{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003588993,0.0004857166,0.000410947,0.001229413,0.0001572948,0.0003610343,0.0006490892,0.0005932901,0.002793008],"category_scores_gemma":[0.001276142,0.0002928539,0.0003220727,0.0004123114,0.0001463883,0.0005401825,0.0003981428,0.0003838255,0.0005799214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000176328,"about_ca_system_score_gemma":0.0002423613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000856335,"about_ca_topic_score_gemma":0.001724186,"domain_scores_codex":[0.9998006,0.00001966532,0.00001254177,0.00004205591,0.0001155693,0.000009552029],"domain_scores_gemma":[0.9993889,0.000246691,0.00008877251,0.00008154455,0.0001676736,0.00002644089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002680805,0.0002396727,0.006956314,0.0004079837,0.0001094386,0.0003800861,0.0002739397,0.1314018,0.2186295,0.00187534,0.002595996,0.6368619],"study_design_scores_gemma":[0.00001679381,0.0001564772,0.007071739,0.00002548014,0.00002077294,0.0002995932,0.00004360059,0.9470003,0.04169926,0.0008366118,0.002788896,0.00004050058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03416623,0.00007562096,0.9602852,0.00002909035,0.00001737957,0.00007165885,0.0002510514,0.004476011,0.0006277933],"genre_scores_gemma":[0.5166339,0.0001362048,0.4795445,0.0000582354,0.00002111413,0.0002361849,0.0007497972,0.0002622624,0.002357892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002793008,"threshold_uncertainty_score":0.009343565,"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."}}