{"id":"W4392193629","doi":"10.1080/15732479.2024.2320686","title":"Damage detection for structural health monitoring using reinforcement and imitation learning","year":2024,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Structural health monitoring; Reinforcement; Imitation; Reinforcement learning; Computer science; Forensic engineering; Engineering; Risk analysis (engineering); Artificial intelligence; Structural engineering; Psychology; Business; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001230958,0.0004038537,0.0003184874,0.0003311627,0.0002626224,0.0002238983,0.00008702504,0.0002007369,0.000004811436],"category_scores_gemma":[0.00004309502,0.0003901969,0.00005104588,0.0002569287,0.00003368866,0.000474054,0.00004661149,0.000600233,1.301219e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003106481,"about_ca_system_score_gemma":0.00002946807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002531008,"about_ca_topic_score_gemma":0.000002093815,"domain_scores_codex":[0.9985359,0.00001584933,0.0003975695,0.0003694603,0.000174624,0.0005065788],"domain_scores_gemma":[0.9995034,0.00009409591,0.00004939135,0.0001434297,0.00004319479,0.0001664265],"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":[0.00002246087,3.962981e-7,0.001942521,0.004328291,0.0001409519,0.000005057223,0.002582337,0.5877197,0.08828486,0.0009542406,0.00005252834,0.3139666],"study_design_scores_gemma":[0.0002392763,0.0001338359,0.0292672,0.0003829797,0.00003520367,0.0001276373,0.0001439583,0.9432865,0.0224469,0.00143219,0.002029724,0.0004745857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444727,0.004067403,0.04708403,0.00001815706,0.002566769,0.0004796118,0.00002191754,0.001281289,0.000008117704],"genre_scores_gemma":[0.9686071,0.0002615199,0.03002729,0.00001178338,0.0009418749,0.00002222792,0.00001907934,0.000104782,0.000004303841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3555668,"threshold_uncertainty_score":0.999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009566782830912642,"score_gpt":0.27266200814616,"score_spread":0.2630952253152474,"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."}}