Abstract
Bridge maintenance and safety are essential for transportation authorities, and Non-Destructive Evaluation (NDE) techniques are critical to assessing structural integrity. However, interpreting NDE data can be time-consuming and requires expertise, potentially delaying decision-making. Recent advancements in Large Language Models (LLMs) offer new ways to automate and improve this analysis. This pilot study introduces a holistic assessment of LLM capabilities for interpreting NDE contour maps and demonstrates the effectiveness of LLMs in providing detailed bridge condition analyses. It establishes a framework for integrating LLMs into bridge inspection workflows, indicating that LLM-assisted analysis can enhance efficiency without compromising accuracy. In this study, several LLMs are explored with prompts specifically designed to enhance the quality of image descriptions, which are applied to interpret five different NDE contour maps obtained through technologies for assessing bridge conditions. Each LLM model is evaluated based on its ability to produce detailed descriptions, identify defects, provide actionable recommendations, and demonstrate overall accuracy. The research indicates that four of the nine models provide better image descriptions, effectively covering a wide range of topics related to the bridge's condition. The outputs from these four models are summarized using five different LLMs to form a comprehensive overview of the bridge. Notably, LLMs ChatGPT-4 and Claude 3.5 Sonnet generate more effective summaries. The findings suggest that LLMs have the potential to significantly improve efficiency and accuracy. This pilot study presents an innovative approach that leverages LLMs for image captioning in parallel and summarization, enabling faster decision-making in bridge maintenance and enhancing infrastructure management and safety assessments.
Abstract (translated)
桥梁维护和安全对交通管理部门至关重要，而非破坏性评估（NDE）技术对于评估结构完整性是必不可少的。然而，解释NDE数据既耗时又需要专业知识，这可能会延误决策过程。近期在大型语言模型（LLMs）方面的进展为自动化并改进此类分析提供了新的途径。这项试点研究对LLM在解读NDE轮廓图方面的能力进行了全面评估，并展示了LLM在提供详细的桥梁状况分析中的有效性。该研究建立了一个框架，将LLM整合到桥梁检查工作流程中，表明使用LLM辅助分析可以提高效率而不会降低准确性。 在这项研究中，几种不同的LLM被探索并应用了一系列专为改善图像描述质量设计的提示语，这些提示用于解释通过不同技术获得的五种NDE轮廓图。每个模型依据其生成详细描述的能力、识别缺陷的能力、提供可操作建议的能力以及整体准确性的表现进行了评估。研究发现，九个模型中有四个提供了更好的图像描述，并且有效涵盖了与桥梁状况相关的一系列广泛话题。这四个模型的输出使用五个不同的LLM进行汇总，从而形成对整个桥梁状况的综合概述。值得注意的是，ChatGPT-4和Claude 3.5 Sonnet生成了更为有效的摘要。 该研究结果表明，LLMs有可能显著提高效率与准确性。这项试点项目提出了一种创新的方法，利用LLM并行处理图像标题生成以及汇总操作，从而加速桥梁维护的决策过程，并提升基础设施管理和安全评估的质量。
URL
https://arxiv.org/abs/2507.14107
