{"id":"W4405319942","doi":"10.1002/eqe.4291","title":"A Clustering‐Based Loading History Selection Method for the Calibration of Buckling‐Restrained Braces in Seismic Analysis","year":2024,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Ministry of Education of the People's Republic of China","keywords":"Metamodeling; Structural engineering; Nonlinear system; Robustness (evolution); Calibration; OpenSees; Earthquake engineering; Sobol sequence; Incremental Dynamic Analysis; Cluster analysis; Seismic loading; Computer science; Buckling; Engineering; Seismic analysis; Sensitivity (control systems); Finite element method; Mathematics; Machine learning; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002672037,0.0002831094,0.0003507782,0.0006610426,0.00004931053,0.00005230723,0.0002010462,0.0001622559,0.00001333955],"category_scores_gemma":[0.00007033849,0.0002508025,0.0002002554,0.0009705037,0.00002467452,0.0002058942,0.00001968941,0.0003943857,2.426607e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008069085,"about_ca_system_score_gemma":0.0000589655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002659276,"about_ca_topic_score_gemma":0.0006241704,"domain_scores_codex":[0.9986279,0.00003119598,0.0005035076,0.000282434,0.0001928033,0.0003621439],"domain_scores_gemma":[0.999069,0.000554821,0.000057294,0.0002190857,0.0000457129,0.00005406688],"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":[0.00001152934,9.404862e-7,0.000312204,0.0005489023,0.0001325513,0.000001746685,0.0002626358,0.8694347,0.0005785556,0.0003166036,0.00001336076,0.1283863],"study_design_scores_gemma":[0.0001139745,0.00003342252,0.1294406,0.00009835594,0.0001072561,0.000006929429,0.00001405934,0.8693002,0.0004432556,0.00009202737,0.0001355472,0.0002142542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3767159,0.0006198407,0.6205042,0.00005059057,0.001053071,0.0003147571,0.00003549352,0.0007026172,0.000003564209],"genre_scores_gemma":[0.9406356,0.00002190971,0.05895292,0.000009708328,0.0001743102,0.00006808672,0.0000443561,0.00006908816,0.00002402498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5639197,"threshold_uncertainty_score":0.9999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113672063687654,"score_gpt":0.2693663100321876,"score_spread":0.2579991036634222,"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."}}