{"id":"W4297916564","doi":"10.2118/210406-ms","title":"Failure Pressure Prediction of Defective Pipeline Using Finite Element Method and Machine Learning Models","year":2022,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline transport; Pipeline (software); Artificial neural network; Machine learning; Artificial intelligence; Finite element method; Computer science; Approximation error; Computation; Failure mode and effects analysis; Engineering; Reliability engineering; Structural engineering; Algorithm; Mechanical engineering","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":[0.0005403076,0.0007042815,0.0005927585,0.001253373,0.0003031739,0.0005429944,0.0006354598,0.0009500554,0.0007749338],"category_scores_gemma":[0.001310538,0.0003834614,0.0007066797,0.0005660329,0.0002833261,0.0005300862,0.0002677316,0.0005127117,0.000139386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005854063,"about_ca_system_score_gemma":0.0008001049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02032581,"about_ca_topic_score_gemma":0.00751447,"domain_scores_codex":[0.9997174,0.00005600113,0.00002478739,0.00006539321,0.0001019814,0.00003442666],"domain_scores_gemma":[0.9992183,0.0003794138,0.0001067477,0.00004435247,0.0002174824,0.00003362805],"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":[0.00003745207,0.00005178949,0.005726203,0.00003375043,0.00001724405,0.00005163655,0.00002313191,0.9770931,0.002139693,0.0001961506,0.0002370342,0.01439282],"study_design_scores_gemma":[5.209877e-7,0.000004827371,0.0003683098,0.00000111059,9.01167e-7,0.000002377764,0.00000214944,0.9993361,0.0002353301,0.00002957075,0.00001740188,0.000001355209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5869141,0.0004162147,0.4085074,0.0002196035,0.00006083508,0.00005279247,0.0002460225,0.001195236,0.002387774],"genre_scores_gemma":[0.9854347,0.00006510902,0.0137686,0.00001246905,0.000005646585,0.00002318622,0.0001274902,0.000012396,0.0005505094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02032581,"threshold_uncertainty_score":0.04041499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319174325465715,"score_gpt":0.260050950798046,"score_spread":0.2268592075433889,"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."}}