The development of offshore wind turbine (OWT) foundations toward larger sizes and deeper embedment presents challenges for traditional foundations, which have long installation times and cause significant environmental disturbances. Helical piles, with their distinct installation method, offer shorter installation times and reduced environmental impact, making them a strong candidate for OWT foundations. However, the lack of efficient methods to assess offshore HP load-bearing performance under various conditions limits their broader application. This project aims to develop an open-source decision-making tool to optimize offshore HP design using data-driven techniques. By incorporating physics-informed machine learning algorithms (PIA), the tool will enable surrogate modeling to replace traditional numerical analyses, allowing accurate predictions of HP’s static and dynamic performance, including installation effects. This will improve design processes, reduce costs, and contribute to sustainable offshore engineering practices. The fellowship has three key Research and Innovation Objectives (R&IO): (1) to establish a validated numerical model that simulates HP load-bearing performance, including installation effects, which will form a database for (2) developing PIA-based data-driven models to assess HP capacity. These models will then be integrated into (3) an open-source decision-making tool using reliability-based foundation design approach (RBFDA) to optimize HP design under various conditions. The fellowship will be conducted at NGI, Norway, with a secondment at OsloMet, Norway. The skills gained will enable the researcher to become an expert in HP technology and support the pursuit of a tenure-track position. Furthermore, the project aligns with the European Green Deal and the UN Sustainable Development Goals (SDGs) by contributing to clean energy and sustainable engineering.

Offshore floating wind turbine (FWT) provides an efficient solution to address climate challenge. Integrated analysis of FWT is vital to reduce the uncertainties in design and save costs. In the field, seabed trenches dramatically increase the failure risks of mooring system and raise operation and maintenance costs. However, the mooring line-seabed interaction related to soil erosion and trench development is not considered in design code (DNVGL-ST-0119, 2018). Thus, two objectives of this project are: 1. reveal the trigger factor for seabed trenching due to the mooring line-soil interaction; simulate the trench development with time. 2. establish the macro element model considering seabed trenching process; integrate the model into the integrated analysis tool of FWTs, e.g., SESAM. A total of 3 packages are designed comprehensively. In the seabed trenching package, conduct 1g erosion test to reveal the trigger factor for seabed trenching (WP1.1). Then propose the soil erosion model based on analytical analysis based on 1g erosion test (WP1.2). Finally, the trench development will be simulated by the trench profile model (WP1.3). In the mooring line-seabed package, get the chain resistance by 1g result (WP2.1); establish the macro element model (WP2.2); considering seabed trenching process, which can consider the soil degradation, erosion, removal (WP2.3). In integrated analysis package, integrate the model into the integrated analysis tool of FWTs, e.g., SESAM (WP3). The new development of the soil erosion model, seabed trench model and macro element of mooring line soil interaction are the new computation tool for scientific analysis. Integrated analysis tool for FWT will be an important and powerful tool for FWT analysis considering mooring line-soil interaction, which will contribute to standards’ setting. This will be a groundbreaking progress and will fill the gaps in the norms and standards (DNVGL-ST-0119).

The resilient and sustainable development of railway networks is fundamental for economic and social progress, especially under the current climate. To achieve this, we must embrace new innovative solutions while replicating best practices that benefit nature. Alongside increasing the infrastructure resilience and safety, we need transformative changes in railway earthwork asset development that go beyond prioritising decarbonisation and digitalisation and seek to restore nature, safeguard biodiversity and produce positive outcomes for human societies. Leveraging cutting-edge technologies like AI, earth observation, statistical analysis, advanced numerical modelling, laboratory tests and sensor technologies, RESOLVE seeks to integrate conventional engineering practices with life sciences, empowered by data science. Furthermore, RESOLVE will pioneer synergistic solutions that tackle climate change adaptation in railway earthwork assets while creating opportunities for nature restoration. This attempt involves developing nature-based solutions (NBS) and environmentally friendly construction materials, with the additional aim of fostering biodiversity restoration beyond ground reinforcement. Highly skilled researchers and practitioners, capable of dealing with such problems are scarce and in high demand by academia and industry. Thus, formed with 11 world leading research organisations and 3 companies across Europe and internationally with expertise and facilities in these areas, RESOLVE aims to ensure comprehensive, robust and implementable solutions are obtained for railway network climate resilience built and eco-sustainable development. The network is carefully designed to enables research and innovative staff exchange across all aspects. RESOLVE secondees will enjoy a highly integrated, interdisciplinary and intersectoral staff exchange, sharing know-how and skills development environment through the planned secondments, networkwide events and local trainings.

The demands on marine space in the EU and worldwide waters have never been greater, driven by the need to provide energy security, develop essential renewable energy infrastructure, create social and economic value and ensure the restoration and future resilience of marine biodiversity and ecosystem services. Whilst Offshore Wind Farms (OWFs) play a key role in combatting climate change, they are not only vulnerable to climate change-induced hazards but also impact marine biodiversity. Focusing solely on engineering aspects to optimise risks and reliabilities in the design of OWFs without addressing the impact on marine biodiversity is sufficient for meeting the requirements of resilient and sustainable development in future OWFs. Therefore, an urgent shift is necessary from the conventional engineering-oriented design approach and mindset to a more comprehensive approach that integrates both engineering aspects and environmental considerations. BETTER assembles a diverse, multidisciplinary team to train a new generation of Doctoral Candidates (DCs) capable of addressing ambitious scientific objectives. The training environment is highly integrated, cross-disciplinary, and intersectoral, enriched through secondments with non-academic partners. This collaborative approach advocates for a paradigm shift in OWF design, promoting a comprehensive strategy that integrates engineering with deep considerations for environmental impacts. BETTER emphasises critical learning, fostering solutions for climate-resilient OWF construction and sustainable marine development. By providing a collaborative, cross-disciplinary training environment, BETTER equips the 15 DCs with the skills, knowledge, and perspectives to navigate the intersection of engineering, environmental sustainability, and marine biodiversity. In doing so, BETTER addresses immediate challenges and lays a foundation for a sustainable and resilient future for offshore wind energy and marine ecosystems.

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.