Improved Information Extraction from Bridge Inspection Reports using Fine-tuned Generative Pre-trained Transformers
Why this work is in the frame
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Bibliographic record
Abstract
Bridge inspection reports contain a wealth of crucial data on bridge components and their related structural defects.This study introduces a novel method that harnesses the power of Generative Pre-trained Transformers (GPT) for improved information extraction from bridge inspection reports.While most studies in this domain focus solely on data extraction, this study transforms inspection data into a ready-to-use format for better utilization in condition assessment and predictive modeling, enabling better-informed budget allocation and decision-making.It employs (1) a baseline GPT model (BL-GPT), which leverages OpenAI's large language models to process textual inspection data through optimized prompt engineering, and (2) a finetuned GPT model (FT-GPT), which enhances the baseline by incorporating domain-specific training to improve performance.These models capture and evaluate the severity levels of reinforced concrete bridge defects based on textual inspection data.The models are validated on data extracted from 2,255 inspection reports-spanning a period of five years (2018-2022)for a set of bridges in Qubec, Canada.The FT-GPT is found to significantly improve performance, stability, and reliability in detecting and standardizing the severity of different types of concrete defects in bridge decks.In specific, it achieves accuracy rates of 98.79% for rebar corrosion, 99.09% for delamination, and 98.64% for cracking, scaling, and spalling of concrete.This study demonstrates the potential integration of generative AI in asset management, an application that has yet to be realized.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it