{"id":"W4323051531","doi":"10.1007/s11440-023-01850-3","title":"Development and application of a novel probabilistic back-analysis framework for geotechnical parameters in shield tunneling based on the surrogate model and Bayesian theory","year":2023,"lang":"en","type":"article","venue":"Acta Geotechnica","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Randomness; Probabilistic logic; Surrogate model; Finite element method; Engineering; Bayesian inference; Displacement (psychology); Geotechnical engineering; Bayesian probability; Structural engineering; Computer science; Mathematics; Machine learning; Artificial intelligence; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.001096475,0.0002163054,0.0003900194,0.0004097719,0.00006457919,0.00002503359,0.0002356239,0.0002510489,0.000002509419],"category_scores_gemma":[0.0005319948,0.0001759681,0.0001272927,0.001333573,0.00008298684,0.00003381767,0.0000644305,0.0003536286,0.0000012921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004234011,"about_ca_system_score_gemma":0.00002008167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009445327,"about_ca_topic_score_gemma":0.00001102469,"domain_scores_codex":[0.9986838,0.00001972882,0.0004548281,0.0003521784,0.0001805188,0.000308945],"domain_scores_gemma":[0.9979832,0.001368061,0.00006414342,0.0004883033,0.0000246586,0.00007164081],"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":[0.00001449791,0.00003162317,0.000007656972,0.00008818109,0.0001101737,2.88488e-7,0.00006865392,0.991394,0.001143735,0.005248271,0.000007003663,0.001885936],"study_design_scores_gemma":[0.0001728799,0.00001794294,0.0002535752,0.00007198131,0.0001819055,3.647318e-7,0.00004027879,0.9947841,0.0003684183,0.003833722,0.00007971664,0.0001950965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606043,0.00002444464,0.8380612,0.0005959193,0.000007819329,0.0004047939,0.00001530946,0.0002713994,0.0000147573],"genre_scores_gemma":[0.9413511,0.00002307412,0.05818229,0.00005757352,0.000005310297,0.0003249233,0.00001714808,0.00003289537,0.000005651847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7807468,"threshold_uncertainty_score":0.717577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960672890830058,"score_gpt":0.2340248344392949,"score_spread":0.2144181055309944,"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."}}