{"id":"W3153543505","doi":"10.1080/13287982.2021.1908710","title":"A Data-Driven Damage Assessment Tool for Truss-Type Railroad Bridges Using Train Induced Strain Time-History Response","year":2021,"lang":"en","type":"article","venue":"Australian Journal of Structural Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Truss; Structural engineering; Bridge (graph theory); Truss bridge; Parametric statistics; Finite element method; Engineering; Noise (video); Strain (injury); Computer science; Mathematics; Statistics; 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":[0.0006824132,0.000413805,0.0006179011,0.0002925386,0.00007580493,0.00008354791,0.0006722415,0.0002157662,0.0001173096],"category_scores_gemma":[0.000344955,0.0004266812,0.000164935,0.0002643439,0.00003653086,0.0007756777,0.00008595314,0.0007725488,0.000001241958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213208,"about_ca_system_score_gemma":0.0004040664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008076774,"about_ca_topic_score_gemma":0.000001148929,"domain_scores_codex":[0.9976467,0.00009482999,0.0009453314,0.0003093081,0.0003961101,0.0006077123],"domain_scores_gemma":[0.9983968,0.0002488025,0.0002384628,0.0005800363,0.0002785505,0.0002573594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001614142,0.000005922662,0.0001598753,0.0003419793,0.0002144052,0.0002532944,0.0003022533,0.1321984,0.8589345,0.00005299081,0.001325959,0.006049075],"study_design_scores_gemma":[0.003451706,0.0008966769,0.3143259,0.001454731,0.0004247497,0.003380759,0.0004419199,0.581737,0.07914468,0.0002067338,0.01229951,0.002235654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946292,0.000207896,0.001657718,0.0001352384,0.002602268,0.0002702788,0.0002476065,0.0002446772,0.000005099396],"genre_scores_gemma":[0.8645781,0.00001423992,0.1344837,0.0000159177,0.00068116,0.000003647439,0.00005472467,0.0000980229,0.00007047079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7797897,"threshold_uncertainty_score":0.9998185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08227174451795047,"score_gpt":0.3509235040179468,"score_spread":0.2686517594999963,"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."}}