{"id":"W4410742951","doi":"10.1061/ajrua6.rueng-1562","title":"LUB: A Novel Adaptive Kriging Framework Incorporating Lower and Upper Bound Analysis for Enhanced Structural Reliability-Based Design Optimization","year":2025,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kriging; Reliability (semiconductor); Upper and lower bounds; Computer science; Reliability engineering; Structural reliability; Mathematical optimization; Mathematics; Engineering; Artificial intelligence; Machine learning; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004698233,0.0004434478,0.001140252,0.001627469,0.0001637169,0.0003736415,0.0003761284,0.0002638527,0.000004921044],"category_scores_gemma":[0.0070054,0.0003656896,0.0002630946,0.002068546,0.00006256254,0.0003997641,0.00006258186,0.0006169534,2.159165e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002359141,"about_ca_system_score_gemma":0.0001809418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004973193,"about_ca_topic_score_gemma":0.00001235192,"domain_scores_codex":[0.9965887,0.0001256204,0.001649785,0.0005436289,0.000612068,0.0004801782],"domain_scores_gemma":[0.9924724,0.005756433,0.0006109774,0.0003814937,0.0005826211,0.0001960822],"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":[0.0001316748,0.00002442473,0.002018139,0.0001466052,0.0002510426,0.000004906579,0.0002851402,0.9933085,0.0002937051,0.003260937,0.00001596212,0.0002589929],"study_design_scores_gemma":[0.0009903434,0.000120766,0.001497429,0.001006127,0.0002891126,0.00001233845,0.0002422099,0.9946551,0.00006756419,0.0006540416,0.0001013841,0.0003635638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05681434,0.001302911,0.9400886,0.00003771502,0.001211045,0.0004504609,0.00002604184,0.00005965654,0.000009182021],"genre_scores_gemma":[0.8335583,0.00004292156,0.1661618,0.000008645331,0.0001398935,0.00004310242,0.000002770072,0.0000297796,0.00001283059],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7767439,"threshold_uncertainty_score":0.9998795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331209897824967,"score_gpt":0.2773658421561214,"score_spread":0.2540537431778718,"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."}}