发表机构
Massachusetts Institute of Technology; Georgia Institute of Technology（麻省理工学院; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对船舶结构设计缺乏结构化数据的问题，提出包含12,753个货舱设计的多模态数据集MiDShip，涵盖几何、性能与约束，并验证了两种生成程序，支持机器学习与自动合规评估。
AI 中文摘要
船舶结构决定了船体的强度、安全性和可制造性，但其设计必须满足数百项船级社规范要求，这使得设计过程复杂且迭代。数据驱动方法受到缺乏将设计几何、结构性能和基于规则的约束联系起来的结构化数据集的限制。本文提出了MiDShip，一个包含12,753个合成货舱结构设计的多模态数据集：其中6,020个为随机设计，496个由SGLD启发程序生成，6,237个由基于方程修复程序生成。每个设计包括参数化数据、完整且可网格化的3D几何、工程图纸和注释、物料清单以及初步结构评估。还评估了源自ABS MVR子集的25项约束。随机设计中没有一个满足所有约束。在SGLD启发设计中，322个（64.9%）完全合规，平均违规数为0.409，比种子均值低82.7%，比随机设计均值低96.9%。通过LLM辅助代码分析开发的修复程序生成了4,952个完全合规设计（79.4%），平均违规数为0.296，比配对源均值低97.1%。在等规模比较中，缩放后的120维参数空间中，修复设计的平均最近邻距离为3.495，SGLD批次为1.144，随机设计为3.729。主要贡献是同步数据集及其生成和评估基础设施；生成研究证明了其效用，而非提出新的优化算法。MiDShip支持船舶结构的机器学习、生成设计和基于规则的自动评估。
英文摘要
Ship structures govern vessel strength, safety, and manufacturability, but their design must satisfy hundreds of classification society requirements, making the process complex and iterative. Data-driven approaches are limited by the lack of structured datasets linking design geometry, structural performance, and rule-based constraints. This paper presents MiDShip, a multimodal dataset of 12,753 synthetic cargo-hold structural designs: 6,020 random, 496 generated by an SGLD-inspired procedure, and 6,237 generated by an equation-informed repair procedure. Each design includes parametric data, full and mesh-ready 3D geometry, engineering drawings and annotations, a bill of materials, and preliminary structural evaluations. Twenty-five constraints derived from a subset of ABS MVR are also evaluated. None of the random designs satisfies all constraints. Among the SGLD-inspired designs, 322 (64.9%) were fully compliant, with an average of 0.409 violations, 82.7% below the seed mean and 96.9% below the random-design mean. The repair procedure, developed through LLM-assisted code analysis, produced 4,952 fully compliant designs (79.4%), averaging 0.296 violations, 97.1% below the paired-source mean. In equal-size comparisons, mean nearest-neighbor distances in the scaled 120-parameter space were 3.495 for repaired designs, 1.144 for SGLD batches, and 3.729 for random designs. The primary contribution is the synchronized dataset and its generation and evaluation infrastructure; the generation studies demonstrate its utility rather than proposing new optimization algorithms. MiDShip supports machine learning, generative design, and automated rule-based evaluation for ship structures.
