A model is trained using thousands of past concrete pour records, where each record includes concrete volume, crew size, pump capacity, site access condition, and the actual pour duration. The model learns to predict pour duration for future projects.

A path-planning AI for an autonomous site robot learns to move from the material storage area to the workface. It tries different routes in a simulated site, receives positive rewards for shorter safe paths, and penalties for collisions or entering exclusion zones.

A model is trained using a large number of unlabelled construction site photos. Parts of each image are hidden, and the model learns to predict or reconstruct the missing parts before being used for other visual tasks.

An excavator records fuel consumption and travel distance every 10 minutes. There are no manual labels, but the system identifies unusual operating patterns, such as high fuel use with very low movement.

A robotic arm learns how to pick and place construction components by trying different actions in a simulated environment. It receives rewards when the component is placed correctly and penalties when it collides or fails.

A model is trained using site images that have already been labelled as “crack”, “spalling”, “corrosion staining”, or “no visible defect”. The model learns to predict the correct defect category for new images.