Data-driven assessment of corrosion in reinforced concrete structures embedded in clay dominated soils
Bibliographic record
Abstract
The integration of Artificial Intelligence techniques, particularly Artificial Neural Networks (ANNs), has transformed predictive modeling in structural and durability engineering. This study investigates the use of ANN-based approaches to predict the corrosion rates of mild steel reinforcement embedded in cementitious composites subjected to clay-dominated soil environments. Key environmental parameters, sodium chloride (NaCl) content (0-4%), inhibitor dosage (DOI) (0-5%), and exposure duration (30-180 days), were selected as input variables. Two ANN architectures, Feedforward Backpropagation (FFBP) and Cascadeforward Backpropagation (CFBP), were developed and trained using 72 experimental data points extracted from the literature. The FFBP model outperformed CFBP in terms of predictive accuracy, achieving a correlation coefficient (R) of 0.998, a mean absolute percentage error (MAPE) of 30.43%, and a root mean square error (RMSE) of 0.071 during testing. Sensitivity analysis revealed that inhibitor dosage had the most significant influence on corrosion behavior, followed by NaCl concentration and exposure duration. The findings confirm that ANN models can effectively capture the nonlinear interactions governing corrosion progression, even under complex environmental conditions associated with clayey soils. This research provides a reliable and practical AI-driven framework for assessing corrosion risk, guiding material design, and enhancing long-term infrastructure durability in aggressive subsurface conditions. The study underscores the growing relevance of machine learning in simulating time-dependent deterioration processes in geotechnical and structural materials.
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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 | 0.001 | 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".