Work

Image‐based large language model approach to road pavement monitoring

Year: 2025

Type: article

Abstract: Accurate and rapid assessment of pavement surface condition is essential for maintaining transportation safety and minimizing vehicle wear. Manual pavement inspections are subjective and time-consuming, and machine learning methods typically require large labeled datasets. This study introduces an innovative zero-shot learning method that leverages large language models’ (LLMs) image analysis and natural-language understanding capabilities for accurate road condition assessment. Prompts were designed in alignment with the pavement surface condition index criteria to generate multiple evaluation models, which were then compared against official scores to identify an optimized configuration. Tests conducted using Google Street View imagery indicate that the optimized LLM-based model achieves a mean absolute error of 1.07 on a 0–10 scale, outperforming expert evaluations. The proposed approach enables rapid, accurate, and consistent assessments without the need for labeled data, demonstrating the transformative role of LLMs in automating infrastructure monitoring and emphasizing the importance of structured prompt engineering for reliable performance.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.70075

Authors: Shuoshuo Xu, Kai Zhao, James Loney, Zili Li, Andrea Visentin

Institution: University College Cork

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,230