{"id":"W4391292004","doi":"10.1016/j.oceaneng.2024.116839","title":"A fragility-based framework for identification of unfavorable impact location for bridge columns under barge collisions","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Structural Response to Dynamic Loads","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Guangxi Key Research and Development Program; Natural Science Foundation of Chongqing; National Natural Science Foundation of China; Henan Province Science and Technology Innovation Talent Program; Hunan Provincial Science and Technology Department; Key Project of Research and Development Plan of Hunan Province","keywords":"BARGE; Pier; Fragility; Structural engineering; OpenSees; Bridge (graph theory); Column (typography); Engineering; Precast concrete; Marine engineering; Finite element method","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":[0.001264715,0.001021215,0.001235395,0.002243352,0.0008206403,0.001746145,0.002281921,0.001798139,0.003383841],"category_scores_gemma":[0.003363175,0.0005871648,0.0009631027,0.001003521,0.001221415,0.001311839,0.002039431,0.001131435,0.0005193522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008929967,"about_ca_system_score_gemma":0.001639757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01582121,"about_ca_topic_score_gemma":0.01130909,"domain_scores_codex":[0.9995129,0.0001102152,0.00002074018,0.0001184883,0.0001298392,0.0001078373],"domain_scores_gemma":[0.9989635,0.0005058279,0.0001555219,0.00005658419,0.0002464532,0.00007203942],"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":[0.00003787158,0.00005145944,0.001583785,0.000036909,0.00002730304,0.0001076035,0.0000458945,0.9661316,0.001495638,0.01367056,0.0004728796,0.01633844],"study_design_scores_gemma":[0.000001046828,0.000008216234,0.0001904808,0.000003454204,0.000002795052,0.000006138512,0.000008587995,0.9972427,0.00006511436,0.00241239,0.00005531894,0.00000382113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0240126,0.000148769,0.9732653,0.0001293668,0.00001820152,0.00004928703,0.0001383287,0.0001448676,0.002093205],"genre_scores_gemma":[0.8889493,0.0002838321,0.106702,0.00008103474,0.00008217108,0.0001997,0.0004113506,0.00005755168,0.003232958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01582121,"threshold_uncertainty_score":0.03145826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241609302113235,"score_gpt":0.2806407195470162,"score_spread":0.2682246265258839,"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."}}