In recent years, the issue of resource efficiency has also become increasingly important in construction engineering, as soil and rock account for more than 50% of mineral construction waste. Tunnel projects play a special role in this regard, as large quantities are generated at specific times and places. Due to the high degree of mechanisation and the associated advantages in terms of construction performance and safety at work, almost the half of tunnels is built with TBMs (TBM). For documentation and control of the construction process, these are equipped with various sensor systems that provide comprehensive data sets. But in this area, modern data-driven processes have not yet found a wide application. The overall objective of the REMATCH project is therefore to use the data sets from TBMs, with the help of AI methods, to enhance the recycling of the large quantities of tunnel excavation material. In this regard, an innovative real-time measurement system for material characterisation is to be de- veloped which gives decision support on the question if soil is “usable” or “not usable” for other purposes and thus has to be disposed of in a landfill. This system will base on several approaches using AI methods: firstly, on image recognition of excavated material, secondly, on intelligent data processing of TBM data, and, thirdly, on a knowledge database.