{"id":"W4402148858","doi":"10.1007/978-3-031-61527-6_29","title":"Artificial Neural Network-Based Hysteresis Model for Steel Braces in Concentrically Braced Frames","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Structural engineering; Artificial neural network; Hysteresis; Computer science; Braced frame; Brace; Engineering; Artificial intelligence; Computer network; Physics; Frame (networking)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001562311,0.0007969146,0.0008534307,0.0005821791,0.00003626749,0.00009763028,0.0003707868,0.001016001,0.00002475712],"category_scores_gemma":[0.0001446953,0.0008577866,0.0002214041,0.000216797,0.00003158108,0.00009492919,0.00006826035,0.001641174,0.000004457792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006663375,"about_ca_system_score_gemma":0.00006810013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001373766,"about_ca_topic_score_gemma":0.0006541553,"domain_scores_codex":[0.9973493,0.000008830152,0.0008550743,0.0006030928,0.0003015022,0.0008822342],"domain_scores_gemma":[0.9983472,0.0009804302,0.00008100806,0.0004206269,0.00004740427,0.0001233669],"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":[0.00003124429,0.000002995052,0.00003287127,0.001314639,0.00003255239,0.00003400943,0.000118401,0.9806935,0.0001887839,0.001639781,0.00008527171,0.01582595],"study_design_scores_gemma":[0.0001737081,0.00004281682,0.00007616142,0.001735519,0.00003184747,0.000003663024,3.314947e-7,0.9756635,0.0007755836,0.01966673,0.001073647,0.0007565031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01894705,0.03248623,0.9209486,0.0006521564,0.009748613,0.005302936,0.0005074039,0.007904996,0.003502001],"genre_scores_gemma":[0.9795666,0.0001178313,0.0179674,0.0000835726,0.001266548,0.0001971546,0.00004608507,0.0004351792,0.0003195783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606196,"threshold_uncertainty_score":0.9993873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02221430456959781,"score_gpt":0.2582142461173867,"score_spread":0.2359999415477889,"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."}}