{"id":"W4381988369","doi":"10.1016/j.soildyn.2023.108105","title":"Fuzzy-logic framework for updating the seismic fragility of deteriorating bridges via visual inspections","year":2023,"lang":"en","type":"article","venue":"Soil Dynamics and Earthquake Engineering","topic":"Concrete Corrosion and Durability","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Government of Ontario; McMaster University","keywords":"Fragility; Bridge (graph theory); Vulnerability (computing); Fuzzy logic; Vulnerability assessment; Ranking (information retrieval); Computer science; Parameterized complexity; Incremental Dynamic Analysis; Set (abstract data type); Engineering; Seismic analysis; Reliability engineering; Forensic engineering; Structural engineering; Artificial intelligence; Algorithm; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003708378,0.0001720592,0.0002141823,0.00008830186,0.0001711875,0.00005263987,0.000101647,0.0001031239,0.000005084013],"category_scores_gemma":[0.0003289281,0.0001539019,0.00009347568,0.0003697683,0.0000451894,0.00008140795,0.00006288421,0.0002541321,0.000003106677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000393769,"about_ca_system_score_gemma":0.00001295887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002859712,"about_ca_topic_score_gemma":0.00007491311,"domain_scores_codex":[0.9990751,0.00001417285,0.0003134572,0.0001965741,0.0001076685,0.0002930048],"domain_scores_gemma":[0.9992329,0.0003851306,0.00004082951,0.0002118296,0.00004868813,0.00008063221],"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":[0.000008031862,0.000007751711,0.003097684,0.0004128012,0.00003021891,0.00000149276,0.0006468968,0.929129,0.003266645,0.01121496,0.00001185049,0.05217269],"study_design_scores_gemma":[0.0001077706,0.00003279635,0.03622793,0.00004803007,0.00001158513,0.000004078925,0.0004217561,0.9608297,0.0002420229,0.001861737,0.00005147225,0.0001611381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7346967,0.00007659832,0.2641391,0.00009182741,0.000376054,0.0001528078,0.0000323304,0.0003795188,0.0000550167],"genre_scores_gemma":[0.9976059,0.00006029337,0.002114619,0.00002330579,0.00008680152,0.00003800565,0.00002619429,0.0000353089,0.000009577897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2629091,"threshold_uncertainty_score":0.6275936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009447550115281637,"score_gpt":0.2330350721796894,"score_spread":0.2235875220644077,"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."}}