{"id":"W2302626862","doi":"10.1061/(asce)cf.1943-5509.0000886","title":"Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models","year":2016,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Artificial neural network; Robustness (evolution); Backpropagation; Engineering; Pipeline (software); Forensic engineering; Civil engineering; Reliability engineering; Computer science; Artificial intelligence; Environmental engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006790639,0.0008208483,0.0003883801,0.0007408261,0.0003414119,0.0006394071,0.0007886645,0.0006658676,0.0006396387],"category_scores_gemma":[0.00211484,0.0001967626,0.0005048084,0.0005719823,0.00029776,0.000547599,0.0002625127,0.0003139787,0.00008568705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003052186,"about_ca_system_score_gemma":0.001326473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1836319,"about_ca_topic_score_gemma":0.1213996,"domain_scores_codex":[0.9998016,0.00005534269,0.0000135802,0.00004009794,0.00005365113,0.00003572978],"domain_scores_gemma":[0.9991965,0.000395455,0.000104957,0.0000295647,0.0002435871,0.00002997717],"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":[0.00002963619,0.00002488303,0.003402202,0.00001004606,0.00001188074,0.00001877684,0.000007891142,0.9916972,0.0002164977,0.0001320038,0.00009040671,0.004358495],"study_design_scores_gemma":[9.2935e-7,0.000009752671,0.0006911506,9.760798e-7,0.000002440347,0.00000139826,0.000003220998,0.999108,0.0001185708,0.00004158948,0.00002019176,0.000001776135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9326264,0.0003380792,0.06252155,0.0002188644,0.00002772589,0.00004834564,0.0004336366,0.0002458132,0.003539631],"genre_scores_gemma":[0.9943151,0.00008140849,0.004552197,0.000009390164,0.000003413542,0.00002046532,0.0001870849,0.000005442701,0.0008254815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1836319,"threshold_uncertainty_score":0.365126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776567536603788,"score_gpt":0.2219707917879766,"score_spread":0.1942051164219387,"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."}}