{"id":"W3216640330","doi":"10.1061/(asce)cp.1943-5487.0001003","title":"Multiclass Damage Identification in a Full-Scale Bridge Using Optimally Tuned One-Dimensional Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Convolutional neural network; Computer science; Pattern recognition (psychology); Initialization; Benchmark (surveying); Hyperparameter; Bridge (graph theory); Artificial intelligence; Identification (biology); Artificial neural network","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":[0.000340289,0.0005965272,0.0004084486,0.0007142346,0.0001680275,0.0003401267,0.0005960191,0.0005746062,0.0007280171],"category_scores_gemma":[0.0005300147,0.0002378704,0.0003971981,0.0003520506,0.0002112145,0.0007884714,0.0005898884,0.0003873622,0.0002329921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003324642,"about_ca_system_score_gemma":0.0002511205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00180022,"about_ca_topic_score_gemma":0.004271166,"domain_scores_codex":[0.9998337,0.0000142946,0.000008195854,0.00006629472,0.00004666084,0.00003089663],"domain_scores_gemma":[0.9998222,0.00003799336,0.0000406949,0.00004004289,0.00004450918,0.00001464199],"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":[0.0003492388,0.0002159369,0.01776676,0.0001341019,0.0001653031,0.0003722726,0.0001521139,0.2587144,0.162689,0.001447255,0.001463116,0.5565305],"study_design_scores_gemma":[0.000002572807,0.00005242182,0.006870766,0.00000629953,0.00001532466,0.00009736254,0.00002185456,0.9822412,0.009901308,0.0004444569,0.0003373732,0.000009072241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3907258,0.0006056543,0.6057557,0.0001022701,0.00006060168,0.00004610816,0.0001706184,0.0008290392,0.00170415],"genre_scores_gemma":[0.9355886,0.000159766,0.06222678,0.00004152076,0.00001734749,0.00002730104,0.0002397403,0.00002231985,0.001676493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00180022,"threshold_uncertainty_score":0.003579497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238205595130078,"score_gpt":0.273997051023553,"score_spread":0.2501764915105452,"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."}}