Bridging AI and explainability in civil engineering: the Yin-Yang of predictive power and interpretability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Civil engineering relies on data from experiments or simulations to calibrate models that approximate system behaviors. This paper examines machine learning (ML) algorithms for AI-driven decision support in civil engineering, specifically construction engineering and management, where complex input–output relationships demand both predictive accuracy and interpretability. Explainable AI (XAI) is critical for safety and compliance-sensitive applications, ensuring transparency in AI decisions. The literature review identifies key XAI evaluation attributes—model type, explainability, perspective, and interpretability and assesses the Enhanced Model Tree (EMT), a novel method demonstrating strong potential for civil engineering applications compared to commonly applied ML algorithms. The study highlights the need to balance AI’s predictive power with XAI’s transparency, akin to the Yin–Yang philosophy: AI advances in efficiency and optimization, while XAI provides logical reasoning behind conclusions. Drawing on insights from the literature, the study proposes a tailored XAI assessment framework addressing civil engineering's unique needs—problem context, data constraints, and model explainability. By formalizing this synergy, the research fosters trust in AI systems, enabling safer and more socially responsible outcomes. The findings underscore XAI’s role in bridging the gap between complex AI models and end-user accountability, ensuring AI’s full potential is realized in the field.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it