PhD candidate: Sondre Ninive Andersen

Main Supervisor: Geir Mathisen

One way of reducing the cost of infrastructure maintenance is to continuously monitor the health of the structure using sensor networks. This approach allows remote monitoring, reducing the need for periodic inspections, lowering cost. However, typical sensor networks with sensors connected through wires to a data acquisition unit are expensive, both in equipment cost and in installation. New technologies have made it possible to employ wireless sensor networks, with inexpensive sensor nodes spread across the structure, all communicating wirelessly to a base station. This reduces the cost of installation, allowing such cost-saving networks to be employed more widely than before.

However, while data can easily be transmitted from wireless sensor nodes, supplying the nodes with energy is more challenging. Batteries can be used, of course, but these would eventually run out, and so would need to be replaced. Instead, ambient energy can be harvested from the environment, either from light, wind, temperature differences, vibration, or other sources. While this is possible, the amount of energy that can be extracted is usually very limited, so it is critical that the sensor node is as efficient in its energy consumption as possible.

The goal of my project is to formulate a general method for modelling the factors that impact the energy use of such a sensor node, and work towards a solution to this optimization problem.
 

PhD candidate: Joakim Sellevold

Main supervisor: Nils Rüther

In a changing climate, it is increasingly important to protect infrastructure against floods. One of the most commonly used flood protection measures is the culvert: an open conduit that allows water to flow safely through embankments.

In recent years, stricter design criteria for flood protection of roads have been implemented in Norway. A large percentage of existing culverts is therefore likely under-designed, meaning they will not be able to safely handle larger floods. The present project will expand existing design methods, by investigating the effects of partial blockage and tapered inlets on flow of water through culverts. 

Main goal 1 – Effects of partially blocked culverts:

It has been documented that blockage of culverts reduces discharge capacity, causing increased local flooding and erosion in the area near the culvert. However, the present design methods do not account for the effect of partial blockage of the inlet in a precise manner. The first goal of the present project is therefore to adapt the current design methods to account for the effects of various blockage scenarios. This allows for better risk analysis related to blockage of culverts during floods.

Main goal 2 – Optimized tapered culvert inlets:

Tapered inlets have a funnel-shaped transition from the inlet section to the main barrel of the culvert. The use of tapered inlets can increase discharge capacity significantly for culverts under certain flow conditions. Such designs have been used at least since the 1960s, leading to increased performance and reduced costs. However, existing tapered inlets are of simplified designs, adapted to older production methods. The second goal of the present study is therefore to develop novel, standardized tapered inlets that can be integrated into existing product lines. The designs will be optimized for hydraulic performance. This will increase the discharge capacity without increasing the overall pipe diameter of the culvert, leading to reduced costs and carbon footprints. 

Research methodology and implementation of results:

For both partial blockage, and tapered inlets, physical scale models and CFD-simulations will be used to study the flow of water for different blockage scenarios and inlet designs. From the results, design values will be obtained, for use with existing design frameworks. This approach allows for rapid implementation of the results in existing guidelines and design manuals.

PhD candidate: Sigurdur Már Valsson

Main supervisor: Gudmund Reidar Eiksund

Geotechnics describes the behavior of soils and rocks from an engineering perspective. Knowledge about the grounds mechanical properties forms the basis for all geotechnical design. Soil conditions in each project are explored with soil investigations, which include borings with geotechnical drill rigs as well as laboratory tests on soil samples.

In 2013 the Geological Survey of Norway launched a national database for soil investigation (known as NADAG), where both the public and private sector could share data from their projects. The initiative was a great success, and in early 2019 the database contained registrations from over 100.000 boreholes in Norway. The Norwegian Public Roads Administration has its own database (GUDB) that is synchronized with NADAG each night. All data from NADAG (including data from GUDB) are freely available for download online.

Machine learning is a scientific field with the aim of giving computers the ability to learn and act based on data, with minimal human intervention. The aim of this project is to utilize machine learning to study a large amount of previous soil investigation data to create models that can be used for automatic interpretation of new investigations, by using both simple (decision trees, k nearest neighbors, etc.) and advanced mathematical models (different types of neural networks).

The Norwegian Public Roads Administration has conducted a pilot project to investigate the feasibility of using machine learning in geotechnics. We found that a simple machine learning model outperformed conventional methods in identifying the presence of quick clay with data from the cone penetration test (a common soil investigation method).

The main goal of this project is to study how machine learning can be used to interpret soil investigation data, as a human would approach the problem. To train models to study the data structure from each test (registered values, changes with depth, sharp transitions, etc.) to assign layers, identify soil types and to assign mechanical properties.

PhD candidate: Shamsuddin Daulat

Main supervisor: Franz Tscheikner-Gratl

Urban infrastructure is ageing and in constant need for maintenance and rehabilitation. Maintenance and rehabilitation are complex and often costly. Researchers and practitioners are constantly trying to find cost-effective solutions to achieve the highest impact on the overall condition of the networks while keeping or improving the current performance and service levels. Rehabilitating multiple co-located infrastructures (e.g., roads, sewer, water, electric cables, etc.) simultaneously has shown promising results. There are potential synergies in integrating the rehabilitation of these infrastructures, which can be exploited. Exploring and evaluating these possibilities and implementing them into a decision framework is the subject of this PhD project.

Objectives:

This PhD project will investigate on how to integrate the rehabilitation of multiple co-located infrastructures by focusing on the following topics:

• Identification of the infrastructures which have the potential to be rehabilitated simultaneously and mapping out possible dependencies, interdependencies and externalities.
• Finding the optimal timeframe for this integrated rehabilitation considering the varying life expectancies of the various infrastructure.
• Costs and benefits of the integrated rehabilitation approach and evaluation of the necessary savings due to possible synergies to make the approach cost effective.
• Evaluation of current performance assessment methods and possible changes on the performance of individual infrastructures when multi-infrastructure rehabilitation is practiced.