Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specification violations may invalidate downstream simulations. The potential of large language models (LLMs) for automatic generation of modeling code has been demonstrated. However, non-executable or physically inconsistent outputs remain prevalent under stringent engineering constraints. A framework for physics-consistent automatic building modeling is therefore proposed, integrating domain knowledge construction, constraint-oriented model alignment, and verification-driven evaluation. CivilInstruct is introduced as a domain-specific dataset that formalizes structural engineering knowledge and constraint reasoning to enable simulation-ready model generation. A two-stage fine-tuning strategy is further employed to enforce constraint satisfaction and application programming interface compliance, substantially reducing hallucinated and non-conforming outputs. MBEval is presented as a verification-driven benchmark that evaluates executability and structural dynamics consistency through closed-loop validation. Experimental results show consistent improvements over baselines across rigorous verification metrics. Our code is available at https://github.com/Jovanqing/AutoBM.

翻译：结构建模是计算工程科学的基础组成部分，其中即使微小的物理不一致或规范违反都可能导致下游仿真失效。大型语言模型（LLM）在自动生成建模代码方面的潜力已得到验证。然而，在严格的工程约束下，不可执行或物理不一致的输出仍然普遍存在。为此，本文提出了一种物理一致的自动建筑建模框架，整合了领域知识构建、约束导向的模型对齐以及验证驱动的评估。我们引入了CivilInstruct作为领域专用数据集，该数据集形式化了结构工程知识与约束推理，以实现可直接用于仿真的模型生成。进一步采用两阶段微调策略以强制满足约束条件并符合应用程序接口规范，从而显著减少幻觉性及不符合规范的输出。提出了MBEval作为验证驱动的基准测试，通过闭环验证评估可执行性与结构动力学一致性。实验结果表明，在各项严格验证指标上均较基线方法取得持续改进。我们的代码公开于 https://github.com/Jovanqing/AutoBM。