{"id":"W4388066825","doi":"10.1016/j.jcsr.2023.108288","title":"Failure mode and capacity prediction for bolted T-stub connections using ensemble learning","year":2023,"lang":"en","type":"article","venue":"Journal of Constructional Steel Research","topic":"Structural Load-Bearing Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Boosting (machine learning); Finite element method; Random forest; Stub (electronics); Nonlinear system; Ensemble learning; Failure mode and effects analysis; Computer science; Machine learning; Structural engineering; Feature selection; Engineering; Artificial intelligence; Reliability 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006755276,0.0008354728,0.0007544391,0.001090371,0.0003479061,0.0004172402,0.0009587799,0.0009030952,0.001662424],"category_scores_gemma":[0.001951026,0.0002652609,0.0007285529,0.0005512912,0.0003144355,0.0009244525,0.0004628724,0.0008270651,0.0004149377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003914831,"about_ca_system_score_gemma":0.0003431603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005551799,"about_ca_topic_score_gemma":0.007062374,"domain_scores_codex":[0.9998357,0.0000293382,0.000008562652,0.0000555914,0.00003261484,0.00003809408],"domain_scores_gemma":[0.9985167,0.0008287072,0.0001264434,0.0001328885,0.0003227791,0.00007241598],"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":[0.000116395,0.00007145952,0.005937573,0.00001923418,0.00004102158,0.00007460493,0.00002057865,0.9459612,0.001749794,0.0003972015,0.0005897415,0.04502119],"study_design_scores_gemma":[3.75411e-7,0.000004804815,0.0003631764,7.646545e-7,0.000002492033,0.00000331913,0.000001704039,0.9993667,0.0001183607,0.0001260232,0.0000110648,0.000001255003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6649529,0.0004086157,0.3315744,0.00012783,0.00006980544,0.0000244113,0.0003233349,0.0009819715,0.001536817],"genre_scores_gemma":[0.9916664,0.00005426176,0.007177426,0.00000972143,0.0000142345,0.00001317764,0.0002278666,0.00001816388,0.0008188701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005551799,"threshold_uncertainty_score":0.01103896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08792435213379991,"score_gpt":0.3460970790249634,"score_spread":0.2581727268911635,"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."}}