arXiv：cs.LG· Noah J. Bagazinski, Md. Ferdous Alam, Jaya Manideep Rebbagondla, Faez Ahmed·· 3 小时前AI 评分31
MiDShip：面向工程设计的船舶货舱结构多模态数据集
MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design
AI 导读
研究团队发布 MiDShip 多模态数据集，包含 12,753 个合成货舱结构设计，涵盖参数化数据、3D 几何、工程图纸、物料清单及初步结构评估。其中 6,237 个经方程引导修复流程生成的设计中 4,952 个（79.4%）完全满足基于 ABS MVR 子集推导的 25 项约束，平均违规 0.296 项；随机设计则无一全部合规。该数据集旨在支持船舶结构的机器学习、生成式设计与自动化规则评估。
正文
Abstract: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.
| Subjects: | Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02214 [cs.CE] |
| (or arXiv:2610.02214v1 [cs.CE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02214 arXiv-issued DOI via DataCite |
Submission history
From: Noah Bagazinski [view email]
[v1]
Fri, 4 Sep 2026 21:33:18 UTC (11,091 KB)
来源：arXiv：cs.LG · arxiv.org