{"id":"W7083315972","doi":"10.1063/5.0283343","title":"An intelligent semi-active bridge pier protection system with MR-STF damping and TSO–DBO optimized fuzzy control","year":2025,"lang":"en","type":"article","venue":"AIP Advances","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"123 Certification (Canada)","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Magnetorheological fluid; Fuzzy control system; Metaheuristic; Benchmark (surveying); Fuzzy logic; Collision; Particle swarm optimization; Magnetorheological elastomer","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001000191,0.000160401,0.0002573034,0.0000685652,0.0001206966,0.00005472284,0.00007430614,0.00007070661,0.000008820937],"category_scores_gemma":[0.00002220434,0.0001158651,0.00002878551,0.0001571333,0.00005200985,0.000387931,0.00001078027,0.0001351354,0.000004430543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006628784,"about_ca_system_score_gemma":0.00001402268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001865747,"about_ca_topic_score_gemma":0.00002362545,"domain_scores_codex":[0.9992498,0.00005420652,0.0001949389,0.0002283516,0.00008962292,0.0001830919],"domain_scores_gemma":[0.999652,0.00006574165,0.00003169487,0.0001310327,0.00005811717,0.00006138228],"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":[0.001297156,0.00008428597,0.001743298,0.00063049,0.000434645,0.00002049098,0.0005009096,0.8183216,0.03966117,0.01268606,0.00006003067,0.1245598],"study_design_scores_gemma":[0.003573325,0.0005451766,0.006530456,0.0004891577,0.0001013518,0.00002393732,0.0008640434,0.9691414,0.01213636,0.0005666489,0.005501369,0.0005267878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0419993,0.002621855,0.9512842,0.0001534893,0.0003092454,0.0006357208,0.00001080656,0.0004299027,0.002555529],"genre_scores_gemma":[0.9983478,0.0002087656,0.001005666,0.00009386241,0.00007916096,0.0001831379,0.000005396609,0.00001238939,0.00006387262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9563484,"threshold_uncertainty_score":0.4724841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007397588606458991,"score_gpt":0.2250802523803275,"score_spread":0.2176826637738685,"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."}}