{"id":"W4413114555","doi":"10.18280/ts.420409","title":"Wavelet-Statistic-Frame with LSTM-Attention Network for Continuous Damage Identification and Localization in Bridge Vibration Signals","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistic; Frame (networking); Wavelet; Bridge (graph theory); Identification (biology); Computer science; Vibration; Speech recognition; Pattern recognition (psychology); Continuous wavelet transform; Artificial intelligence; Wavelet transform; Mathematics; Acoustics; Statistics; Discrete wavelet transform; Telecommunications; Physics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004168635,0.0009174362,0.0005107202,0.0004040137,0.0002131611,0.0003870553,0.001252865,0.0008026899,0.001694429],"category_scores_gemma":[0.0009432193,0.0002957388,0.0005952598,0.0005332614,0.0002751952,0.0009879537,0.0007025707,0.001148409,0.0004801899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006940475,"about_ca_system_score_gemma":0.0007528662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008061503,"about_ca_topic_score_gemma":0.01101685,"domain_scores_codex":[0.999815,0.0000252854,0.0000102083,0.00007114185,0.00003981687,0.00003851429],"domain_scores_gemma":[0.9998266,0.00006209911,0.00002152639,0.00001913064,0.00005832587,0.00001229049],"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":[0.0003037457,0.0002188151,0.001918806,0.000171062,0.0001472988,0.0002428784,0.0001051306,0.4241929,0.03079838,0.003743287,0.006188753,0.5319691],"study_design_scores_gemma":[0.000004071393,0.00003389482,0.0002648877,0.000004261586,0.00001413833,0.00001702326,0.000006004219,0.9957922,0.002271336,0.001236649,0.0003508818,0.000004631681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08255548,0.001657457,0.9081403,0.000497175,0.0002337655,0.00005791083,0.0004099698,0.003326624,0.003121373],"genre_scores_gemma":[0.8970042,0.0006424483,0.09537424,0.0003544426,0.0001086113,0.0001156942,0.001015467,0.00008894371,0.005295874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008061503,"threshold_uncertainty_score":0.01602918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180352792194317,"score_gpt":0.2655310492409102,"score_spread":0.253727521318967,"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."}}