cs.CESep 4, 2026
SaveMiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design
Organizations: Department of Mechanical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139 · George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology Atlanta, GA 30318
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.
Figures & tables
Figure 1: Overview of the MiDShip dataset, a multimodal dataset for ship cargo-hold structural design and evaluation. Each design is represented by a parametric design vector used to generate full 3D and mesh-ready geometries. Corresponding 2D engineering drawings are produced alongside labels for structural density, longitudinal cross-sectional area, center of gravity, maximum bending moment, and twenty-five local scantling evaluations derived from ABS design rules.
Figure 2: Each ship structure is defined by a set of 120 parameters grouped into three categories: principal characteristics and ship class (10 parameters), shell and plate dimensions (27 parameters), and stiffener/girder spacing and sizing (83 parameters). There are twenty unique stiffeners characterized by the parametric designs.
Figure 3: Two-dimensional t-SNE visualizations of the design distributions, colored by ship class and the presence of longitudinal bulkheads. Panel (a) shows the 6,020 random designs; panel (b) overlays all 500 attempted SGLD candidate vectors; panel (c) overlays a 100-design sample of repaired candidate vectors; and panel (d) overlays all 6,254 attempted repair candidate vectors. The projection provides a qualitative view of the jointly embedded parameter vectors and is not used to quantify global design-space coverage or engineering diversity.
Figure 4: Examples of dataset designs. The leftmost column shows two containership designs; the two middle columns show tanker designs with varying longitudinal bulkheads and hatch openings; and the rightmost column shows two bulk-carrier designs.
Figure 5: Example mesh-ready geometry. This representation is identical to the full 3D geometry except that smaller stiffeners are replaced with curves. The structural properties of each stiffener are stored in a list to support future finite-element meshing.
Figure 6: Example 2D engineering drawing of the longitudinal structure. The drawing contains a slice of the ship structure, a title block, two information blocks, and formatted margins.
Figure 7: The 2D drawing in Figure 6 with labeled bounding boxes around structural elements. The positions, dimensions, and unique identifiers of the structural elements and bounding boxes are stored in a separate metadata file.
Figure 8: Distributions of structural properties for the 6,020 retained random designs. The plots show the volume-normalized structural steel mass, longitudinal cross-sectional area, and calculated maximum bending moment released with the dataset.
Figure 9: Examples of random designs with high calculated maximum bending moments (top) and low structural densities (bottom).
| Population | Designs | Feasibility rate ( ) | Mean CV count ( ) | MNN distance |
|---|---|---|---|---|
| Random designs | 6,020 | 0.000 | 13.192 | |
| SGLD seed pool | 25 | 0.000 | 2.360 | – |
| SGLD-generated designs | 496 | 0.649 | 0.409 | |
| Repair source designs | 3,127 | 0.000 | 10.288 | – |
| Repaired designs | 6,237 |
Table 1: Comparison of feasibility and constraint-violation statistics for the complete dataset subsets and the method-specific starting populations. The SGLD seed pool and repair source designs are drawn from the random subset and are not additional dataset instances. Feasibility is the fraction of designs satisfying all twenty-five implemented constraints, and the mean constraint-violation (CV) count is the total number of violations divided by the number of designs. Candidate parameter sets that failed CAD generation are excluded. MNN values are means across equal-size 100-design sets, where is the sample standard deviation. The random and repaired results use thirty sets sampled without replacement within each set, with possible overlap across repeated draws; the SGLD result uses the five actual 100-candidate generation batches. MNN was not calculated for the method-specific starting populations. The MNN ranges describe between-set dispersion and are not confidence intervals. Full violation-count distributions are reported in Appendix Table 8 . Arrows indicate the preferred direction for feasibility and CV count; MNN is reported descriptively, and boldface denotes the best feasibility and CV values.
| Constraint | Random satisfaction rate (%) | Validation | Validation (%) |
|---|---|---|---|
| Double-bottom height | 83.3 | 0.931 | 6.3 |
| Bottom-floor thickness | 71.9 | 0.977 | 5.0 |
| Bottom-girder thickness | 13.6 | 0.967 | 5.1 |
| Bottom-floor spacing | 73.3 | 0.972 | 5.4 |
| Inner-bottom deck thickness | 8.9 | 0.891 | 8.3 |
| Hopper-plate thickness | 44.2 | 0.912 | 7.5 |
Table 2: Per-constraint satisfaction rates in the retained random subset and constraint-surrogate validation performance. Random satisfaction is the percentage of the 6,020 retained random designs that satisfy each constraint. The and normalized root mean squared error ( ) values use all 1,204 designs in the class-stratified validation set and are calculated in logistic-transformed residual space. SM denotes section modulus.
