# MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design

arXiv: https://arxiv.org/abs/2610.02214 · HTML: https://mlsanity.com/paper/2610.02214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks](https://mlsanity.com/paper/2406.09624/) (2024)
- [From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design](https://mlsanity.com/paper/2311.12668/) (2023)
- [DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction](https://mlsanity.com/paper/2403.08055/) (2024)
- [DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation](https://mlsanity.com/paper/2404.07917/) (2024)
- [BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks](https://mlsanity.com/paper/2103.05844/) (2021)
- [Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning](https://mlsanity.com/paper/2305.08279/) (2023)
- [FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames](https://mlsanity.com/paper/2201.10459/) (2022)
- [ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model](https://mlsanity.com/paper/2305.00210/) (2023)
- [ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints](https://mlsanity.com/paper/2311.06315/) (2023)
- [DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications](https://mlsanity.com/paper/2305.09018/) (2023)
- [A Multi-Stage Hybrid Framework for Automated Interpretation of Multi-View Engineering Drawings Using Vision Language Model](https://mlsanity.com/paper/2510.21862/) (2025)
- [Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer](https://mlsanity.com/paper/2505.01530/) (2025)
- [Designing ship hull forms using generative adversarial networks](https://mlsanity.com/paper/2311.05470/) (2023)
- [A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings](https://mlsanity.com/paper/2312.13620/) (2023)
- [BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs](https://mlsanity.com/paper/2402.05301/) (2024)

## Abstract (arXiv)

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.