{"id":"W4407436737","doi":"10.1016/j.compstruc.2025.107672","title":"Prediction of hysteresis response of steel braces using long Short-Term memory artificial neural networks","year":2025,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Canadian Institute of Steel Construction","keywords":"Artificial neural network; Long short term memory; Term (time); Hysteresis; Structural engineering; Computer science; Short-term memory; Control theory (sociology); Engineering; Artificial intelligence; Recurrent neural network; Psychology; Working memory; Neuroscience; Physics; Control (management)","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":[0.0002592845,0.0004933099,0.0002439259,0.000247334,0.000115127,0.0002675869,0.0004249282,0.0004933804,0.0008927381],"category_scores_gemma":[0.0007847677,0.0001815088,0.0003235663,0.000205186,0.0001636626,0.0003467774,0.0002129558,0.000424966,0.0001916441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926775,"about_ca_system_score_gemma":0.000308626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003251275,"about_ca_topic_score_gemma":0.006524906,"domain_scores_codex":[0.9999263,0.0000153365,0.000005367587,0.00001896002,0.00002300334,0.00001104438],"domain_scores_gemma":[0.9997941,0.00007847779,0.0000297421,0.00001897054,0.00007062358,0.000008011638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008481544,0.00007281869,0.001599287,0.00006988214,0.00003679027,0.00005995508,0.00002300558,0.927469,0.02101488,0.0005706112,0.0004185973,0.04858036],"study_design_scores_gemma":[0.000001006089,0.00001578309,0.0003521796,0.000001723605,0.000001863309,0.000003584976,0.000001859519,0.9977925,0.001656957,0.0001041878,0.00006639646,0.000001959398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4136115,0.0004074137,0.5807736,0.0002051778,0.00008972543,0.00005891024,0.0004664922,0.0008037602,0.003583506],"genre_scores_gemma":[0.9780351,0.0001098227,0.01983092,0.00002528228,0.000006404217,0.00004714732,0.0003193571,0.00001269966,0.001613294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003251275,"threshold_uncertainty_score":0.00646472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274019531802537,"score_gpt":0.2918460409529935,"score_spread":0.2591058456349681,"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."}}