{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:04:38Z","timestamp":1777608278214,"version":"3.51.4"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T00:00:00Z","timestamp":1609286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Greek Diaspora Fellowship"},{"name":"Stavros S. Niarchos Foundation and Fulbright Programme"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p>\n            Fusion of data mining and computational mechanics is a modern approach for the exploitation of available data within rigorous modeling. First steps in this direction have been focused on the usage of neural networks and other soft computing tools as metamodeling tools. This framework seems suitable for numerical homogenization techniques realized within the so-called FE\n            <jats:sup>2<\/jats:sup>\n            environment, where the lower-level analysis of a detailed representative volume element is replaced by a prediction based on a previously prepared database. Numerically prepared data are used here, although the method can be used with experimental data as well. In this case, the need for a constitutive description of the fine scale is bypassed. Extraction of material properties from the database, required by the upper-level finite element analysis, is based on backpropagation artificial neural networks. The method is applicable to monuments and masonry structural systems. We investigate this approach here for the analysis of masonry structures with elastoplastic behavior. Results indicate a satisfactory comparison with published research.\n          <\/jats:p>","DOI":"10.1145\/3423154","type":"journal-article","created":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T05:29:15Z","timestamp":1609392555000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Data-driven Computational Homogenization Using Neural Networks"],"prefix":"10.1145","volume":"14","author":[{"given":"Georgios A.","family":"Drosopoulos","sequence":"first","affiliation":[{"name":"Discipline of Civil Engineering, Structural Engineering and Computational Mechanics Group, University of Kwazulu-Natal, Durban, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios E.","family":"Stavroulakis","sequence":"additional","affiliation":[{"name":"Computational Mechanics and Optimization Laboratory, School of Production Engineering and Management, Technical University of Crete, Chania, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,12,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of XXIV AIMETA Conference","author":"Kova\u010devi\u0107 V. C.","year":"2019","unstructured":"V. C. Kova\u010devi\u0107 , S. Monchetti , M. Betti , and C. Borri . 2019. Metamodels in computational mechanics for Bayesian FEM updating of ancient high-rise masonry structures . In Proceedings of XXIV AIMETA Conference 2019 . Lecture Notes in Mechanical Engineering. A. Carcaterra, A. Paolone, G. Graziani (Eds.). Springer, Cham. V. C. Kova\u010devi\u0107, S. Monchetti, M. Betti, and C. Borri. 2019. Metamodels in computational mechanics for Bayesian FEM updating of ancient high-rise masonry structures. In Proceedings of XXIV AIMETA Conference 2019. Lecture Notes in Mechanical Engineering. A. Carcaterra, A. Paolone, G. Graziani (Eds.). Springer, Cham."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2014.08.004"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)EM.1943-7889.0001500"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2514\/3.11810"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0143-974X(97)00039-4"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2014.01.041"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2006.02.015"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s004660050264"},{"key":"e_1_2_1_9_1","volume-title":"Inverse and Crack Identification Problems in Engineering Mechanics","author":"Stavroulakis G. E.","unstructured":"G. E. Stavroulakis . 2001. Inverse and Crack Identification Problems in Engineering Mechanics . Springer . G. E. Stavroulakis. 2001. Inverse and Crack Identification Problems in Engineering Mechanics. Springer."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2007.08.017"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconres.2020.106167"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9020243"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0045-7949(01)00083-9"},{"key":"e_1_2_1_14_1","volume-title":"Advances of Soft Computing in Engineering","author":"Ghaboussi J.","unstructured":"J. Ghaboussi . 2010. Advances in neural networks in computational mechanics and engineering . In Advances of Soft Computing in Engineering , Z. Waszczyszyn (Ed.). CISM International Centre for Mechanical Sciences, Vol . 512. Springer , Vienna, 191--236. J. Ghaboussi. 2010. Advances in neural networks in computational mechanics and engineering. In Advances of Soft Computing in Engineering, Z. Waszczyszyn (Ed.). CISM International Centre for Mechanical Sciences, Vol. 512. Springer, Vienna, 191--236."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"G. Stavroulakis G. Bolzon Z. Waszczyszyn and L. Ziemianski. 2003. Inverse analysis. In Comprehensive Structural Integrity B. Karihaloo R. O. Ritchie and I. Milne (Eds.). Numerical and computational methods Vol. 3. Elsevier Science Ltd. 685--718.  G. Stavroulakis G. Bolzon Z. Waszczyszyn and L. Ziemianski. 