SMUA4300 Advanced Research Methods Emneplan

Much of the work in students´ scientific or practical work both during their studies and later in their professional life will require a good understanding of research methods and analysis tools and techniques. This course will provide the knowledge and skills necessary for planning and conducting engineering research, for processing data and for analysing results, with special focus upon the most common statistical techniques and data-driven computational techniques as some of the most common tools used in these areas.

1) One internal examiner

2) and 3) Two internal examiners

External examiners are used regularly.

No formal requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge:

The student has advanced knowledge of:

• research methods, especially in engineering research
• statistical and analytical techniques, including knowledge of the most common libraries and tools used in statistical analysis and visualisation of the results
• designing experiments, preparing data and interpreting analysis results
• how relevant statistical and computational techniques relate to each other and where they are used

Skills:

The student has:

• required skills in setting up sound experiments, hypotheses and research questions, and in finding and preparing relevant data
• required skills in identifying which statistical and analytical techniques are to be used and how and where they should be used
• hands-on experience with some of the most common computational techniques and libraries as well as related tools for statistical analysis
• hands-on experience with relevant tools for use in analyses

General competence:

The student:

• has broad overview of the computational tools and techniques used in analysis and engineering research, including statistical techniques and techniques related to data science and machine learning
• has an overview of the terminology related to statistical analysis and data science.
• is able to design experiments for successful engineering research, analyses and critical interpretation of results
• can extend his/her knowledge and skills in programming/scripting, analysing, managing and visualizing data

Infrastructure systems are complex systems of links and nodes designed to facilitate human activity such as the movements of people and goods, and water and waste water. Asset management is used to ensure the condition and performance of the infrastructure throughout its lifecycle to ensure the system's efficiency, resilience and sustainability. Technology and smart infrastructure are increasingly utilized within asset management for condition monitoring, performance prediction, and selection of treatment alternatives related to operations, maintenance, and replacement. This course allows students to integrate and apply engineering skills to consider the use of smart infrastructure within infrastructure asset management in a problem-based learning context.

Knowledge

The candidate should have knowledge of:

• asset management and how it is currently applied within infrastructure systems
• impacts of digitization on operations and maintenance of infrastructure systems, including the use of Big Data
• smart infrastructure system components

Skills

The candidate is able to:

• understand a simplified asset management plan, including risk assessment
• describe technological solutions for smart operations/management and maintenance of road and rail infrastructure, and how they can be applied to improve the sustainability and resiliency of infrastructure
• use and manage simple datasets within infrastructure operations/management and maintenance

Competencies

The candidate can:

• consider a holistic view of the life-cycle of infrastructure and the role of asset management within it
• understand the potential and challenges when transitioning to smart operations of management systems
• work within a team to develop and define a project objective and scope, apply critical thinking skills within an open-ended task, manage workload over the course of the project, and present results in a professional environment.

Lectures, readings, discussions, project-based learning

Digital lectures will be recorded, and the material will be made available to students on CANVAS.

The following required coursework must be done before a student can take the exam:

1. Students should divide into groups working on the final project (individual work is possible).

2. Two weeks before submitting the project, Students should present their own idea ready for implementation (description) as part of the final project during subject consultations in the form of a discussion.

The assessment consists of three parts:

1. Project work - written report of up to 40 pages, which counts 40%
2. Project work - oral presentation, which counts 20% (duration 20 minutes)
3. Individual oral exam, which counts 40% (duration 20 minutes)

Project work in parts 1 and 2 is done in groups with a maximum of 3 students. Students may also choose to work individually.

All assessment parts must be awarded a pass grade (E or better) in order for the student to pass the course.

Assessment parts 1) can be appealed, parts 2) and 3) cannot be appealed.

All documents and devices must be approved by the teacher.

Graded scale A-F