# Automating structural reliability analysis with a multi-agent large language model framework

arXiv: https://arxiv.org/abs/2607.16580 · HTML: https://mlsanity.com/paper/2607.16580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 14 of 14)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations](https://mlsanity.com/paper/2504.09602/) (2025)
- [Integrating Large Language Models for Automated Structural Analysis](https://mlsanity.com/paper/2504.09754/) (2025)
- [A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis](https://mlsanity.com/paper/2510.05414/) (2025)
- [A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis](https://mlsanity.com/paper/2507.02938/) (2025)
- [Automating Structural Engineering Workflows with Large Language Model Agents](https://mlsanity.com/paper/2510.11004/) (2025)
- [Can ChatGPT implement finite element models for geotechnical engineering applications?](https://mlsanity.com/paper/2501.02199/) (2025)
- [Rare event estimation using stochastic spectral embedding](https://mlsanity.com/paper/2106.05824/) (2021)
- [Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes](https://mlsanity.com/paper/2505.04666/) (2025)
- [A Novel Multi-Agent Architecture to Reduce Hallucinations of Large Language Models in Multi-Step Structural Modeling](https://mlsanity.com/paper/2603.07728/) (2026)
- [Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems](https://mlsanity.com/paper/2606.06525/) (2026)
- [Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation](https://mlsanity.com/paper/2507.19771/) (2025)

## Abstract (arXiv)

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.