Automated Truck Load Configuration & Inspection of Prefab Construction Elements
Join AUTOLOAD_IRVA and explore how dynamic load planning can make truck loading of heavy prefab construction elements faster, more efficient and less dependent on expert knowledge.
Challenge
Loading heavy prefab construction elements onto trucks is a complex planning task. Today, the process largely depends on skilled workers and their knowledge of stacking, load stability and safety constraints. Planning is often manual and operators rely on predefined instructions during loading, making it difficult to respond efficiently to last-minute changes.
At the same time, increasing product variability and modularisation add complexity. Loads need to account for available truck capacity, axle loads and safe stacking, while the unloading sequence must match on-site crane operations and limited storage space.
Existing logistics software can support order planning, but does not typically calculate how construction elements should be stacked for efficient, safe loading and unloading. Existing stacking algorithms, meanwhile, are not designed for the specific characteristics and expert rules associated with heavy construction elements.
Project Goals
In this project (AUTOLOAD_IRVA), we aim to develop a truck loading assistant for the manual loading of prefab construction elements, capable of planning and adapting stacking configurations on the fly.
The solution will combine advanced planning algorithms with expert rules, BIM data, available truck space, transport requirements and loading constraints. Dynamic orchestration and live monitoring will enable the loading plan to adapt to the actual situation, while digital work instructions will guide operators through the process.
The project targets first-time-right loading, higher truck utilisation and greater productivity, with an ambition to make loading 20% faster. It also aims to improve on-site efficiency by reducing the need for buffers and enabling more components to be used directly after unloading. While initially supporting manual loading, the solution will be designed with future automated loading in mind.
Join our research
We are looking for companies that want to participate in our research and work with us on the valorisation of the project.
Interested?
Then complete the form below.
