{"id":"W4323349501","doi":"10.1016/j.ress.2023.109214","title":"Hierarchical reinforcement learning for transportation infrastructure maintenance planning","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Elevator Systems and Control","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Reinforcement learning; Bridge (graph theory); Hierarchy; Computer science; Abstraction; Scale (ratio); State space; Bridge maintenance; Action (physics); Operations research; Risk analysis (engineering); Engineering; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001440254,0.0005851951,0.001283414,0.0004809143,0.0003868039,0.000539654,0.001297095,0.0008871872,0.003332951],"category_scores_gemma":[0.005492954,0.0005205093,0.0004179312,0.0004253389,0.001018405,0.001063143,0.001042845,0.001389274,0.0002571604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422283,"about_ca_system_score_gemma":0.00151697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01963092,"about_ca_topic_score_gemma":0.01399988,"domain_scores_codex":[0.9994804,0.0002299034,0.00002289801,0.00008128599,0.00008578375,0.00009974986],"domain_scores_gemma":[0.9964315,0.002810479,0.0001962798,0.0001195079,0.0002849431,0.0001573013],"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":[0.00004586706,0.0000334897,0.0002390995,0.00001967632,0.00001180619,0.00001130209,0.00001669901,0.9855289,0.0001621704,0.003259623,0.0003953903,0.01027608],"study_design_scores_gemma":[0.000006755507,0.000008498117,0.00003576272,0.000001063054,0.000001597326,8.780264e-7,0.000001254988,0.997493,0.00003333786,0.002391167,0.00002547711,0.00000117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0786463,0.0003139472,0.9166286,0.0004149161,0.000050977,0.00006579668,0.0001232243,0.0005517845,0.003204505],"genre_scores_gemma":[0.9532935,0.00009797542,0.04360616,0.00009305977,0.00003452468,0.0001008463,0.0001133507,0.00004214739,0.00261846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01963092,"threshold_uncertainty_score":0.03903329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004258769706325694,"score_gpt":0.1947607227715644,"score_spread":0.1905019530652387,"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."}}