{"id":"W4362474531","doi":"10.1016/j.ymssp.2023.110324","title":"Efficient structural model updating with spatially sparse modal data: A Bayesian perspective","year":2023,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Identifiability; Gibbs sampling; Bayesian probability; Bayesian inference; Sensitivity (control systems); Computer science; Modal; Algorithm; Hierarchy; Degrees of freedom (physics and chemistry); Finite element method; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002518304,0.0008981978,0.00169176,0.001139232,0.0005628676,0.001510323,0.002214782,0.001779375,0.002587684],"category_scores_gemma":[0.01535383,0.001555472,0.0008554502,0.001445839,0.001192448,0.00330294,0.00217213,0.002475931,0.0006647232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007328941,"about_ca_system_score_gemma":0.001654689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006202742,"about_ca_topic_score_gemma":0.01018593,"domain_scores_codex":[0.9989187,0.0004037608,0.00006022982,0.0001885019,0.0003547917,0.00007408289],"domain_scores_gemma":[0.9938906,0.004557261,0.0003777851,0.0005310137,0.000538628,0.000104615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001202879,0.00007498301,0.0006834384,0.0001662949,0.0001037653,0.00005353053,0.0001063948,0.8236193,0.003348916,0.05464417,0.001976055,0.1151028],"study_design_scores_gemma":[0.000008529229,0.00001064605,0.0001210842,0.000008728673,0.000009092767,0.00001681243,0.000006609522,0.9750508,0.0004137485,0.02391376,0.0004319217,0.000008273454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002717053,0.0001228263,0.9962777,0.000253849,0.00001452352,0.000009852431,0.00004533743,0.00009358697,0.0004651744],"genre_scores_gemma":[0.3686137,0.001191253,0.6230282,0.0004593906,0.0003804117,0.0002122745,0.0007446995,0.0002894035,0.005080574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006202742,"threshold_uncertainty_score":0.01331824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536260311921343,"score_gpt":0.2922829988566084,"score_spread":0.256920395737395,"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."}}