Revolutionizing Bridge Operation and Maintenance with LLM-based Agents: An Overview of Applications and Insights

TL;DR

This paper explores how large language model-based AI agents can transform bridge operation and maintenance by enhancing inspection, decision-making, and autonomous evaluation, addressing current industry limitations.

Contribution

It provides a comprehensive overview of AI applications in bridge O&M, highlighting potential benefits, challenges, and future opportunities for intelligent systems in this field.

Findings

Existing intelligent inspection devices and algorithms support AI integration.

AI can improve decision-making and autonomous evaluation in bridge O&M.

Challenges include technical limitations and implementation hurdles.

Abstract

In various industrial fields of human social development, people have been exploring methods aimed at freeing human labor. Constructing LLM-based agents is considered to be one of the most effective tools to achieve this goal. Agent, as a kind of human-like intelligent entity with the ability of perception, planning, decision-making, and action, has created great production value in many fields. However, the bridge O&M field shows a relatively low level of intelligence compared to other industries. Nevertheless, the bridge O&M field has developed numerous intelligent inspection devices, machine learning algorithms, and autonomous evaluation and decision-making methods, which provide a feasible basis for breakthroughs in artificial intelligence in this field. The aim of this study is to explore the impact of AI bodies based on large-scale language models on the field of bridge O&M and to analyze the potential challenges and opportunities it brings to the core tasks of bridge O&M. Through in-depth research and analysis, this paper expects to provide a more comprehensive perspective for understanding the application of intelligentsia in this field.