Research

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.

The objectives for this expedition are focusing on a better scientific understanding of Lake Altaussee, Austria through its cultural, geological, and ecological significance. The priority was to obtain a multi-beam sonar map of Lake Altaussee and a sub-bottom profiling of the lake bed. Biologists from the Scripps Institution of Oceanography, San Diego, California, and from the Paul Ricard Oceanographic Institute (France) collected samples from all water-entry points and from the lake surface area. At the deepest part of the lake (74.2 m), a Deep Trekker remotely operated vehicle (ROV) provided an important view of a geological occurrence: images of colored sediment and rock suggested the presence of iron ore. The Team also collected water, sediment, snow and air samples destined to be tested for microfibers, with the goal being to understand the dynamics of these fibers and eventually, by collecting and analyzing two juvenile fish and a dozen copepods, determine if they enter the local food web.

The project targets to develop a scaleable and multiplyable businessmodel for climate change adaptation in the building stock, at the interface of public and private space. The implementation of a vertical greening pilot and street greening in the Rosaliagasse Vienna is prepared, building upon the results of the previous exploratory study, referring to legal, constructive, greening and financial basics. Meidlinger L-Demo continues here and will clarify the research questions referrint to the design, plant structures, material and constructive solutions. The scientific monitoring will focus ecological and microclimatic effects on the object, the street space and the building. MEIDLINGER L – Demo will elaborate a scaleable and multiplyable model for Vienna and other cities and urban areas. Technical and legal feasibility will be proved. The concepts referring to greening solutions, rain water management, mobility, sanitation, energy gain and the added value for the inhabitants can be transferred to other neighbourhoods and urban districts.