{"id":"W2010174052","doi":"10.1115/detc2014-35623","title":"An Efficient Reliability Analysis Method for Structures With Epistemic Uncertainty Using Evidence Theory","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Probabilistic logic; Reliability theory; Computer science; Measure (data warehouse); Limit (mathematics); Limit state design; Uncertainty quantification; Vertex (graph theory); Extreme point; Point (geometry); Mathematical optimization; Point estimation; Reliability engineering; Mathematics; Data mining; Theoretical computer science; Statistics; Artificial intelligence; Machine learning; Failure rate; Engineering; Structural 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":[0.002191629,0.00100507,0.001374365,0.002405329,0.0006461836,0.001413885,0.00158742,0.001122472,0.002088741],"category_scores_gemma":[0.005871901,0.0006024896,0.002066443,0.00144581,0.001031023,0.001864917,0.001602296,0.001839577,0.0004323406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008639379,"about_ca_system_score_gemma":0.001547423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001784308,"about_ca_topic_score_gemma":0.001254286,"domain_scores_codex":[0.9986093,0.0004993231,0.0000826855,0.0002239037,0.0005144349,0.00007028874],"domain_scores_gemma":[0.9975646,0.001524867,0.000196581,0.0001383933,0.0005258317,0.0000497163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007277315,0.00003351911,0.0007756024,0.000362471,0.00009145503,0.0001703954,0.0002790361,0.6646413,0.008232488,0.1329928,0.001197305,0.1911509],"study_design_scores_gemma":[0.000008544936,0.00003384335,0.0001526542,0.00002515006,0.00002343094,0.00007423668,0.00002092978,0.9697511,0.001292851,0.02745613,0.001143511,0.00001767841],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008663484,0.00006696614,0.9987524,0.00001712219,0.000003916811,0.000008934192,0.000006255454,0.00002632793,0.0002517514],"genre_scores_gemma":[0.1654156,0.0005132899,0.8322303,0.00003028129,0.00004763961,0.0002142304,0.0001135723,0.00007965376,0.001355383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002405329,"threshold_uncertainty_score":0.0115906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004874907440214,"score_gpt":0.4110264197356001,"score_spread":0.3105389289915786,"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."}}