What Drives Jumper Unity Values?

TL;DR

Why this study

Given that the model works, the next question is “why?” This article walks through a deep‑dive feature‑importance study for our ML model that predicts per‑element unity along jumper structures. The goal isn’t just a ranking; it’s intuitive, engineering‑useful guidance on which inputs the model relies on—and by how much.

Along the way, we call out pitfalls (notably a normalization mismatch), explain our methodology choices to keep the analysis physically meaningful, and discuss how the results should inform design, data QA, and model use.

The setup: what the model sees

Training standardized all 32 global inputs using the dataset mean/std. For this study, we applied the exact same normalization before inference.

Baseline performance and configuration

Results

1) What moved the needle most (permutation importance)

Scrambling a single global parameter reveals two standouts: end expansion and pipe properties. Both dwarf other features, with geometry and certain system inputs following.

FeatureΔMSE (mean)ΔMSE (std)
Hub2 EndExpansion (in-plane)0.1535930.000289
Hub1 EndExpansion (in-plane)0.1530820.000563
Pipe Outer Diameter0.0919330.000227
Segment 5 Length0.0323370.000076
Segment 3 Length0.0321590.000114
Design Pressure0.0307000.000108
Segment 7 Length0.0233220.000147
Segment 4 Length0.0146150.000060
Segment 1 Length0.0114380.000077
Pipe Wall Thickness0.0068890.000010

Permutation Importance

2) What happens when we remove whole groups (ablation)

Here we group the features into 6 groups: End expansion, Geometry, Pipe properties, System, Vertical movement, and Tolerance. We then remove each group (either zeroing or median the values) and measure the performance drop. Permutation isolates marginal effect; ablation asks “what if this entire set were typical or idealized?” We apply physically meaningful baselines and measure the performance drop.

Group (baseline)New MSEΔMSEΔ%
End expansion (zero)0.1736570.173559177,216.32%
Geometry (median)0.0721110.07201373,530.14%
Pipe properties (median)0.0635470.06344964,786.20%
System (median)0.0175500.01745217,819.70%
Vertical movement (zero)0.0038080.0037103,788.44%
Tolerance (zero)0.0034680.0033703,441.00%

Ablation study

3) Does position matter?

We also tested the effect of position on the model. The position of each element is encoded from 0(Hub1) to 1(Hub2), and we tested the effect of zeroing, ones, and linear ramp. We found that the model is not very sensitive to position, and the linear ramp is the closest to the original in most cases.

Positional Sensitivity

What this means in practice