{"id":"W4401632252","doi":"10.22215/etd/2024-16109","title":"Strengthening Beams using FRCM Machine Learning Approach and Numerical Models","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Robustness (evolution); Structural engineering; Finite element method; Beam (structure); Mortar; Computer science; Reinforcement; Artificial intelligence; Machine learning; Engineering; Materials science; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004742499,0.0005968664,0.0004638833,0.0006942148,0.0003306712,0.0006328992,0.001006663,0.001230786,0.001809382],"category_scores_gemma":[0.001121157,0.0004128774,0.0007575325,0.0005457056,0.0003706358,0.0005359384,0.000460071,0.0009073871,0.0004783401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006696273,"about_ca_system_score_gemma":0.0007904186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171138,"about_ca_topic_score_gemma":0.008451658,"domain_scores_codex":[0.9998103,0.00004302057,0.0000117369,0.00003887541,0.00007509063,0.00002113462],"domain_scores_gemma":[0.9994622,0.0002471183,0.00009268882,0.00003328943,0.000149127,0.00001553545],"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":[0.000006054123,0.00001507727,0.0002514003,0.00001289133,0.000004866462,0.00001372223,0.000008384198,0.9917823,0.0005855004,0.0009470969,0.00008766129,0.006285115],"study_design_scores_gemma":[3.177547e-7,0.000002366807,0.0000258135,0.000001175802,5.233941e-7,0.000001171928,7.54585e-7,0.9997125,0.00009803845,0.0001024353,0.000054062,8.229458e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06800956,0.0004154968,0.9203668,0.0002151061,0.00005201386,0.0001264417,0.0002251333,0.00083541,0.009754037],"genre_scores_gemma":[0.7797844,0.0004927801,0.2111441,0.0001054002,0.00004260089,0.0004194493,0.0004730754,0.00008884031,0.007449376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01171138,"threshold_uncertainty_score":0.02328646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697510133392366,"score_gpt":0.2416371132877744,"score_spread":0.2246620119538507,"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."}}