Description

Engineers rebuild similar bridge models for every option, reapply the same load cases, and pull results out of the GUI case by case. This session covers two parametric Python tools that remove that loop by driving the model through the MIDAS CIVIL NX Open API, which exposes CIVIL NX as structured data over a JSON request/response layer.

The first is a Vierendeel truss bridge generator, written during a tender design where geometry was expected to change repeatedly for non-structural reasons. A small parameter set — span, vertical hanger spacing, depth, member sizes — builds the entire model in CIVIL NX, runs a linear buckling analysis and checks deflection, then writes each run to its own Excel tab with a master comparison tab. In the demo, a 42 m span option returns a utilization of 1.50 against an L/600 limit and fails, while the next option in the same batch passes.

The second is a network arch bridge optimization tool, used for preliminary optioneering and for a conference paper on rise-to-span ratio, hanger inclination angle and hanger count under AS 5100.2 loading. Arch generation, arch inclination, top bracing pattern and radial hanger arrangement all come from one parameter block, and the script defines traffic lanes and the M1600 vehicle before running linear static and influence line moving load analysis, graphing design actions along the arch and tie in Excel. Both tools rest on the same features — programmatic modeling, batch processing, and automated analysis and results extraction — and the speaker's closing point is that automation clears repetitive work rather than replacing engineering judgment.