Gonzalez-Carrasco I, Garc\u00eda-Crespo A, Ruiz-Mezcua B et al (2011) Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches. Appl Intell 35:89\u2013109. https:\/\/doi.org\/10.1007\/s10489-009-0205-8","journal-title":"Appl Intell"},{"key":"9382_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s00521-011-0635-1","volume":"21","author":"I Gonzalez-Carrasco","year":"2012","unstructured":"Gonzalez-Carrasco I, Garc\u00eda-Crespo A, Ruiz-Mezcua B et al (2012) A neural network-based methodology for the recreation of high-speed impacts on metal armours. Neural Comput Appl 21:91\u2013107. https:\/\/doi.org\/10.1007\/s00521-011-0635-1","journal-title":"Neural Comput Appl"},{"key":"9382_CR14","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1590\/1679-78251200","volume":"12","author":"M Hosseini","year":"2015","unstructured":"Hosseini M, Dalvand A (2015) Neural network approach for estimation of penetration depth in concrete targets by ogive-nose steel projectiles. Latin Am J Solids Struct 12:492\u2013506. https:\/\/doi.org\/10.1590\/1679-78251200","journal-title":"Latin Am J Solids Struct"},{"issue":"4","key":"9382_CR15","doi-asserted-by":"publisher","first-page":"2040","DOI":"10.3390\/app12042040","volume":"12","author":"QBA Latif Imran","year":"2022","unstructured":"Latif Imran QBA, Memon ZA, Mahmood Z et al (2022) A machine learning model for the prediction of concrete penetration by the ogive nose rigid projectile. Appl Sci 12(4):2040. https:\/\/doi.org\/10.3390\/app12042040","journal-title":"Appl Sci"},{"issue":"6","key":"9382_CR16","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.ijimpeng.2010.10.017","volume":"38","author":"GR Johnson","year":"2011","unstructured":"Johnson GR (2011) Numerical algorithms and material models for high-velocity impact computations. Int J Impact Eng 38(6):456\u2013472. https:\/\/doi.org\/10.1016\/j.ijimpeng.2010.10.017","journal-title":"Int J Impact Eng"},{"key":"9382_CR17","unstructured":"Johnson GR, Cook WH (1983) A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. In: Proceedings of 7th international symposium on ballistics, The Hague, The Netherlands, pp 541\u2013547"},{"key":"9382_CR18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/0013-7944(85)90052-9","volume":"21","author":"GR Johnson","year":"1985","unstructured":"Johnson GR, Cook WH (1985) Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng Fract Mech 21:31\u201348. https:\/\/doi.org\/10.1016\/0013-7944(85)90052-9","journal-title":"Eng Fract Mech"},{"issue":"2","key":"9382_CR19","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.dt.2014.12.001","volume":"11","author":"N K\u0131l\u0131\u00c7","year":"2015","unstructured":"K\u0131l\u0131\u00c7 N, Ekici B, Hartomac\u0131o\u011flu S (2015) Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools. Defence Technol 11(2):110\u2013122. https:\/\/doi.org\/10.1016\/j.dt.2014.12.001","journal-title":"Defence Technol"},{"key":"9382_CR20","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.ijimpeng.2012.10.011","volume":"56","author":"S Ryan","year":"2013","unstructured":"Ryan S, Thaler S (2013) Artificial neural networks for characterising whipple shield performance. Int J Impact Eng 56:61\u201370. https:\/\/doi.org\/10.1016\/j.ijimpeng.2012.10.011","journal-title":"Int J Impact Eng"},{"key":"9382_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.eswa.2015.09.038","volume":"45","author":"S Ryan","year":"2016","unstructured":"Ryan S, Thaler S, Kandanaarachchi S (2016) Machine learning methods for predicting the outcome of hypervelocity impact events. Expert Syst Appl 45:23\u201339. https:\/\/doi.org\/10.1016\/j.eswa.2015.09.038","journal-title":"Expert Syst Appl"},{"key":"9382_CR22","unstructured":"Shaikh R (2018) Feature selection techniques in machine learning with python. https:\/\/towardsdatascience.com\/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e"},{"key":"9382_CR23","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-030-30355-6_16","volume-title":"An artificial intelligence-based hybrid method for multi-layered armour systems","author":"F Teixeira-Dias","year":"2019","unstructured":"Teixeira-Dias F, Thompson S, Paulino M (2019) An artificial intelligence-based hybrid method for multi-layered armour systems. Springer, Berlin, pp 381\u2013400. https:\/\/doi.org\/10.1007\/978-3-030-30355-6_16"},{"issue":"9","key":"9382_CR24","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1016\/j.dt.2021.08.001","volume":"18","author":"S Thompson","year":"2022","unstructured":"Thompson S, Teixeira-Dias F, Paulino M et al (2022) Ballistic response of armour plates using generative adversarial networks. Defence Technol 18(9):1513\u20131522. https:\/\/doi.org\/10.1016\/j.dt.2021.08.001","journal-title":"Defence Technol"},{"key":"9382_CR25","doi-asserted-by":"publisher","unstructured":"Yao Q, Wang M, Chen Y, et\u00a0al (2018) Taking human out of learning applications: a survey on automated machine learning. arXiv:1810.13306https:\/\/doi.org\/10.48550\/arXiv.1810.13306","DOI":"10.48550\/arXiv.1810.13306"},{"key":"9382_CR26","doi-asserted-by":"publisher","unstructured":"Zukas JA (2004) Practical aspects of numerical simulations of dynamic events. In: Zukas JA (ed) Introduction to hydrocodes, studies in applied mechanics, vol\u00a049. Elsevier, pp 279\u2013310. https:\/\/doi.org\/10.1016\/S0922-5382(04)80009-6","DOI":"10.1016\/S0922-5382(04)80009-6"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09382-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09382-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09382-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T19:04:39Z","timestamp":1710615879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09382-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,24]]},"references-count":26,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9382"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09382-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,24]]},"assertion":[{"value":"9 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}