{"id":"W4402136728","doi":"10.1007/978-3-031-61539-9_26","title":"Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Autoencoder; Bridge (graph theory); Computer science; Artificial intelligence; Pattern recognition (psychology); Structural engineering; Speech recognition; Engineering; Deep learning; Medicine; Anatomy","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":[0.000199945,0.0009031245,0.0006631741,0.0008783525,0.0002524547,0.0005126836,0.0007906269,0.0008274489,0.002710772],"category_scores_gemma":[0.0003741531,0.0004940204,0.0007526969,0.0007088812,0.0002006386,0.0007562448,0.0006045838,0.0006679307,0.001495396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000538378,"about_ca_system_score_gemma":0.0006084081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01296472,"about_ca_topic_score_gemma":0.02042626,"domain_scores_codex":[0.9998447,0.000006121444,0.00000470509,0.0000529662,0.00004489194,0.00004661576],"domain_scores_gemma":[0.9998761,0.00001693315,0.00001504587,0.00002017259,0.00005917181,0.0000125569],"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":[0.0001871834,0.0001290687,0.00523399,0.0001080453,0.0001476896,0.0001940567,0.00004810396,0.1776069,0.04734587,0.001425518,0.009206913,0.7583668],"study_design_scores_gemma":[0.000003132454,0.00003631695,0.003237213,0.00001003116,0.00003373723,0.00006092324,0.00001362312,0.986405,0.008174375,0.000644457,0.001370805,0.00001025765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1721898,0.001152931,0.8090694,0.0002543154,0.0003870333,0.0001002474,0.0008745276,0.005252275,0.01071948],"genre_scores_gemma":[0.8184271,0.0007807009,0.1531384,0.0001442571,0.0001414555,0.00006472666,0.00242213,0.0001963031,0.02468494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01296472,"threshold_uncertainty_score":0.02577853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007321928860851823,"score_gpt":0.1980207578379915,"score_spread":0.1906988289771397,"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."}}