{"id":"W4377145754","doi":"10.1016/j.istruc.2023.05.009","title":"Structural damage severity classification from time-frequency acceleration data using convolutional neural networks","year":2023,"lang":"en","type":"article","venue":"Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Convolutional neural network; Computer science; Pattern recognition (psychology); Accelerometer; Classifier (UML); Artificial intelligence; Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002175077,0.000607312,0.0003256289,0.001160447,0.0001328104,0.0003075944,0.0003465654,0.0004373967,0.0008976031],"category_scores_gemma":[0.0006320245,0.0002068056,0.0003977836,0.0006590799,0.0001397298,0.0003581084,0.000307192,0.0004656597,0.0004199332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003079509,"about_ca_system_score_gemma":0.0002777406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006983744,"about_ca_topic_score_gemma":0.01212002,"domain_scores_codex":[0.999899,0.000007818857,0.000006071449,0.0000284218,0.00003151967,0.00002718093],"domain_scores_gemma":[0.999756,0.00007230086,0.00004839758,0.0000267089,0.00007941355,0.00001721719],"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":[0.0003781428,0.0003714575,0.02473866,0.0001060912,0.000122032,0.0001823894,0.00006245504,0.1833806,0.06091689,0.0009243897,0.003100935,0.7257159],"study_design_scores_gemma":[0.000003741898,0.0000512896,0.01368073,0.00001306298,0.00002355705,0.00004479305,0.00001754881,0.9795019,0.005600457,0.0005863641,0.0004683913,0.000008116757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.607738,0.001068309,0.3849557,0.0002503704,0.0001560051,0.00006111892,0.001168156,0.001418804,0.003183556],"genre_scores_gemma":[0.9671601,0.0003476976,0.02824755,0.00003513474,0.00004890356,0.00002107834,0.001077099,0.00002151021,0.003040852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006983744,"threshold_uncertainty_score":0.01388621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101534631015862,"score_gpt":0.3376775708056036,"score_spread":0.2361429397897416,"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."}}