Determination of Steel Area in Reinforced Concrete Beams Using Data Mining Techniques
Bibliographic record
Abstract
This study aimed to determine the area of reinforcing steel in rectangular reinforced concrete beams, a critical concern given that a significant proportion of residential structures in Peru do not conform to technical design regulations.Data were collected through a structured form encompassing various design variables, yielding a comprehensive data matrix.The methodology adopted involved Knowledge Discovery in Databases (KDD), executed in several stages: (1) selection, where the InfoGainAttributeEval algorithm was utilized to identify variables influencing the reinforcing steel area; (2) preprocessing, during which anomalous and duplicate data were purged using the Python libraries, Pandas and Numpy; (3) reduction, and (4) data mining.For the latter, Decision Stump, Hoeffding Tree, J48, Logistic Model Trees (LMT), and Random Tree classification algorithms were employed, facilitated by Weka 3.9.4.Accuracy rates of these algorithms were found to be 25, 51.70, 77.97, 78.39, and 88.98% respectively.The Random Tree algorithm, in conjunction with the GP_04 model, provided estimations of the steel area in the beams with a reliability exceeding 88%.The application of this model could enable the optimization of beam design, facilitate cost savings on materials, and enhance structural safety.This research thus presents a significant contribution to the field of structural engineering, particularly in regions where compliance with technical design standards is suboptimal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".