| Constraint | Number of designs | Frequency among evaluated repaired designs (%) |
|---|---|---|
| Hopper stiffener section modulus | 745 | 11.94% |
| Transverse-bulkhead thickness | 505 | 8.10% |
| Horizontal transverse-bulkhead stiffener section modulus | 340 | 5.45% |
| Vertical transverse-bulkhead stiffener section modulus | 182 | 2.92% |
| Deck-plate thickness | 37 | 0.59% |
| Hopper-plate thickness | 25 | 0.40% |
Table 3: Constraints remaining violated after repair and evaluation using the implemented constraint calculations. Percentages use the 6,237 successfully evaluated repaired designs as the denominator.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
| Index | Name | Units | LL | UL |
| 0 | L_3h | m | 40 | 80 |
| 1 | B | m | 15 | 80 |
| 2 | T | frac. D | 0.25 | 0.667 |
| 3 | D | m | 10 | 40 |
| 4 | R_b | frac. Db | 0.75 | 1 |
| 5 | l_overhang | frac. L_3h | 0.03 | 0.05 |
Table 4: The 120 MiDShip parameter fields and their nominal random-sampling specifications, including units, lower limits (LL), and upper limits (UL).
| Index | Name | Units | LL | UL |
| 40 | blkhd trans stiff h | mm | 120 | 320 |
| 41 | blkhd trans stiff t | mm | 7 | 12 |
| 42 | blkhd trans stiff w | mm | 14 | 200 |
| 43 | blkhd trans stiff torc | bool | 0 | 1 |
| 44 | blkhd vert stiff h | mm | 120 | 320 |
| 45 | blkhd vert stiff t | mm | 7 | 12 |
Table 5: The 120 MiDShip parameter fields and their nominal random-sampling specifications, including units, lower limits (LL), and upper limits (UL) (continued).
| Index | Name | Units | LL | UL |
|---|---|---|---|---|
| 80 | bulk car bottom hopper t | mm | 15 | 35 |
| 81 | bulk car top hopper t | mm | 15 | 35 |
| 82 | num bulk car bottom hopper stiff | int | 3 | 6 |
| 83 | bulk car bottom hopper stiff h | mm | 120 | 320 |
| 84 | bulk car bottom hopper stiff t | mm | 7 | 12 |
| 85 | bulk car bottom hopper stiff w | mm | 14 | 200 |
Table 6: The 120 MiDShip parameter fields and their nominal random-sampling specifications, including units, lower limits (LL), and upper limits (UL) (continued).
| Requirement Number | Structural Constraint |
|---|---|
| ABS Part 3, Ch. 2, Sec. 4/3.1 | Minimum Double Bottom Height |
| ABS Part 3, Ch. 2, Sec. 4/1.2 | Minimum Double Bottom Height |
| ABS Part 3, Ch. 2, Sec. 4/5.1 | Minimum Bottom Floor Thickness |
| ABS Part 3, Ch. 2, Sec. 4/5.1 | Minimum Bottom Girder Thickness |
| ABS Part 3, Ch. 2, Sec. 4/5.1 | Maximum Bottom Floor Spacing |
| ABS Part 3, Ch. 2, Sec. 4/9.2 | Minimum Inner Bottom Deck Thickness |
Table 7: ABS rule-to-output applications used to formulate twenty-five constraint outputs for selected spacing, sizing, and stiffness requirements. For outputs governed by multiple applicable minimum requirements, the greatest required value is used.
| Violation count | SGLD seeds ( ) | Retained SGLD designs ( ) | Repair sources ( ) | Retained repaired designs ( ) |
| 0 | 0 | 322 | 0 | 4,952 |
| 1 | 3 | 145 | 3 | 914 |
| 2 | 10 | 29 | 10 | 206 |
| 3 | 12 | 0 | 19 | 142 |
| 4 | 0 | 0 | 44 | 23 |
| 5 | 0 | 0 | 103 | 0 |
Table 8: Violation-count distributions for the method-specific source populations and retained generated designs. All counts use the implemented evaluator underlying the reported results. The SGLD comparison is population-level because candidates were initialized from interpolations of seed vectors. The repair-source column contains the 3,127 unique source designs; the paired source mean reported in the text is weighted over the 6,237 retained source–repair pairs and therefore differs slightly from the unique-source mean shown here.