2003. Inverse analysis. In Comprehensive Structural Integrity B. Karihaloo R. O. Ritchie and I. Milne (Eds.). Numerical and computational methods Vol. 3. Elsevier Science Ltd. 685--718.","DOI":"10.1016\/B0-08-043749-4\/03117-7"},{"key":"e_1_2_1_16_1","volume-title":"Advances in Engineering Software 39","author":"Tsompanakis Y.","year":"2008","unstructured":"Y. Tsompanakis , N. D. Lagaros , and G. E. Stavroulakis . 2008. Soft computing techniques in parameter identification and probabilistic seismic analysis of structures . Advances in Engineering Software 39 , ( 2008 ), 612--624. Y. Tsompanakis, N. D. Lagaros, and G. E. Stavroulakis. 2008. Soft computing techniques in parameter identification and probabilistic seismic analysis of structures. Advances in Engineering Software 39, (2008), 612--624."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2019.00110"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219876219500452"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.istruc.2019.06.017"},{"key":"e_1_2_1_20_1","first-page":"95","volume-title":"Civil Engineering Department","author":"Wu X.","year":"1995","unstructured":"X. Wu and J. Ghaboussi . 1995. Neural Network--based Material Modeling. Civil Engineering Studies, Structural Research Series No. 599, University of Illinois at Urbana Champaign , Civil Engineering Department , 1995 . UILU-ENG- 95 - 2002 . X. Wu and J. Ghaboussi. 1995. Neural Network--based Material Modeling. Civil Engineering Studies, Structural Research Series No. 599, University of Illinois at Urbana Champaign, Civil Engineering Department, 1995. UILU-ENG-95-2002."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/nme.905"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2008.05.004"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.112893"},{"key":"e_1_2_1_24_1","unstructured":"X. Lu D. G. Giovanis J. Yvonnet V. Papadopoulos F. Detrez and J. Bai. 2018. A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene\/polymer nanocomposites Computat. Mech. (2018) 10.1007\/s00466-018-1643-0.  X. Lu D. G. Giovanis J. Yvonnet V. Papadopoulos F. Detrez and J. Bai. 2018. A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene\/polymer nanocomposites Computat. Mech. (2018) 10.1007\/s00466-018-1643-0."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.112955"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2013.06.012"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2019.00075"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"T. Kirchdoerfer and M. Ortiz. 2016. Data-driven computational mechanics. Comput. Meth. Appl. Mech. Eng. 304 (2016) 81--101.  T. Kirchdoerfer and M. Ortiz. 2016. Data-driven computational mechanics. Comput. Meth. Appl. Mech. Eng. 304 (2016) 81--101.","DOI":"10.1016\/j.cma.2016.02.001"},{"key":"e_1_2_1_29_1","volume-title":"Non-linear Mechanics of Structures\u2014New Approaches and Nonincremental Methods of Calculation","author":"Ladeveze P.","unstructured":"P. Ladeveze . 1998. Non-linear Mechanics of Structures\u2014New Approaches and Nonincremental Methods of Calculation . Springer . P. Ladeveze. 1998. Non-linear Mechanics of Structures\u2014New Approaches and Nonincremental Methods of Calculation. Springer."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-5096(63)90036-X"},{"key":"e_1_2_1_32_1","volume-title":"User's guide. Stockholm Sweden: COMSOL AB","author":"Multiphysics MSOL","year":"2007","unstructured":"CO MSOL Multiphysics . 2007. User's guide. Stockholm Sweden: COMSOL AB ; 2007 . COMSOL Multiphysics. 2007. User's guide. Stockholm Sweden: COMSOL AB; 2007."},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"R. de Borst M. A. Crisfield J. J. C. Remmers and C. V. Verhoosel. 2012. Non-linear Finite Element Analysis of Solids and Structures (2nd edition). John Wiley 8 Sons UK.  R. de Borst M. A. Crisfield J. J. C. Remmers and C. V. Verhoosel. 2012. Non-linear Finite Element Analysis of Solids and Structures (2nd edition). John Wiley 8 Sons UK.","DOI":"10.1002\/9781118375938"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2006.08.009"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the 10th HSTAM International Congress on Mechanics.","author":"Drosopoulos G. A.","unstructured":"G. A. Drosopoulos , M. E. Stavroulaki , and G. E. Stavroulakis . 2013. Homogenization and elastic analysis of masonry walls . In Proceedings of the 10th HSTAM International Congress on Mechanics. G. A. Drosopoulos, M. E. Stavroulaki, and G. E. Stavroulakis. 2013. Homogenization and elastic analysis of masonry walls. In Proceedings of the 10th HSTAM International Congress on Mechanics."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00419-018-1440-4"}],"container-title":["Journal on Computing and Cultural Heritage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423154","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3423154","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:56Z","timestamp":1750195496000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423154"}},"subtitle":["FE\n            <sup>2<\/sup>\n            -NN Application on Damaged Masonry"],"short-title":[],"issued":{"date-parts":[[2020,12,30]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["10.1145\/3423154"],"URL":"https:\/\/doi.org\/10.1145\/3423154","relation":{},"ISSN":["1556-4673","1556-4711"],"issn-type":[{"value":"1556-4673","type":"print"},{"value":"1556-4711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,30]]},"assertion":[{"value":"2020-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}