Research

The project will establish an Integrated Research Centre (IREC) for advanced numerical and analytical analysis of reliability, performance, and service life of existing and new civil engineering structures such as bridges, tunnels, protective barriers, etc. The IREC will aim to integrate the knowledge and tools of the cross-border partners from BOKU-IKI and BUT-STM and offer a unified and efficient access and service to the target groups on both sides of the border. This synergy will enable easy cross-border transfer of parts of solutions to complex problems. Within the framework of its activities, the Centre will offer both knowledge transfer in the form of seminars and publications and direct application and consultancy activities towards partners from engineering offices and infrastructure operators and owners. The common services, methods and tools will be intensified and adapted to the current needs of the target groups and thus prepared for an optimized operation of the research centre after the end of the project. The research centre will be autonomous after the end of the project, self-financed by the funds raised by providing services to the technical offices and the professional community. Without the joint involvement of the two cross-border partners, the required comprehensiveness and availability of the services offered could not be achieved, and thus the necessary change in the approach of the professional community to the application of modern advanced numerical methods to improve and streamline the design and assessment of building structures and to ensure sustainable and reliable transport infrastructure. For the duration of the project, the research centre will consist exclusively of two founding project partners. Future expansion of the research centre with additional partners from research institutions or industry is not excluded, in line with the needs of future developments in the field and the needs of the programme regions.

Hydrographic survey of five scours in the riverbed of the river Wienfluss 1) Hydrographic survey a) Unmanned survey boat for use in very small waters The survey boat includes a semi-autonomous navigation unit with survey-grade satellite navigation unit (GNSS) with double antenna system for precise alignment of the survey boat. b) Multibeam echo sounder (MBES) Norbit iWBMS (minimum water depth under the transducer: 0.2 m) The MBES scans the bottom of the riverbed with 512 beams per ping. The possibility of swiveling the fan allows adjacent areas to be detected which cannot be navigated by boat due to the water depth, but in particular steeply rising bank areas (drop-offs) can be detected. c) Post-processing of the echo sounder measurement data The existing shallow water depths and narrow scour areas cause strong multipath reflections of the signals, which require extensive manual post-processing of the measurement data. This includes manually cleaning the point cloud of multipath reflections and filtering the data sets for subsequent DTM creation. DTM creation of the recorded underwater area with a grid width of 20 cm 2) Terrestrial photogrammetric survey and evaluation of the scour edge areas Due to the greatly reduced water level caused by the pumping out of the Wien River, the scours are no longer covered with water up to their upper edge. This means that, contrary to the original plan, the scour edge areas cannot be recorded with the echo sounder. For this reason, it was decided to carry out a photogrammetric survey and evaluation of the scour edge areas. The photogrammetric images are analyzed using the structure from motion (SFM) method and the surface is displayed as a 3D point cloud. The point cloud is filtered and artifacts from the evaluation are removed manually. In order to merge the underwater DTMs with the surface DTMs of the scour areas, the water-covered areas of the surface model are masked out manually.

Our proposed research initiative seeks to propel machine learning into the forefront of geotechnical engineering, with a vision to address critical challenges and revolutionise the field for the betterment of society. The overarching goals of our project align with the need to confront uncertainty, combat climate change through zero carbon emission strategies, address soil parameter heterogeneity, expedite finite element (FE) calculations e.g., for reliability analyses, and enhance design efficiency to reduce material consumption, particularly in the context of concrete. By undertaking this multidimensional approach, our research aims not only to apply machine learning in geotechnical engineering but to fundamentally transform the field, ushering in a new era of efficiency, sustainability and resilience. Through collaboration and innovation, we aspire to make machine learning an integral and indispensable tool for addressing the complex challenges faced by geotechnical practitioners in the 21st century.