{"id":"W4410100255","doi":"10.1080/15732479.2025.2499513","title":"A Bayesian belief network approach to bridge infrastructure resilience assessment against seismic hazard","year":2025,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Regina","funders":"","keywords":"Bridge (graph theory); Bayesian network; Resilience (materials science); Seismic hazard; Hazard; Earthquake scenario; Computer science; Engineering; Forensic engineering; Risk analysis (engineering); Environmental science; Construction engineering; Civil engineering; Business; Artificial intelligence","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":[0.0002531427,0.001023191,0.001015783,0.0006928548,0.0004279628,0.0003352324,0.0007757252,0.0005968026,0.00006776722],"category_scores_gemma":[0.00009930066,0.0009558168,0.0002204656,0.002189816,0.0001514958,0.0004599882,0.0002826131,0.001528598,0.000002547861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844161,"about_ca_system_score_gemma":0.000156062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001462021,"about_ca_topic_score_gemma":0.00001272686,"domain_scores_codex":[0.9961827,0.00006519794,0.0008594497,0.001056985,0.0005281192,0.00130759],"domain_scores_gemma":[0.9982209,0.0001055389,0.00009098276,0.0009798119,0.0001344084,0.0004683814],"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":[0.00001442115,0.000006122348,0.00526695,0.0003790827,0.0002314672,0.000006577773,0.0002745759,0.9572477,0.005084502,0.001893673,0.009973016,0.01962185],"study_design_scores_gemma":[0.0005546887,0.00005268826,0.2650912,0.0002049673,0.0001514799,0.00005672235,0.0001113291,0.7066972,0.001275947,0.003718739,0.02093255,0.001152474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3008277,0.0007507892,0.6882249,0.0001754172,0.001541666,0.0008064358,0.0001216354,0.0007828316,0.006768649],"genre_scores_gemma":[0.9506578,0.0001083618,0.04721714,0.0009266887,0.0007844913,0.00005088942,0.000101923,0.00009636342,0.00005629733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6498302,"threshold_uncertainty_score":0.9992892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0024225621037838,"score_gpt":0.2093538279120866,"score_spread":0.2069312658083028,"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."}}