{"id":"W2985572923","doi":"10.1080/15623599.2019.1686836","title":"BIM-integrated TOPSIS-Fuzzy framework to optimize selection of sustainable building components","year":2019,"lang":"en","type":"article","venue":"International Journal of Construction Management","topic":"BIM and Construction Integration","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Multiple-criteria decision analysis; TOPSIS; Sustainability; Computer science; Decision support system; Ideal solution; Process (computing); Fuzzy logic; Building information modeling; Analytic hierarchy process; Management science; Selection (genetic algorithm); Systems engineering; Operations research; Risk analysis (engineering); Engineering; Data mining; Machine learning; Artificial intelligence; Operations management","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":[0.00021411,0.0001415276,0.0002158995,0.000792869,0.0000316374,0.00008120535,0.0002733596,0.00007666751,0.000472374],"category_scores_gemma":[0.00002555455,0.0001399592,0.0001214666,0.0003871274,0.00003121379,0.0003773623,0.00004032062,0.000224102,0.00002418081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003278051,"about_ca_system_score_gemma":0.00002585944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001519506,"about_ca_topic_score_gemma":0.000001006015,"domain_scores_codex":[0.998561,0.00003040265,0.0006217468,0.0001220362,0.0005062323,0.0001586268],"domain_scores_gemma":[0.9986815,0.00003436378,0.0002533413,0.000101355,0.000860006,0.00006942044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003688918,0.00007220777,0.01059177,0.0001144702,0.001299992,0.00002795048,0.0001827468,0.2389533,0.01252894,0.6551393,0.001241633,0.07947881],"study_design_scores_gemma":[0.01322355,0.001555266,0.0650676,0.004847901,0.0009148979,0.003847853,0.0327946,0.1031103,0.1852682,0.1660839,0.4202707,0.003015143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6035414,0.00004458417,0.383617,0.0002405808,0.005084361,0.0002508981,0.000004993656,0.00006355396,0.007152593],"genre_scores_gemma":[0.8987803,0.00006625422,0.1005947,0.00004895669,0.0001722783,0.000006199819,0.000003846137,0.00001590179,0.0003115597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4890554,"threshold_uncertainty_score":0.570737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004976882418034429,"score_gpt":0.2247198118053141,"score_spread":0.2197429293872797,"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."}}