MABY5100 Building Information Modelling - BIM Emneplan

To interne sensorar. Ein tilsynssensor er tilknytt emnet etter retningslinjene for oppnemning og bruk av sensorar ved OsloMet.

I emne 3 byggjer arbeidet i norskfaget vidare på kunnskapane frå emne 1 og 2. I emne 3 arbeider studentane særleg med begynnaropplæringa og praktisk-estetiske arbeidsformer. Undervisninga knyter linjer tilbake til og utdjupar det grunnleggjande språkperspektivet i emne 1, og byggjer vidare på og spissar tekstperspektivet i emne 2.

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 the use of BIM-based solutions (software and processes) for digital planning, throughout the building's life cycle

· knowledge of the benefits of taking an innovative, interdisciplinary approach to solving work assignments

· in-depth knowledge of the latest developments in digital design and Construction.

Skills:

The student is capable of:

• using software that supports the development and implementation of building solutions
• using methods and standards for interdisciplinary exchange of information between the parties involved in the design of a building and the construction process
• assessing how the digital interaction between design, construction and the production process can be improved.

General competence:

The student is capable of:

· using digital BIM-based solutions in the development and production of innovative solutions in the field

· using scholarly publications as support in problem-solving

· in cooperation with others, contributing to academic research in information systems for building technology and structural engineering.

The teaching will consist of participation in lectures, discussions and independent project assignment work. Exercises and individual knowledge tests (multiple choice) will be added continuously during the first part of the teaching period. Practical use of BIM-based software for the collection of information, calculations and assessments and presentation of solutions is taught by means of brief courses, self-study and exercises.

The students will work in groups of 3-5 on developing solutions and proposing measures for implementation. The work shall be presented as an academic project report. Emphasis is placed on identifying innovative ways of working and cooperating.

None.

Portfolio assessment subject to the following requirements:

1. Individual Revit project (deliver file with BIM model), weighted approx. 30 %

2. 3-5 individual knowledge tests, the average score of the tests combined is weighted a total of approx. 30 %

3. Academic report prepared in groups of 3-5 students, approx. 10-15 pages, weighted approx. 40 %.

Each student's work will be assessed together as a portfolio with one individual grade at the end of the semester, but the three parts that make up the portfolio must be assessed as 'pass' in order for the student to pass the course. The weights are approximate and included here to provide additional information to the students on how their work is assessed. The size of the group will be taken into account when assessing the group report. Students who are absent from individual knowledge tests may be given up to one opportunity to take a new knowledge test. Alternatively, the student may submit one written report if he or she is prevented from attending the knowledge test. Detailed guidelines for the project assignment will be published in Canvas.

The overall assessment can be appealed. If a student fails the portfolio assessment, he/she is given one opportunity to resubmit the portfolio.

This course will provide an advanced understanding of bioprocesses by which water pollutants can be removed. The course will additionally convey an overview and a deeper understanding of urban water resource recovery in the context of circular economy. It will provide comprehensive knowledge about the behavior of contaminants and the processes for their conversion/removal in engineered water systems. The main focus will be on systems analysis and process engineering, as well as on classification and risk assessment of pollutants and water-borne resources.

The students will make use of software such as Matlab, Python, West, Sumo or similar tools.

A grade scale with grades from A to E for pass (A is the highest grade and E is the lowest) and F for fail is used in connection with the final assessment.

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

• good understanding of the water pollutant and resource classifications in water resource systems;
• advanced knowledge of biological, chemical and physical-chemical reactor operations to remove water pollutants;
• advanced comprehension of bioprocess reactor operations in water resource recovery facilities;
• advanced knowledge of design, optimization and control of water process systems;
• good understanding of data analysis in water process systems.

Skills:

The student

• can use pollutants classifications, describe their impact and fate in the water environment;
• can conceptualize complex bioprocesses to separate and recover urban water resources;
• is capable to apply systems analysis methods to water resource recovery processes;
• is capable to apply process knowledge to build advanced computer simulation models to critically evaluate and select from alternative technologies;
• has hands-on computational experience to deal with novel scenarios, solve problems and make engineering decisions in the face of incomplete or uncertain information;
• has hands-on expertise to appraise solutions for eliminating water environmental problems.

General competence: 

The student

• has deep insight into smart water process engineering with links to global sustainable development;
• is able to infer mathematical description of advanced unit operations and to create advanced computer simulation models of whole smart water resource engineered systems;
• Is able to solve advanced smart water process design and optimization problems using information processing tools.