In the pursuit of renewable energy, hybrid testing potentially holds the key to extending the lifetime of offshore wind turbines, ensuring we maintain our momentum. As the offshore wind energy sector surpasses 30 years, wind turbine farms are reaching the end of their certification periods, with potential yet to be fully explored.

Current methods for predicting the remaining lifetime, involving the analysis of operational data and practical inspections, often fall short. Instead, hybrid testing a set of critical components, extracted from existing wind turbines, offers a more reliable prediction. Here, a physical component interacts with a numerical model, simulating the dynamic behavior of the remaining wind turbine. The key challenge of this method lies in the clash between physical and numerical realms, replicated by a testbench, as it is subject to instability.

This project aims to develop a robust framework for hybrid testing of wind turbine components and implement it in a real case study, specifically focusing on testing pitch bearings.

With a growing global interest in larger wind turbines, along with floating features for deep water applications, the industry faces costlier and more challenging maintenance tasks of wind turbines. A valuable tool to assess this challenge could be a Digital Twin which monitors the wind turbines in real time and estimates when maintenance is needed.

A Digital Twin combines a physical system, e.g., a wind turbine, and its computer simulation model(s) with data exchange in between. The physical system is equipped with sensors, which, combined with the simulation models, enable the estimation of unobserved or unmeasured quantities. Such estimated quantities could be the fatigue in the steel and/or the changing soil stiffness at the foundation due to the fluctuating load pattern a wind turbine is subject to.

The project aims to develop an algorithmic framework that is suitable for crafting digital twins of offshore wind turbines (WTs).

In countries with cold winters, such as Denmark, there is increasing evidence of summer overheating in residential building stocks. Climate change, winter heat retention as the main focus of the thermal design, and high insulation levels due to renovation are the main causes. Hence, buildings are becoming increasingly uncomfortable for their occupants during hot summers, leading to a rise in hospital admissions due to heat related respiratory diseases and higher mortality rates. The Danish Ministry of Environment, therefore, predicts an increase in air conditioning installation. Aiming to waive this energy-intensive measure, which contradicts the current EU target to reduce the energy consumption of the building stock, passive strategies, such as natural ventilation, could alleviate this problem. From this background, this research project aims to explore and provide a practical solution that uses passive strategies to improve summer thermal comfort in renovated Danish residential buildings. In this way, the solution counteracts the predicted increase in cooling energy due to installing air conditioning and further provides resilience against climate change. The PhD project has the following objectives:

• Identify the overheating potential of the Danish building stock
• Identify passive architectural approaches commonly researched and applied in Denmark
• Develop, apply, and quantify the potential of the selected natural ventilation strategy
• Investigate the indoor environmental quality of the selected natural ventilation strategy under varying boundary conditions by computational fluid dynamics using a validated model

This research was funded by Independent Research Fund Denmark, grant number “0217-00018B”.

The issue of sustainability is becoming more and more the central key to a better future. This focus has also taken hold in the Danish construction industry through, among other things, the sustainability certification of buildings, and the Danish building regulations’ requirement for life cycle assessments for newly constructed buildings. A commonly used depiction of sustainability divides the concept into environmental, economic, and social sustainability. Both environmental and economic sustainability are reasonably well-defined, with clear targets and assessment methods. Social sustainability, on the other hand, is usually more vaguely defined, focusing on concepts such as well-being, health, safety, and equality at both the level of society and individual. To fully address the task ahead, building designers to address sustainability, they need to be introduced and equipped with adequate methods and decision-support tools to facilitate the integration, assessment, and evaluation of social values while also being informed of the environmental and economic consequences of the choice. In the light of this, the main objectives of this PhD project are:

• To create an inventory of the social intents that are commonly considered by the building design team in Danish construction and renovation projects,
• To evaluate the relationship between social intentions, activities to promote these intentions, and the perceived social value of the finished building, and
• To develop an evidence-based decision support tool to support decision-making regarding the inclusion of social intents in building design.  

