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.

Large concrete structures such as bridges, dams and tunnels are often exposed to water flowing at high speeds and carrying a substantial amount of debris that causes surface damage due to mechanical erosion. The damage, which is also known as abrasion, leads to a premature end of the service life if the structures are not designed properly. The economic, societal and environmental costs of poorly design infrastructure is colossal and should therefore be avoided.

This project aims to develop a practical design guidance regarding concrete abrasion for hydraulic structures from a long-term durability perspective. With this being said, a central task is to establish the relation between the actual abrasion rate and the relevant parameters, including hydraulic parameters and concrete properties. Once the abrasion rate is known, the service life of the structure can be designed with high confidence. More specifically, the main objectives include:

1. Develop a testing method which can adequately simulate field conditions for concrete abrasion. In addition, the method should be practical enough for use in normal concrete labs.
2. Investigate the mechanisms behind abrasion induced concrete damage and identify the governing parameters.
3. Quantify the effects of the (governing) parameters on abrasion. More than often, only qualitative results are reported in literature.
4. Establish a mathematical model which can effectively predict the abrasion rate in field.

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.

The initial design phase is a stage in the construction process that is often neglected because it is highly time consuming. This is an issue because the initial design phase holds the most influence on the success of the final building design. Today the current procedure of finding the overall structural layout is based on a trial-and-error approach with very few iterations. This PhD project aims to redeem this untapped potential by creating a tool that can automate the process of creating optimized design suggestions in the initial design phase based on architectural drawings and models.

Designing optimum structural solutions involves a holistic approach because of the many design-variables, objectives and constraints. The project will apply reinforcement learning algorithms coupled with surrogate models and expert systems to handle the high complexity. Additionally, the tool will be constructed with nested loops where each level represents an increasing fidelity level of the calculations. The development of the tool will also extend to areas of interaction, automation and visualization to improve the mediation of the results.

The strategy of the Danish government is for the Danish energy production to be free from fossil fuels by 2050. This requires renewable energy production, which is typically controlled by weather conditions and do not follow the energy demand. A challenge arise in aligning the energy demand to the energy production, either by storing energy or shifting the demand in time. Previous studies in and outside Aarhus university have found a great potential of using model predictive control (MPC) of residential space heating to shift the energy demand in time by exploiting the heat storage potential of thermal mass. It is however yet to be investigated how the MPC reacts towards disturbances from occupant behavior.

Occupants affect the MPC in two ways; firstly, the MPC is set to follow a set of temperature conditions, which change with the occupancy, secondly, the unpredictable behavior of occupants will likely create disturbances in the system and affect the potential of the MPC.

The object of this project comes down to the overall question; do we need to account for occupant behavior in the MPC model? A sub-question related to this is how occupants are affected by MPC of space heating.

The number of piglets per litter per sow has increased drastically, which means that the heat production from the sow has also increased greatly. This presents a challenge for sows in hot weather, and cooling the highly productive sows is imperative. In order to cool the sow efficiently, the mechanism of the physiological reaction of sows in a hot environment and the heat release to the environment should be investigated.

The PhD project is conducted through a combination of the numerical simulations and experimental investigations to: (1) develop mathematical models of heat transfer coefficients based on the conditions that the sow’s body is cooled wholly or partly; (2) to model the heat transfer from sows to the ambient and combine the heat release process with the physiological reactions based on reasonable assumptions; (3) to investigate and evaluate the chill effects by using different strategies to cool the sows.

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.

Interest has been growing recently in floating offshore wind turbines (FOWTs), along with a rapid growth in wind energy more generally. Although FOWT is considered the most promising candidate for future offshore wind energy, its mass application cannot be realised before solving the vibration and stability problems, since the floating structure is subjected to stochastic wind and wave loads together with mooring loads.

The main aim of the project is to develop rigorous theoretical and numerical models for carrying out stochastic dynamic analysis and reliability-based design and maintenance of FOWTs subjected to extreme wave loads. Therefore, different models need to be developed during the project, including a mechanical model of the FOWT and a stochastic model of the extreme wave loads. All the proposed theoretical models will be verified and validated by existing codes, experimental results or measurements.

The project also aims at developing novel structural control techniques for FOWT under extreme wave loads.