Scholarlink

<p>The design of dynamically resilient concrete materials remains a complex, fragmented process that depends on iterative modelling, expert judgment, and poorly integrated workflows spanning structural analysis, material formulation, and seismic performance evaluation. To address this challenge, we develop an autonomous multi-agent system (MAS) that optimizes the life cycle design of fiber-reinforced concrete (FRC) for earthquake resilience. The system focuses on shear walls, beams, and columns that constitute the seismic load bearing envelope of multi-storey buildings. Rather than functioning as a general-purpose structural tool, the MAS is designed to intelligently generate FRC specifications, tailoring fiber type, geometry, and reinforcement ratios according to seismic inputs and project constraints that are automatically parsed by the system. The framework integrates agentic artificial intelligence (AgenAI) via an Agent2Agent (A2A) protocol with physical artificial intelligence (PhysAI) implemented through physics informed stochastic models for micromechanical prediction and energy dissipation damage analysis. A modified maximum entropy principle models fiber matrix interactions under heterogeneous uncertainty, enabling unbiased optimization of energy dissipative properties. In parallel, a CNN based computer vision pipeline employing a 3D U-Net architecture performs accurate segmentation and interpretation of micro-CT data. The architecture comprises five specialized AI agents built on Qwen-30B-A3B and Phi-4-14B large language models (LLMs), with context grounding provided by agentic retrieval augmented generation (ARAG) for domain specific decision making. Experimental validation shows that the system reduces human intensive design cycles by 75% relative to conventional finite element analysis workflows. It achieves 89.2% reasoning adherence, a mean predictive bias below 4% compared with stochastic simulations, and a failure state classification F1 score of 0.98. These results bridge micromechanical realism and automated, code compliant seismic design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automating the seismic-resilient design of fiber-reinforced concrete using a physics-informed multi-agent system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月13日

查看全文
  • Artem Zaitsev

摘要

The design of dynamically resilient concrete materials remains a complex, fragmented process that depends on iterative modelling, expert judgment, and poorly integrated workflows spanning structural analysis, material formulation, and seismic performance evaluation. To address this challenge, we develop an autonomous multi-agent system (MAS) that optimizes the life cycle design of fiber-reinforced concrete (FRC) for earthquake resilience. The system focuses on shear walls, beams, and columns that constitute the seismic load bearing envelope of multi-storey buildings. Rather than functioning as a general-purpose structural tool, the MAS is designed to intelligently generate FRC specifications, tailoring fiber type, geometry, and reinforcement ratios according to seismic inputs and project constraints that are automatically parsed by the system. The framework integrates agentic artificial intelligence (AgenAI) via an Agent2Agent (A2A) protocol with physical artificial intelligence (PhysAI) implemented through physics informed stochastic models for micromechanical prediction and energy dissipation damage analysis. A modified maximum entropy principle models fiber matrix interactions under heterogeneous uncertainty, enabling unbiased optimization of energy dissipative properties. In parallel, a CNN based computer vision pipeline employing a 3D U-Net architecture performs accurate segmentation and interpretation of micro-CT data. The architecture comprises five specialized AI agents built on Qwen-30B-A3B and Phi-4-14B large language models (LLMs), with context grounding provided by agentic retrieval augmented generation (ARAG) for domain specific decision making. Experimental validation shows that the system reduces human intensive design cycles by 75% relative to conventional finite element analysis workflows. It achieves 89.2% reasoning adherence, a mean predictive bias below 4% compared with stochastic simulations, and a failure state classification F1 score of 0.98. These results bridge micromechanical realism and automated, code compliant seismic design.