EPN-V2

MABY5050 Smart Infrastructure and Asset Management Emneplan

Engelsk emnenavn
Smart Infrastructure and Asset Management
Studieprogram
Master’s Programme in Civil Engineering
Omfang
10.0 stp.
Studieår
2022/2023
Emnehistorikk

Innledning

Ingen forkunnskapskrav

Læringsutbytte

Knowledge

The candidate should have knowledge of:

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

Skills

The candidate is able to:

  • develop a simplified asset management plan
  • 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 transport infrastructure operations/management and maintenance

Competencies

The candidate can:

  • consider a holistic view of the life-cycle of transport 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.

Arbeids- og undervisningsformer

Fagstoffet gjennomgås i plenumsforelesninger. I første del av semesteret får studentene øvingsoppgaver som ikke skal leveres inn. 

Studentene arbeider gjennom semesteret med to prosjektoppgaver som er del av eksamen. Det gis tilbakemelding på disse to oppgaver underveis i semesteret.

Arbeidskrav og obligatoriske aktiviteter

A bachelor’s thesis written on a relevant nursing topic in which theory of science and research methods are key aspects. The exam form is intended to give the students an opportunity to incorporate their experience from clinical training.

Credits based on the national curriculum are:

  • Main topic 1 - 7 credits
  • Main topic 2 - 4 credits
  • Main topic 3 - 1 credit
  • Main topic 4 - 3 credits

Vurdering og eksamen

Vurderingen består av 2 prosjektoppgaver og en individuell skriftlig eksamen på 4 timer.

Prosjektoppgavene skal være på opptil 3 000 ord hver, og kan utføres individuelt eller i grupper på inntil 4 studenter.

Prosjektoppgavene teller 40 % og skoleeksamen 60 %. Totalkarakter settes etter en helhetsvurdering. 

Ved gjentak av eksamen, må man levere nye prosjektoppgaver og gå opp til ny skriftlig skoleeksamen.

Hjelpemidler ved eksamen

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

Knowledge

The student

  • has broad knowledge of the nurse’s role and responsibilities in a chosen area of specialisation
  • has broad knowledge of nursing research and other relevant research and professional development in a chosen area of specialisation

Skills

The student is capable of

  • defining clinical issues of relevance to professional development in nursing
  • carrying out systematic literature searches
  • clarifying concepts, analysing and assessing different sources of information, and using these sources to formulate relevant argumentation
  • carrying out an independent, limited literature study under supervision and in accordance with applicable standards of research ethics
  • presenting specialist literature in an independent, logical and systematic manner

Competence

The student

  • is capable of critically and analytically assessing the chosen topic in light of academic and research-based knowledge
  • is familiar with professional ethical issues and can contribute to planning professional development in clinical practices

Vurderingsuttrykk

Gradert skala A - F

Sensorordning

Det benyttes intern og ekstern sensor til sensurering av besvarelsene. 

Et uttrekk på minst 25% av besvarelsene sensureres av to sensorer. Karakterene på disse samsensurerte besvarelsene skal danne grunnlag for å fastsette nivå på resten av besvarelsene.