{"id":"W4413811387","doi":"10.1016/j.engstruct.2025.121265","title":"Robust and efficient dual-graph neural networks for structural damage detection and localization","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Dual (grammatical number); Artificial neural network; Computer science; Graph; Artificial intelligence; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006531681,0.0002443241,0.0001958968,0.000226371,0.0001518162,0.00007684177,0.0000675288,0.0001542644,0.000001287072],"category_scores_gemma":[0.00004692466,0.0002403494,0.00003177336,0.0002385954,0.00003753544,0.00007435613,0.00003892467,0.0002114424,2.492652e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006651302,"about_ca_system_score_gemma":0.000003956188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001725596,"about_ca_topic_score_gemma":0.000002938003,"domain_scores_codex":[0.999146,0.00001001234,0.000220557,0.000237006,0.0000804481,0.0003059535],"domain_scores_gemma":[0.9996167,0.00009408456,0.0000251288,0.0001552547,0.00003615488,0.00007272189],"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":[0.00001048755,3.906126e-7,0.0002333991,0.0002947715,0.00001984407,7.601637e-7,0.00005882456,0.9829207,0.0005097266,0.0009021185,0.00005743457,0.01499155],"study_design_scores_gemma":[0.0002265121,0.00002537218,0.06892293,0.00003931174,0.00002108546,0.00001300939,0.00001292418,0.926906,0.002830392,0.0006457967,0.0001610753,0.0001955419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6723331,0.001132553,0.3241279,0.00001271508,0.001301017,0.0003331978,0.000007004629,0.0007450746,0.000007367543],"genre_scores_gemma":[0.9899172,0.0000477693,0.009716468,0.00002180002,0.0002020656,0.00004246142,0.000008950666,0.00003939811,0.000003903287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3175841,"threshold_uncertainty_score":0.9801164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007057295138122342,"score_gpt":0.2341164175522048,"score_spread":0.2270591224140825,"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."}}