{"id":"W4221041875","doi":"10.1016/j.engstruct.2022.114083","title":"Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning","year":2022,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Western University","funders":"","keywords":"Computer science; Machine learning; Test data; Generalization; Experimental data; Artificial intelligence; Bayesian optimization; Code (set theory); Artificial neural network; Generative grammar; Bayesian network; Synthetic data; Mathematics","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":[0.000236378,0.0003456984,0.0004122322,0.000263499,0.0002429441,0.00003765353,0.0008674522,0.0001098069,0.0001105479],"category_scores_gemma":[0.0002368617,0.0003949245,0.00006921982,0.000348224,0.00004125728,0.0002424091,0.000568428,0.0009508378,4.207557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002527357,"about_ca_system_score_gemma":0.00003236737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001505617,"about_ca_topic_score_gemma":2.187787e-7,"domain_scores_codex":[0.9980243,0.00003249898,0.0005450872,0.0003841248,0.0004706303,0.000543352],"domain_scores_gemma":[0.9987258,0.0002024688,0.0001315901,0.0007755538,0.00004182742,0.0001227037],"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":[0.000007728778,5.073834e-7,0.0001862968,0.0002405782,0.00006706705,0.000003251583,0.0004512499,0.9725695,0.02484827,0.0003391457,0.00001502352,0.001271427],"study_design_scores_gemma":[0.0002381328,0.00006427561,0.0018417,0.00007917878,0.00004037491,0.00006860412,0.0001826546,0.9848559,0.01148204,0.00001944242,0.0007721468,0.0003555668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628868,0.0005605775,0.03286664,0.000005982725,0.001471831,0.0003133077,0.0001537795,0.001671725,0.000069415],"genre_scores_gemma":[0.8941357,0.00001753471,0.105352,0.000005375743,0.0002582801,0.00001901311,0.00008398728,0.0001209993,0.000007192424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07248535,"threshold_uncertainty_score":0.9998503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796999785666278,"score_gpt":0.2586205071042506,"score_spread":0.2406505092475878,"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."}}