{"id":"W1998366731","doi":"10.1115/1.3085890","title":"Optimal Vibration Suppression of Timoshenko Beam With Tuned-Mass-Damper Using Finite Element Method","year":2009,"lang":"en","type":"article","venue":"Journal of vibration and acoustics","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tuned mass damper; Finite element method; Optimal design; Timoshenko beam theory; Structural engineering; Vibration; Beam (structure); Damper; Harmonic; Sequential quadratic programming; Sensitivity (control systems); Control theory (sociology); Engineering; Computer science; Quadratic programming; Mathematics; Physics; Mathematical optimization; Acoustics; Electronic engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002415458,0.0001267343,0.0002448903,0.0001089994,0.00006426349,0.00005109821,0.00006098699,0.00009177253,0.00006760065],"category_scores_gemma":[0.00005701973,0.00008692305,0.00004875467,0.0001221663,0.00002210862,0.0004019348,0.00000712801,0.000158651,4.43876e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002155627,"about_ca_system_score_gemma":0.00003473954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.366071e-7,"about_ca_topic_score_gemma":1.570442e-7,"domain_scores_codex":[0.9990128,0.00003963171,0.0005024675,0.00007779892,0.0002469921,0.0001203488],"domain_scores_gemma":[0.9993891,0.00009024073,0.0001768989,0.00007075538,0.0001903917,0.00008258808],"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":[0.00005356661,0.00002267624,0.00005017178,0.00001294071,0.00001908149,0.000002696331,0.00006953072,0.5457148,0.4526584,0.0001141496,0.0001081344,0.00117388],"study_design_scores_gemma":[0.0008319712,0.0006782506,0.001589485,0.0000766192,0.00008709728,0.00002650129,0.00008823942,0.9234233,0.07278943,0.0002012507,0.00008786531,0.0001199196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0834762,0.0001780564,0.9158135,0.0002021091,0.0001336455,0.00008054252,0.000006100971,0.00001997381,0.00008984891],"genre_scores_gemma":[0.7826614,0.0001125556,0.2168941,0.0001497484,0.0001556584,4.530496e-7,0.000005995913,0.000008078965,0.00001199486],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6991852,"threshold_uncertainty_score":0.3544619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126655049926465,"score_gpt":0.2512752386903156,"score_spread":0.240008688191051,"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."}}