{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002480824,0.0002655451,0.0003376124,0.0007540619,0.00007957328,0.0000599275,0.0003579909,0.00012933,0.000004554427],"category_scores_gemma":[0.00004828903,0.0002254926,0.0001419409,0.0005453269,0.00003205178,0.0002119935,0.0000284673,0.0005276919,1.382817e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002238867,"about_ca_system_score_gemma":0.00004580036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002843043,"about_ca_topic_score_gemma":0.000001345576,"domain_scores_codex":[0.998736,0.00001181279,0.0005751448,0.0001467547,0.0001492677,0.0003810562],"domain_scores_gemma":[0.9993711,0.0001845744,0.00009813951,0.0001783627,0.0001136167,0.00005420602],"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":[0.00001637458,0.000004426774,0.0004082563,0.000117674,0.00009257484,0.00000516508,0.000128389,0.9926898,0.00341393,0.0001560489,0.00002002045,0.002947335],"study_design_scores_gemma":[0.001785883,0.00003031592,0.00607621,0.0001966166,0.00004064968,0.00003886533,0.00003860845,0.975577,0.01579158,0.00003755451,0.0001792186,0.0002074897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1806634,0.0009683539,0.8165007,0.0000357389,0.001480665,0.0002321648,0.000003550262,0.0001006408,0.00001471073],"genre_scores_gemma":[0.5841705,0.00003481518,0.4156139,0.00001420238,0.0001001594,0.00001996794,6.850197e-7,0.00003851854,0.00000717068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4035071,"threshold_uncertainty_score":0.9195322,"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."}}