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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.10064 |
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| _version_ | 1866929650548080640 |
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| author | Chen, Xinyu Zhang, Lianzhen |
| author_facet | Chen, Xinyu Zhang, Lianzhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revolutionizing Bridge Operation and Maintenance with LLM-based Agents: An Overview of Applications and Insights Chen, Xinyu Zhang, Lianzhen Multiagent Systems 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. |
| title | Revolutionizing Bridge Operation and Maintenance with LLM-based Agents: An Overview of Applications and Insights |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2407.10064 |