{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001286819,0.0002166981,0.0003630505,0.0002935803,0.00007371743,0.00002620711,0.0002905749,0.0001630686,0.000003951085],"category_scores_gemma":[0.00002321382,0.0002162901,0.00009277182,0.0002962249,0.0001046888,0.0001224642,0.00009996287,0.0002175899,4.239825e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084431,"about_ca_system_score_gemma":0.00002784709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002708479,"about_ca_topic_score_gemma":0.00000490483,"domain_scores_codex":[0.9986532,0.000115311,0.0005692562,0.0002160344,0.0001832702,0.0002629201],"domain_scores_gemma":[0.9991892,0.000228042,0.00009575572,0.0003567104,0.00007694357,0.00005332992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008066664,0.0000141283,0.02672092,0.001332555,0.0002115148,0.00001673279,0.0007686697,0.6432758,0.07185108,0.0002050055,0.0004960356,0.2543009],"study_design_scores_gemma":[0.00009857865,0.00005146755,0.571238,0.0002128804,0.0000308297,0.000008497577,0.00002392155,0.3632327,0.0647005,0.0002865132,0.000002842895,0.0001132363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594244,0.0006070043,0.03629676,0.000009602926,0.003013176,0.0002644754,0.00002476515,0.0003476359,0.00001222187],"genre_scores_gemma":[0.9928367,0.00001270044,0.006846136,0.00001127114,0.0002554353,0.000004011638,0.00000556782,0.00002534526,0.000002888683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5445172,"threshold_uncertainty_score":0.8820053,"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."}}