{"id":"W4408405290","doi":"10.1016/j.engstruct.2025.120018","title":"Prediction of ground movement-induced pipe responses considering variable PGD magnitudes using physics-informed neural networks and transfer learning","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Variable (mathematics); Movement (music); Physics; Computer science; Artificial intelligence; Acoustics; Mathematics; Mathematical analysis","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":[0.0001129347,0.0003709649,0.0004012984,0.0002324524,0.0001215082,0.0000927582,0.0001398227,0.0002191461,0.00001182063],"category_scores_gemma":[0.0001406621,0.0003870646,0.00007097937,0.0004557464,0.00004653564,0.000218856,0.00004787855,0.0005770631,9.605432e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009358943,"about_ca_system_score_gemma":0.00003420126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006332099,"about_ca_topic_score_gemma":0.000001598024,"domain_scores_codex":[0.9986781,0.00002157768,0.0004333723,0.0002530849,0.0001786995,0.0004351341],"domain_scores_gemma":[0.9993768,0.0002373424,0.00002870965,0.0002171806,0.00005614422,0.00008382885],"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":[0.00002149103,0.000003030145,0.0001368393,0.0004275493,0.0001407842,0.000002270822,0.00009014094,0.9148867,0.07799336,0.004850085,0.000006855855,0.001440904],"study_design_scores_gemma":[0.0005592325,0.00005004887,0.006905194,0.0001692026,0.00008240964,0.00001458182,0.00006172233,0.9752539,0.01521689,0.001219834,0.0001683645,0.0002985881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5875336,0.0006595064,0.4103315,0.000006531022,0.0006092045,0.000153305,0.00001069368,0.0005757328,0.0001199042],"genre_scores_gemma":[0.9926988,0.00005644066,0.006994022,0.00002226488,0.0001191901,0.00001196548,0.00001188981,0.00005560313,0.00002983619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4051652,"threshold_uncertainty_score":0.9998581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123467432793463,"score_gpt":0.2124743490318623,"score_spread":0.200127605752516,"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."}}