Automated and AI-supported generation of shift logs in conventional tunnelling

In conventional tunnelling, digitalization has been implemented in various processes,
tools and machines. They assist in the excavation and support processes and are also able to document cycle times. However, in daily operations on-site, shift log entries are still generated manually instead of being extracted from machina data, often resulting in a time delay and thus in inaccuracies due to forgetting. Additionally, the level of detail provided, with a typical granularity of 15 minutes, is not sufficient for several evaluation tasks such as recalculations.
Thus, the I-WASP research project attempts to generate shift logs and shift time documentation based on automatically collected machine data. This has the advantage of providing consistent, on-time documentation with high temporal resolution, while at the same time reducing the manual workload for foremen and site managers, leaving only the need for plausibility checks.
By combining advanced position sensing technology with inertial measurement technology, data are collected without the need to access the different machine's operating hard- and software, for instance for loaders or drill rigs. This allows for a system that is completely independent of the types and brands of machines used. The sensors are mounted on various machines and connected wirelessly to transfer the data in real-time to the data management and processing system. Using rule-based algorithms and artificial intelligence, individual sub-processes can be identified and visualized in chronological order.
A just-in-time illustration of the process times enables continuous support of the site management, while detailed documentation of the work process allows rapid analysis of the as-built status. Decision-making processes based on this analysis offer the advantages of quicker optimizations of the construction process and extraction of data for invoicing and contract management.