{"id":"W4391546875","doi":"10.1007/978-3-031-34027-7_24","title":"Condition-Based Maintenance of Highway Bridges Using Q-learning and Considering Component Dependency","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Dependency (UML); Component (thermodynamics); Q-learning; Highway maintenance; Transport engineering; Engineering; Computer science; Artificial intelligence; Physics; Reinforcement 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001456157,0.0005840913,0.0006931758,0.0005996774,0.00003966831,0.00003925462,0.0001438903,0.0004866955,0.00004371641],"category_scores_gemma":[0.0001045027,0.0006453128,0.0001074384,0.00009565031,0.00006641512,0.00006426843,0.00007506766,0.001416817,0.00000236136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004708306,"about_ca_system_score_gemma":0.0000434353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006370945,"about_ca_topic_score_gemma":0.00009012426,"domain_scores_codex":[0.9982802,0.000008636576,0.0006021773,0.0004113305,0.0002630108,0.0004346575],"domain_scores_gemma":[0.9989514,0.0005554704,0.0001029914,0.0002382436,0.00005481004,0.00009707635],"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":[0.000006898376,0.000002143671,0.0002723887,0.004422752,0.00007308574,0.0001234546,0.00012723,0.9826989,0.005995453,0.002737734,0.000006863359,0.003533136],"study_design_scores_gemma":[0.0003795046,0.00007231069,0.0005493455,0.01315357,0.0001077739,0.0001459225,0.000004099898,0.9459203,0.02467661,0.0101717,0.003531684,0.001287205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2049792,0.08279628,0.6650214,0.0002671578,0.01151325,0.003519472,0.0004369821,0.0120466,0.01941972],"genre_scores_gemma":[0.9885105,0.0003869995,0.01047272,0.00000953965,0.0002371718,0.00001626293,0.00002216278,0.0002485888,0.00009606939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7835313,"threshold_uncertainty_score":0.9995998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132480607381317,"score_gpt":0.2480498305219956,"score_spread":0.2348017697838639,"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."}}