{"id":"W4389637870","doi":"10.1016/j.autcon.2023.105237","title":"Reinforcement layout design for deep beams based on bi-objective topology optimization","year":2023,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Topology optimization; Reinforcement; Rebar; Structural engineering; Lexicographical order; Tension (geology); Computer science; Topology (electrical circuits); Optimal design; Compression (physics); Stability (learning theory); Mathematical optimization; Engineering; Mathematics; Materials science; Finite element method; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002444524,0.0001553785,0.0001549541,0.0007206593,0.00008532147,0.00002346362,0.00007304793,0.0001748012,0.00009139287],"category_scores_gemma":[0.0001430374,0.0001889858,0.00003778229,0.0006739374,0.00005259724,0.000198222,0.00000834529,0.0001030602,0.00003072986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002603105,"about_ca_system_score_gemma":0.00002667686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004105492,"about_ca_topic_score_gemma":0.000002364133,"domain_scores_codex":[0.9990457,0.00004352325,0.0003516037,0.0001967359,0.0001179492,0.0002444434],"domain_scores_gemma":[0.9994146,0.0002544669,0.00006320571,0.0001645121,0.00006959715,0.00003365908],"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":[0.00001672545,0.000004859214,0.0001477643,0.0000314219,0.00001330614,6.304908e-7,0.0001711651,0.994817,0.00006589301,0.001960659,0.0001649958,0.002605649],"study_design_scores_gemma":[0.0007689588,0.00006423439,0.0005189875,0.00002590672,0.000009496894,0.000004723028,0.0001690255,0.9964418,0.001396189,0.0003536083,0.00007792429,0.000169117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003599745,0.000006955486,0.9917609,0.0001354004,0.00155134,0.0006483871,0.000005776142,0.001455589,0.0008358792],"genre_scores_gemma":[0.7341565,0.00001924406,0.2647929,0.00005629267,0.00009008131,0.0005806365,0.0002150796,0.00005412937,0.00003516785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7305567,"threshold_uncertainty_score":0.7706616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01116537176591678,"score_gpt":0.2319019546023781,"score_spread":0.2207365828364613,"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."}}