This project has received funding from the European Union’s Horizon 2020 research project, PROBONO, under grant agreement no. 101037075.

Cable icing is a critical issue for bridges based on cable systems for two main reasons: (1) the aerodynamic behavior can be altered inducing large vibrations, and (2) the ice can detach from cable surfaces due to mechanical or thermodynamic reasons creating large risk for the traffic flowing on the deck. Nowadays, several solutions were proposed for protecting cable systems in bridges from the icing hazard, but they are still far from being mature systems providing full protection.

One of the critical issues of these systems is the reliable detection and quantification of the ice accretion on the cable system. Accurate detection and measurement of the ice became critical to manufacturers and operators of cable systems, in order to minimize, predict or quantify these issues. Ice detection systems exist, but none of them is reliable enough to perform under such extreme conditions. An ice detection system can provide early warning ice alarms, ice accumulation rate information, and accurate visual information of ice profiles to the operator.

Computer vision techniques offer promising non-contact solutions to civil infrastructure condition assessment. This research project aims at exploring the opportunities of computer-vision techniques for the detection, qualification, and quantification of ice and snow accretion and falling on bridge cable systems. It is envisioned that these methods can reliably identify the ice accretion and thus provide an early warning indication to bridge operators. Subsequently, the key challenges that persist toward the goal of automated vision-based ice control on bridge cables and industrial applications should also be explored in this research project.

The AECO sector is facing an ever-growing demand for critical innovations in terms of digital transformation and technologies such as ICT, Industry 4.0, artificial intelligence (AI), big data, blockchain technologies, building information modeling (BIM), etc. The digital construction industry can provide an evidence-based understanding of the built environment. If properly implemented, benefits can be realized by creating, managing, and maintaining information throughout a building's lifecycle, from concept design to the eventual disposal of the building. It enables all stakeholders involved in a building project to take advantage of access to real-time federated data sources linked to the physical built environment, commonly referred to as the Internet of Things (IoT) or a Digital Twin (DT).

Digital Twin allows the digital transformation of physical buildings by integrating their digital models and analytical simulation engines with their real-world data, maximizing the value of data, and creating beneficial synergies across their entire life cycles. The soul of a Digital Twin lies in its data. To create a digital replica that mimics the behavior of a building, heterogeneous data must be extracted from multiple sources on the building in question. Existing limitations create a lack of complete and reliable information on buildings, which leads to data modification and limits the scope of benefits that can be achieved through subsequent modeling. For those buildings for which a building information model is available, reliable data can be extracted and integrated with data from multiple sources to create a Digital Twin for a specific purpose (e.g., Improved building design, enhanced energy efficiency, predictive maintenance, etc.) of the building under consideration. In this framework, this PhD project aims to achieve the following objectives:

• To explore BIM and linked data standardization formats and their associated information flow for the development of Digital Twin.
• To select and identify the level of information for a specific purpose and extract the required data for the development of Digital Twin, while linking real-time data to the information model.
• To propose and evaluate a system architecture to reduce the complexity of creating a Digital Twin for the specific purpose at a building level.

This project has received funding from the Higher Education Commission of Pakistan

With the aim of implementing the concept of sustainability, numerous measures are emerging today that address the increasing challenges of our society, which include climate change and resource scarcity. The building construction sector is one of the largest contributors to the emission of greenhouse gasses, and one of the largest producers of waste. Building with timber, which is a renewable resource, can reduce the environmental impact of buildings. In addition to that, it is important to extend the service life of construction materials, including that of timber, as much as possible. This objective can be achieved by reusing, upcycling and recycling construction elements, and by developing modular, reconfigurable and reusable construction elements and systems.

Therefore, this research sets out to investigate construction solutions that allow timber building systems to be more reusable, and that allow for a wide range of design configurations. At the same time, for timber-based structural systems, factors such as the mechanical properties of engineered timber products and the corresponding prefabrication or digital fabrication methods are essential.

