New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns
Bibliographic record
Abstract
Predicting the mechanical strength of structural elements is a crucial task for the efficient design of buildings. Considering the shortcomings of experimental and empirical approaches, there is growing interest in using artificial intelligence techniques to develop data-driven tools for this purpose. In this research, empowered machine learning was employed to analyze the axial compression capacity (CC) of circular concrete-filled steel tube (CCFST) composite columns. Accordingly, the adaptive neuro-fuzzy inference system (ANFIS) was trained using four metaheuristic techniques, namely earthworm algorithm (EWA), particle swarm optimization (PSO), salp swarm algorithm (SSA), and teaching learning-based optimization (TLBO). The models were first applied to capture the relationship between the CC and column characteristics. Subsequently, they were requested to predict the CC for new column conditions. According to the results of both phases, all four models could achieve dependable accuracy. However, the PSO-ANFIS was tangibly more efficient than the other models in terms of computational time and accuracy and could attain more accurate predictions for extreme conditions. This model could predict the CC with a relative error below 2% and a correlation exceeding 99%. The PSO-ANFIS is therefore recommended as an effective tool for practical applications in analyzing the behavior of the CCFST columns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".