{"id":"W4404083128","doi":"10.1016/j.advengsoft.2024.103796","title":"Multi-feature driven seismic damage state identification for reinforced concrete shear walls using computer vision and machine learning","year":2024,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Feature (linguistics); Reinforced concrete; Shear (geology); Identification (biology); Shear wall; Structural engineering; Computer science; Artificial intelligence; State (computer science); Geology; Pattern recognition (psychology); Engineering; Machine learning; Geotechnical engineering; Algorithm","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":[0.0001205209,0.0002336513,0.000208352,0.000191225,0.00006058506,0.0001108589,0.0000955733,0.00008858342,0.000001324393],"category_scores_gemma":[0.0000505735,0.0002321482,0.00004723475,0.0001817307,0.0000198321,0.0006656959,0.0000394774,0.0003769122,0.000001607851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271784,"about_ca_system_score_gemma":0.00000831266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005184255,"about_ca_topic_score_gemma":0.00000182182,"domain_scores_codex":[0.9990793,0.000008015068,0.0002341713,0.0002642704,0.00009570698,0.0003185766],"domain_scores_gemma":[0.9996387,0.0001242919,0.00002572731,0.0001280283,0.00003332986,0.0000499399],"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":[0.000005996171,2.874758e-7,0.0002085267,0.0008167865,0.00001686601,0.00001573299,0.0003912335,0.9641109,0.02173643,0.00002031395,0.00001157891,0.01266537],"study_design_scores_gemma":[0.0002851993,0.00003031475,0.0004514799,0.0006978484,0.00001208469,0.00001492202,0.00001625616,0.9881102,0.00284336,0.00001584125,0.007263476,0.0002589854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1408949,0.004075272,0.852822,0.000007466702,0.001302441,0.0002363997,0.00002585122,0.0006339955,0.000001662924],"genre_scores_gemma":[0.7812642,0.000738758,0.2175146,0.000009920155,0.0002020679,0.00002724043,0.00005883643,0.00009713437,0.00008719537],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6403693,"threshold_uncertainty_score":0.946673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005041855417475887,"score_gpt":0.2370972371057791,"score_spread":0.2320553816883032,"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."}}