Using Large Language Models to Identify Project Risks for Sustainable Operations
Why this work is in the frame
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Bibliographic record
Abstract
Risk identification in construction projects is crucial for sustainable operations. However, it is often hindered by omissions and participant subjectivity. This study explores the application of large language models (LLMs) in identifying risks and impacted activities within construction projects. The methodology includes developing a RiskGPT agent, fine-tuning it with prompt words, augmenting it with structural knowledge, and evaluating its application on real projects. Preliminary results from a case study demonstrate the potential benefits and challenges of using LLMs in this context. Despite the generalization tendency and occasional technical issues, LLMs show promise in augmenting human expertise and providing a robust foundation for risk management in construction projects. Future research should focus on improving training data quality, enhancing contextual understanding, and refining the integration of LLM outputs with human insights to maximize their practical applicability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it