An interpretable machine learning approach for predicting the capacity and failure mode of reinforced concrete columns
Bibliographic record
Abstract
During seismic events, reinforced concrete (RC) columns play a crucial role in maintaining buildings’ structural integrity. This motivated engineers and practitioners to search for key parameters that influence the load-carrying capacity and failure mechanisms of such columns. However, the complexity and nonlinearity of seismic effects along with the intricate nature of RC columns as a composite system challenge the capabilities of analytical and empirical approaches to accurately capture the response of RC columns. Subsequently, the present study utilizes Machine Learning (ML) techniques to identify the failure modes and predict the corresponding capacities of RC columns based on both their geometrical and material properties. Decision trees and different ensemble methods were employed to predict both the columns’ failure mode and ultimate capacity. A multivariate dataset consisting of 486 cyclically loaded rectangular and circular columns was used to develop and validate the models. In addition, different embedded variable selection techniques were employed to evaluate the significance of input parameters in predicting the performance of columns. Moreover, partial dependence plots and accumulated local effects were employed to uncover the interrelationships between the input features and the modelled outputs. The developed models yielded an average accuracy of 90% and 95% for predicting the failure mode and ultimate capacity of RC columns, respectively. Given such high accuracy, it can be inferred that, ML techniques have the potential to provide efficient and reliable prediction tools to support seismic design and assessment decisions - mitigating seismic risks and empowering resilience planning in the face of extreme events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".