← 研踪 PaperTrailarXiv:2607.16580 · v2
Automating structural reliability analysis with a multi-agent large language model framework
首次提交 2026-07-18 09:45
版本更新 2026-11-09 12:29
雷达观测 2026-11-10 13:18
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 (LLM) 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. Numerical reliability estimates are not generated by an LLM; instead, they are obtained solely through deterministic execution of validated solvers. This design mitigates the risk of hallucinated numerical outputs and supports reproducibility. The Method Planner is fine-tuned using quantized low-rank adaptation (QLoRA) for a priori reliability-method category selection. 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.
作者备注：Submitted to Automation in Construction
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v2 · 2026-11-09 12:29 · 最新观测版本
Automating structural reliability analysis with a multi-agent large language model framework
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 (LLM) 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. Numerical reliability estimates are not generated by an LLM; instead, they are obtained solely through deterministic execution of validated solvers. This design mitigates the risk of hallucinated numerical outputs and supports reproducibility. The Method Planner is fine-tuned using quantized low-rank adaptation (QLoRA) for a priori reliability-method category selection. 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.
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