{"id":"W4318777570","doi":"10.3390/buildings13020391","title":"Combining Artificial Neural Network and Seeker Optimization Algorithm for Predicting Compression Capacity of Concrete-Filled Steel Tube Columns","year":2023,"lang":"en","type":"article","venue":"Buildings","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Benchmark (surveying); Artificial neural network; Compression (physics); Structural engineering; Column (typography); Compressive strength; Computer science; Algorithm; Engineering; Artificial intelligence; Materials science; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004914653,0.0006202199,0.0004131914,0.0004926129,0.0002106737,0.0003681107,0.0004636196,0.0007107537,0.0004504959],"category_scores_gemma":[0.001215397,0.0002542567,0.0003236675,0.0003637816,0.0003083792,0.0005225079,0.0003528871,0.0004483825,0.0000801268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004743713,"about_ca_system_score_gemma":0.0007130604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223975,"about_ca_topic_score_gemma":0.007453518,"domain_scores_codex":[0.9998187,0.00004898787,0.00001110203,0.00004750544,0.00004953092,0.00002407298],"domain_scores_gemma":[0.9996092,0.0002289616,0.000052604,0.00001558008,0.00008096655,0.00001288036],"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":[0.00002647378,0.00002493922,0.001032133,0.00001565202,0.00001628895,0.00002076519,0.000009438303,0.9783778,0.001281711,0.0003391004,0.0001227755,0.018733],"study_design_scores_gemma":[4.829839e-7,0.000005152998,0.00006879057,4.809917e-7,0.000001108357,0.000001179376,8.104225e-7,0.9997074,0.0001542408,0.00004584358,0.00001370123,7.543206e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3312241,0.0007226454,0.6628754,0.0002928776,0.00006539291,0.00005433198,0.00005912745,0.0006386319,0.004067423],"genre_scores_gemma":[0.9642682,0.0001700898,0.03392081,0.00004591882,0.00001864199,0.00005438143,0.00007631087,0.00001365668,0.00143201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01223975,"threshold_uncertainty_score":0.02433699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02801737206235718,"score_gpt":0.2722350646592713,"score_spread":0.2442176925969141,"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."}}