Moreover, this doctoral research aims to investigate new modular wooden components based on reclaimed and discarded material. It will investigate digital fabrication and construction processes, including digitally fabricated joints of these components. Finally, it will propose a construction system and explore and validate its design space by means of a combinatorial design, and by proposing construction components that combine virgin and reclaimed material in an optimal way, depending on the structural needs.

Wind, solar, wave, and tidal energy play a central role in achieving the decarbonization of our energy system. As a consequence, a large portion of future power grids will be installed offshore in the form of floating structures interconnected by a shared mooring system in a scalable and cost-optimal way.

Optimizing such systems requires accurate prediction of hydrodynamic loading exerted on floating structures. Despite the tremendous development of computational modeling tools, hydrodynamic loading models still require extensive experimental validation to provide accurate predictions. Such experiments are time-consuming and, therefore, limited in duration and number.

This project aims at developing and implementing machine learning algorithms for the design of optimal hydrodynamic experiments. The goal of the algorithms is to provide information about a floating model such that the cost of calibration of hydrodynamic loading models is minimized, and uncertainty on responses of interest can be quantified.

An improper thermal environment may result in a negative spiral of development for animals, especially domestic animals raised in a relatively closed environment. Some regions of high latitude, i.e. Northeast China, are main areas for dairy production. However, the climate there in winter is especially cold and the average temperature can be as low as minus 20 degrees. Additionally, high-humidity air and high concentration of harmful gases, i.e. Carbon dioxide, methane, ammonia, and nitrous oxide, appeared in dairy cattle barns contribute to a passive impact on the production and reproduction of cows. Hence, it is always a challenge to achieve a balance between the construction economy and good indoor climate towards dairy cattle barns in these regions.

The aims of the project are: 1) to introduce an innovative ventilation design for optimizing the thermal and airflow conditions in these different types of cattle barns; 2) to set up a dynamic predictive model to provide a precision environment control strategy at individual animal or defined zone level; 3) to improve animal welfare and to reduce environmental impact in cold region; 4) to generate a design standard for ventilation and construction of cattle barn with considerations of energy saving and animal welfare.

According to the Danish National Annex to Eurocode 7, part I, the shaft resistance for a bored cast-in-place pile should not be assumed to be greater than 30 per cent of the shaft resistance of the corresponding driven pile, and the toe resistance is maximised to 1000 kPa. Since 1977 this principle has been enforced (code requirement) in Denmark, allegedly due to execution problems encountered in one or two un-documented case histories.

Hence, it is widely recognised that this reduction in bearing capacity is believed to be overly conservative. If the bored cast-in-place pile is established correct, the reduction of the shaft resistance is still applicable due to limited understanding of the governing mechanism and limited knowledge of the complex soil-pile interaction.

A consequence of this lack of understanding is that bored cast-in-place piles are often designed too conservative, and the bored cast-in-place piles are built more expensive than what is required.

This Industrial PhD project will investigate the shaft and toe resistance of bored cast-in-place piles based on full-scale field tests, model field tests, geotechnical and structural monitoring, and develop a first order analytical method for determination of the shaft (and toe) resistance for bored cast-in-place piles.

In my PhD project I work with social value creation in the built environment from a relational approach. Inspired by design anthropology, architectural anthropology, and posthuman practice theory, I explore buildings as relational performances, rather than static objects, and view design and use as part of the same continuous process of emergence. Understanding the relationship between people and environments as dynamic and relational, enacted through the performance of sociometrical practices, it is not the buildings as physical objects that interest me, nor value or performance in any absolute sense. Rather, I explore how value is co-created or co-performed between buildings and inhabitants, how we can understand these relationships and how we might work to support them. The central question is not what buildings are (buildings-as-entities), but what they make possible (buildings-as-relational-performances).

Through a multi-sited ethnographic approach, I explore how values get formulated, how they are negotiated and designed into buildings, and how they “live on”, after the buildings are taken into use. In the second part of the project, I present the concept of social commissioning as a particular approach to supporting social value creation, based on a relational approach to design, in the intersection between building-as-project and building-as-lived-space.