{"id":"W4411735843","doi":"10.1016/j.jobe.2025.113198","title":"A graph-based multi-objective genetic algorithm for optimizing the structural performance in double-layer shell structures","year":2025,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Shell (structure); Algorithm; Genetic algorithm; Graph; Layer (electronics); Materials science; Theoretical computer science; Composite material; Machine learning","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":[0.0005080688,0.0009409407,0.0006104201,0.0009032699,0.00044398,0.0006052652,0.0009051175,0.001049957,0.001289452],"category_scores_gemma":[0.0009050217,0.0003640505,0.0007114618,0.0007869652,0.0004642301,0.0004717474,0.000534057,0.0006568872,0.0001821313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008410845,"about_ca_system_score_gemma":0.001184087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006545088,"about_ca_topic_score_gemma":0.006313951,"domain_scores_codex":[0.9998393,0.00004664099,0.000006083947,0.00003207053,0.00005520494,0.00002075],"domain_scores_gemma":[0.9998042,0.0001119355,0.0000220786,0.000009713132,0.00004121031,0.00001090533],"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":[0.00002134088,0.00003958254,0.0003925123,0.0000285459,0.00003088129,0.00003640857,0.00003765548,0.9597874,0.002899478,0.002901824,0.0003955978,0.03342883],"study_design_scores_gemma":[0.000008008337,0.00002170993,0.00005681895,0.000003816225,0.000006227158,0.000007857299,0.000005327061,0.9986686,0.0004577885,0.0005290086,0.0002320718,0.000002824237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04741431,0.0002831833,0.9478321,0.0001571572,0.00004330016,0.0001070956,0.00004673882,0.0003425466,0.003773523],"genre_scores_gemma":[0.3972476,0.000261695,0.5985655,0.0001442293,0.00002091141,0.0003867473,0.0001754341,0.00007864347,0.003119202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006545088,"threshold_uncertainty_score":0.01301402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008692568488126329,"score_gpt":0.2406889142776596,"score_spread":0.2319963457895332,"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."}}