I need a proof-of-concept digital twin of a real building that can sit in any modern browser, rendered entirely in three.js. You may ingest geometry from LiDAR files, spectral photogrammetry outputs, or an existing CAD model I provide (Blender or similar). Once the mesh is in place, your task is to stream live IoT data straight onto the model and use that feed to predict the building’s health by analysing stress and strain in real time. The live feed will include temperature, humidity, vibration, rainfall, AQI and vehicle-movement counts. Visual cues—colour gradients, heatmaps, small vector arrows—should appear directly on the twin so anyone can spot areas of concern at a glance. Behind the scenes, a lightweight predictive layer should flag thresholds and issue warnings when stresses drift outside safe margins. Please produce a self-contained three.js dashboard that: • Loads the optimised 3D model quickly and allows orbit, pan and zoom. • Subscribes to the sensor endpoints (MQTT, WebSocket or REST—whichever you prefer) and updates readings with minimal latency. • Calculates and displays a live “health score” that blends current stress/strain data with short-term predictions. • Provides basic charts or panels for historical trends without leaving the page. • Ships with clear setup scripts, source code and a short read-me so I can deploy it on my own server. I’m open to your preferred stack for the back end—Node.js, Python or Go are fine—as long as the front end remains pure three.js/WebGL and works on Chrome, Edge and Firefox. If you have prior experience fusing LiDAR meshes with three.js or implementing MQTT/REST bridges, that will be a big plus.