Built for the Digital Twin environment, Gauge3D performs railway clearance assessments at all stages of the asset life cycle.
Powered by the Microsoft Azure Web App Service, everything is done in the browser. Just sign up, log in, and start gauging.
Import survey point clouds (LAS/LAZ format) and assess clearances to surveyed trackside assets. Identify the causes of clearance infringements with the easy-to-use track alignment camera.
Undertake clearance assessments to proposed trackside structures using 3D CAD model data (OBJ/DXF format). Gauge3D operates in the same coordinate system as the imported CAD model, achieving compliance with modern BIM standards.
Gauge3D features touchscreen support for mobile devices, making it possible to use on site. Large point clouds can be viewed on older hardware using a bespoke dynamic processing method.
Load existing and proposed track geometry data using 3D model data (DXF) or an alignment geometry report (LandXML). Combine alignments, point clouds and 3D model data to undertake structure and passing vehicle clearance assessments.
Automatically process point clouds into cross-sections used for clearance assessments. Gauge3D features vegetation and dust filtering, plus cross-section clean-up tools. Export processed cross-sections to SCN format for use with other gauging software.
A 3D swept envelope of the vehicle is used to generate vehicle cross-sections for clearance analysis, modelling accurate vehicle throws at transition curves and switches where geometric changes are instantaneous.
Using client-supplied data, Gauge3D can undertake kinematic vehicle modelling with a model trained by machine learning. Alignment speed can be imported and edited to accommodate design requirements.
Clearance assessment results can be exported to universal 3D file formats (DXF & OBJ) for review in the Common Data Environment using CAD software. Multi-disciplinary design teams can easily see where clearance infringements are located.
Automatically generate optimised rail alignment geometry using the Gauge3D Design module. Using TensorFlow machine learning processes, Gauge3D clearance results can be used to find the optimal route through the space available.