{"id":"W2775763120","doi":"10.1007/s10518-017-0288-2","title":"Assessment of structural damage detection methods for steel structures using full-scale experimental data and nonlinear analysis","year":2017,"lang":"en","type":"article","venue":"Bulletin of Earthquake Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; École Polytechnique Fédérale de Lausanne","keywords":"Structural engineering; Earthquake shaking table; Context (archaeology); Wavelet; Computer science; Downtime; Identification (biology); Nonlinear system; Data mining; Reliability engineering; Engineering; Geology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003378291,0.0007165202,0.0003889652,0.0008022109,0.0002935914,0.0005364413,0.0007726758,0.001160927,0.0006721754],"category_scores_gemma":[0.007804131,0.000317343,0.0003497177,0.0003215141,0.0004143106,0.001316875,0.0004236644,0.0003185755,0.0002080048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005248009,"about_ca_system_score_gemma":0.0005485987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001665691,"about_ca_topic_score_gemma":0.002600087,"domain_scores_codex":[0.999198,0.0002698701,0.0000532297,0.0001191957,0.0003186447,0.00004108489],"domain_scores_gemma":[0.9905728,0.00569346,0.0007295514,0.0006178551,0.0022473,0.0001391299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002855346,0.003224451,0.12767,0.001023177,0.0003185604,0.0001857423,0.0003332485,0.2992874,0.248539,0.001174975,0.0009777653,0.3144103],"study_design_scores_gemma":[0.0001045251,0.001429858,0.07468886,0.00002854706,0.00009050709,0.0001039118,0.0001242305,0.8749783,0.04756952,0.0004427698,0.0003963149,0.00004259596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9154032,0.0004303527,0.08238906,0.0001198956,0.00002169753,0.0001116877,0.0002271124,0.0001954207,0.001101515],"genre_scores_gemma":[0.9740379,0.00009437357,0.02542545,0.00001008463,0.000008650222,0.0000486702,0.0001432124,0.00001004215,0.0002217977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003378291,"threshold_uncertainty_score":0.01786631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03905548153098178,"score_gpt":0.387659864302842,"score_spread":0.3486043827718603,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}