使用多智能体大语言模型框架实现结构可靠性分析自动化
Automating structural reliability analysis with a multi-agent large language model framework
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中文总结 AI 辅助
研究提出多智能体大语言模型框架，通过专门智能体处理结构可靠性分析流程，用QLoRA微调方法规划器，由确定性求解器计算结果，降低专业知识壁垒，保持计算可信度，实现结构可靠性分析自动化。
中文摘要 AI 辅助
结构可靠性分析支持建筑和民用基础设施的设计与安全评估，但整个工作流程都需要专业知识。本研究提出了一个多智能体大语言模型框架，可从自然语言问题陈述自动执行组件级可靠性分析，直至对可靠性指标和失效概率的解释性估计。专门的智能体处理问题表述、方法规划、代码生成、执行和结果解释，并在关键决策点进行人工确认。方法规划器使用QLoRA进行微调以进行先验可靠性方法类别选择。分析结果不是由大语言模型直接生成，而是由经过验证的确定性求解器计算可靠性估计值，提高了可重复性并降低了幻觉风险。该框架使用开放权重模型并支持无封闭API的本地执行。结果表明，它降低了结构可靠性评估的专业知识壁垒，同时保持了计算可信度。
英文摘要
Structural reliability analysis supports the design and safety assessment of buildings and civil infrastructure but requires specialized expertise throughout the workflow. This study presents a multi-agent large language model framework that automates component-level reliability analysis from a natural-language problem statement to interpreted estimates of the reliability index and failure probability. Specialized agents handle problem formulation, method planning, code generation, execution, and result interpretation, with human confirmation at key decision points. The Method Planner is fine-tuned using QLoRA for a priori reliability-method category selection. Analysis results are not generated directly by an LLM; instead, validated deterministic solvers compute the reliability estimates, improving reproducibility and reducing hallucination risk. The framework uses open-weight models and supports local execution without closed APIs. Results show that it lowers the expertise barrier to structural reliability assessment while preserving computational trustworthiness.
