{"id":"W4408040563","doi":"10.18280/ts.420125","title":"Developed Improved Weighed Quantum Particle Swarm Optimisation with Deep Convolutional Neural Network Algorithm to Improve the Automated Concrete Surface Defect Detection in Bridge Inspections","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Particle swarm optimization; Convolutional neural network; Computer science; Swarm behaviour; Artificial neural network; Surface (topology); Algorithm; Quantum; Structural engineering; Artificial intelligence; Engineering; Mathematics; Physics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000263548,0.0002170933,0.0001811301,0.00006458504,0.0002318285,0.00007445081,0.000121324,0.00006741262,0.0000145113],"category_scores_gemma":[0.00001354141,0.0001731518,0.00005327116,0.0006195109,0.00004479108,0.0001606701,0.00002935337,0.0002324247,0.000004977511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003491491,"about_ca_system_score_gemma":0.00005787449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001718599,"about_ca_topic_score_gemma":0.0002874299,"domain_scores_codex":[0.9987363,0.00005661863,0.0003413129,0.0002469095,0.0001597077,0.0004591153],"domain_scores_gemma":[0.9995711,0.000080292,0.00004715518,0.0001364269,0.0001016433,0.00006331342],"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":[0.0001357051,0.000008811368,0.001402988,0.00002234574,0.0001275304,0.000004724885,0.00043283,0.9214495,0.06766023,0.0002813128,0.0001526918,0.00832134],"study_design_scores_gemma":[0.001036122,0.0001185257,0.07624742,0.00003514029,0.00003770867,0.000005638242,0.0001107771,0.8953775,0.02658936,0.00004855532,0.0001966916,0.0001965971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7565506,0.00007112308,0.2414229,0.0001007915,0.000707463,0.0006099076,0.000008164992,0.0004695973,0.00005941129],"genre_scores_gemma":[0.9952313,0.00000496809,0.00424148,0.0001152775,0.0002050231,0.0001465359,0.00001396252,0.00002482202,0.00001664741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2386807,"threshold_uncertainty_score":0.7060927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006751825115218755,"score_gpt":0.2168368970505381,"score_spread":0.2100850719353193,"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."}}