{"id":"W4401418273","doi":"10.1016/j.tws.2024.112330","title":"Remaining useful life prediction of pipelines considering the crack coupling effect using genetic algorithm-back propagation neural network","year":2024,"lang":"en","type":"article","venue":"Thin-Walled Structures","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Nanhu Scholars Program for Young Scholars of Xinyang Normal University; Key Program of NSFC-Tongyong Union Foundation; National Aerospace Science Foundation of China","keywords":"Artificial neural network; Genetic algorithm; Backpropagation; Coupling (piping); Computer science; Algorithm; Pipeline transport; Materials science; Artificial intelligence; Engineering; Machine learning; Mechanical engineering; Composite material","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":[0.000338347,0.0003834374,0.0004141119,0.0001193542,0.0001951285,0.0001820718,0.0002371439,0.0002286454,0.00004048686],"category_scores_gemma":[0.0001580157,0.0002511322,0.0001580785,0.000387262,0.0001017786,0.0001496291,0.00006302726,0.0006564368,0.000002970477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007067628,"about_ca_system_score_gemma":0.00004530936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003475284,"about_ca_topic_score_gemma":0.000003976777,"domain_scores_codex":[0.9982221,0.00005915621,0.0005982208,0.0003273941,0.0003512568,0.0004419285],"domain_scores_gemma":[0.9989918,0.0004175536,0.00007539119,0.0003537303,0.00006119975,0.000100297],"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":[0.00001314937,0.000001226402,0.00009086076,0.0004206926,0.00014335,0.00000914885,0.0002934155,0.9909409,0.004557672,0.0001766332,0.0002120821,0.00314083],"study_design_scores_gemma":[0.0002457415,0.00005482373,0.003604205,0.0002476848,0.0001275754,0.00009620297,0.00004669935,0.9907698,0.001399417,0.002910987,0.0002562333,0.0002406521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7337247,0.00657158,0.2549909,0.00007023686,0.002816759,0.0005388107,0.00002017487,0.001170214,0.00009664198],"genre_scores_gemma":[0.9830081,0.00008465238,0.01580598,0.00002516405,0.0009403942,0.0000122763,0.00001484742,0.00009314245,0.00001543034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2492835,"threshold_uncertainty_score":0.9999941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440047022302106,"score_gpt":0.2230181441786742,"score_spread":0.2086176739556531,"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."}}