Other
Rail catenary pole placement, automated
Days of expert engineering work, reduced to seconds.
Days to seconds · Engineering time
The challenge
A 148 km high-speed rail line needed 4,351 electrical (catenary) poles placed along its length. The spacing rules are anything but fixed: poles can sit up to about 65 m apart on straight track, but tighten to as little as 27 m on sharp curves, with the exact maximum set by curve radius and track cant read from a 36-page lookup table.
Nothing can be placed on a structure. On this line alone that meant avoiding 159 structures across 166 track segments: 126 culverts, 10 viaducts, 9 overpasses, 8 tunnels and 4 bridges.
Done by hand, the job means cross-referencing a spreadsheet, a 36-page PDF table and a structure list, then marking every position on a drawing, kilometre after kilometre for 148 km. It takes days, is easy to get slightly wrong, and starts over from scratch whenever the line design changes.
The approach
I built a Python system that does the entire job from the firm's own existing files, with no new data entry.
- It reads the track geometry (segment start and end, element type, curve radius and cant) straight from the engineering spreadsheet.
- It uses an LLM to parse the 36-page maximum-spacing PDF into structured, cached data, replacing the manual lookup that used to slow everything down.
- It derives the forbidden zones from the structure sheet, handling a tricky quirk where consecutive rows mark a structure's start and end with no explicit end column.
- It places every pole with a greedy algorithm that walks the line and pulls the spacing in tighter as curves and structures demand.
- It converts kilometre positions into real X/Y coordinates and outputs an Excel file for review plus DXF/DWG that open directly in AutoCAD.
Correctness was proven, not assumed. The output was compared directly against 4,351 pole coordinates the firm's own engineers had placed by hand as ground truth, backed by 68 automated tests covering geometry, structure handling, coordinate conversion, placement logic and output format.
Stack: Python, a greedy placement algorithm, LLM-assisted PDF parsing, and Excel/DXF/DWG output.
The outcome
Results that moved the needle.
- Days to seconds
-
Engineering time
A multi-day manual task became a single command.
- 4,351
-
Poles validated
Checked against the firm's hand-placed reference points.
- 68
-
Automated tests
Covering geometry, placement logic and output format.
- 148 km
-
Line automated end to end
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