{"id":"W3046970727","doi":"10.1016/j.commatsci.2020.109962","title":"An artificial neural network modeling approach for short and long fatigue crack propagation","year":2020,"lang":"en","type":"article","venue":"Computational Materials Science","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Paris' law; Extrapolation; Artificial neural network; Structural engineering; Damage tolerance; Fracture mechanics; Materials science; Nonlinear system; Crack closure; Computer science; Engineering; Machine learning; Mathematics; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000345054,0.0004606241,0.0004875717,0.0003373348,0.0004337369,0.0006446922,0.001166516,0.001392428,0.002354091],"category_scores_gemma":[0.001095424,0.0004404675,0.0004811651,0.0004403111,0.0002767119,0.001044799,0.0005075785,0.0009340401,0.0003824241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005518398,"about_ca_system_score_gemma":0.0007177219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01539001,"about_ca_topic_score_gemma":0.01447132,"domain_scores_codex":[0.9999118,0.00002336385,0.000005279926,0.00002017343,0.00002820711,0.00001112095],"domain_scores_gemma":[0.9997191,0.0001405522,0.00002298169,0.00001340294,0.00009113314,0.00001276722],"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":[0.000009451421,0.0000142314,0.0001014912,0.00001237462,0.000009565631,0.00001839808,0.000008439982,0.9865595,0.0004802117,0.003333201,0.0002337464,0.009219383],"study_design_scores_gemma":[3.177126e-7,0.000001015657,0.000008102556,4.907253e-7,6.409982e-7,7.636312e-7,4.617694e-7,0.9996372,0.00002675975,0.0002778287,0.00004598859,5.137281e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03765706,0.0007397467,0.9506653,0.0003937272,0.0001495763,0.00003918503,0.0001262104,0.0002444533,0.009984653],"genre_scores_gemma":[0.8193331,0.000900862,0.1543863,0.0002158677,0.0001702019,0.0001922061,0.0002424348,0.0001467402,0.02441213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01539001,"threshold_uncertainty_score":0.03060085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06335637116617901,"score_gpt":0.2723831539361146,"score_spread":0.2090267827699356,"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."}}