Application of Gurson–Tvergaard–Needleman Constitutive Model to the Tensile Behavior of Reinforcing Bars with Corrosion Pits
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{"title"=>"Application of Gurson-Tvergaard-Needleman Constitutive Model to the Tensile Behavior of Reinforcing Bars with Corrosion Pits", "type"=>"journal", "authors"=>[{"first_name"=>"Yidong", "last_name"=>"Xu", "scopus_author_id"=>"36770132600"}, {"first_name"=>"Chunxiang", "last_name"=>"Qian", "scopus_author_id"=>"7202310696"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"368116194", "doi"=>"10.1371/journal.pone.0054368", "sgr"=>"84872313501", "scopus"=>"2-s2.0-84872313501", "pmid"=>"23342140"}, "id"=>"da9627c3-10e3-329a-a91a-73be082340bf", "abstract"=>"Based on meso-damage mechanics and finite element analysis, the aim of this paper is to describe the feasibility of the Gurson-Tvergaard-Needleman (GTN) constitutive model in describing the tensile behavior of corroded reinforcing bars. The orthogonal test results showed that different fracture pattern and the related damage evolution process can be simulated by choosing different material parameters of GTN constitutive model. Compared with failure parameters, the two constitutive parameters are significant factors affecting the tensile strength. Both the nominal yield and ultimate tensile strength decrease markedly with the increase of constitutive parameters. Combining with the latest data and trial-and-error method, the suitable material parameters of GTN constitutive model were adopted to simulate the tensile behavior of corroded reinforcing bars in concrete under carbonation environment attack. The numerical predictions can not only agree very well with experimental measurements, but also simplify the finite element modeling process.", "link"=>"http://www.mendeley.com/research/application-gursontvergaardneedleman-constitutive-model-tensile-behavior-reinforcing-bars-corrosion", "reader_count"=>6, "reader_count_by_academic_status"=>{"Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Master"=>3}, "reader_count_by_user_role"=>{"Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Master"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Materials Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Materials Science"=>{"Materials Science"=>1}}, "group_count"=>0}

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PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Engineering and technology", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}, {"subject_area"=>"/Physical sciences/Chemistry", "average_usage"=>[247, 429, 544, 647, 747, 842, 929, 1012, 1099, 1179, 1263, 1339, 1409]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}, {"subject_area"=>"/Physical sciences/Physics", "average_usage"=>[254, 421, 527, 626, 720, 813, 900, 983, 1063, 1136, 1210, 1283, 1342]}]}
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