mlsanityData to 7 Oct 2026

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

arXiv 2407.10064 ↗First posted 14 Jul 2024cs.MAcited by 3

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 68 of its 204 references that are in the map. First posted on arXiv 14 Jul 2024.

Builds on

68 of 204 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey17 Apr
  2. 2024Exploring Autonomous Agents through the Lens of Large Language Models: A Review5 Apr
  3. 2024Understanding the planning of LLM agents: A survey5 Feb
  4. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep

Other references · 64

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50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agentscited by 14
  2. 2025Zero-Shot Image-Based Large Language Model Approach to Road Pavement Monitoringcited by 1
  3. 2026Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challengescited by 1

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