{"id":"W4409446843","doi":"10.1016/j.engstruct.2025.120276","title":"Generative adversarial network approach for predicting tensile behavior and failure pattern of fiber-reinforced cementitious matrices","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Arup Group (Canada); McMaster University","funders":"","keywords":"Ultimate tensile strength; Structural engineering; Adversarial system; Cementitious; Generative grammar; Fiber; Materials science; Composite material; Computer science; Engineering; Artificial intelligence","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":[0.0001047896,0.0002418266,0.0003175701,0.00015388,0.00006852388,0.00005591729,0.0001172308,0.0001175103,0.00003748906],"category_scores_gemma":[0.00003628194,0.0002417606,0.00005224607,0.0001991957,0.00002691368,0.0001192131,0.00005676749,0.0001060054,2.660103e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004077527,"about_ca_system_score_gemma":0.00001347182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000118656,"about_ca_topic_score_gemma":2.859415e-7,"domain_scores_codex":[0.9990249,0.000008072594,0.0003807592,0.0001776936,0.0001141718,0.0002944099],"domain_scores_gemma":[0.9995992,0.00006265556,0.00007219721,0.0001478719,0.00008922651,0.00002886844],"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":[0.00001208959,9.877572e-7,0.0001172849,0.0006758545,0.0002051546,6.654572e-7,0.0001682259,0.9382181,0.05605775,0.002470001,0.001436872,0.0006370353],"study_design_scores_gemma":[0.002112677,0.0001190451,0.001662402,0.0001968442,0.0003011949,0.000007171906,0.0001567208,0.7525126,0.2383889,0.00008688987,0.003771139,0.0006844441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3381781,0.0003679108,0.6580134,0.000009580243,0.001144401,0.001156387,0.0001635107,0.0003652689,0.0006014582],"genre_scores_gemma":[0.9585944,0.000009493247,0.04056148,0.00002128134,0.0003595011,0.0001605098,0.0001453931,0.00003872578,0.0001092533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6204163,"threshold_uncertainty_score":0.985871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005261492952402123,"score_gpt":0.2070365430175762,"score_spread":0.2017750500651741,"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."}}