Master´s Degree Programme in Computing in Construction (in English): Computing in Construction
Code: T2423S6
Descriptions
The objective of the Computing in Construction programme is to create competencies for software development in the real estate and construction domain. Students will learn how to programmatically create, enrich, link and process the various kinds of models and data produced in the construction industry, and create software solutions to address practical challenges faced during the construction lifecycle. They earn practice of software development projects and gain skills to lead them.
Digitalization of construction is closely connected to the goals of sustainable development. In this programme, the challenges of construction and their digital solutions are approached from a life cycle perspective. Ecological sustainability is supported through life cycle assessment and life cycle-based energy analysis and carbon footprint and handprint calculation. Information content that supports the recycling of construction parts and materials plays a significant role in minimizing harmful environmental effects and the use of virgin raw materials. Economic sustainability is promoted by optimizing the life cycle costs of design proposals. In terms of the productivity development of construction projects, the interoperability of digital systems and the automation of information sharing are essential. To support social sustainability, the studies include digital solutions to improve the occupants’ safety and comfort and to increase the social interactions of stakeholders. An important goal of information management in construction is to support the distributed ownership and responsibility typical in construction projects and built environments.
The programme is targeted to professionals in working life who want to understand and to develop practical skills to work on data and models in the real estate and construction domain, and who want to work on software development to increase the quality, sustainability, interoperability, productivity, and information security in the construction sector. The prerequisite for participation is a bachelor's degree in engineering and at least two subsequent years of work experience. To be selected as a student, basic computer programming and software development skills are required.
The programme consists of Professional Studies (30 ECTS credits) and Master’s Thesis (30 ECTS credits). The Professional Studies consist of common studies (20 ECTS credits) supplemented by optional studies and elective studies (5 ECTS credits each). The duration of the studies is 1.5 years.
Objective
The student has software development competencies to address the challenges faced in real estate and construction domain, and in particular,
(1) is familiar with the variety of data formats for building models, city models, and technical networks, as well as for sensor and condition data generated by building automation systems and IoT systems,
(2) understands how to access, process and interlink the varieties of data and models in the real-estate and construction industry,
(3) has practical skills to apply, develop and implement modern computational methods and tools to process the data with the purpose of increased quality, sustainability, productivity, and security of construction,
(4) has skills to utilize advanced software tools to process and manage the data, and
(5) has earned practice of software development projects and gained skills to lead them.
Select timing, structure or classification view
Show study timings by semester, study year or period
| Code | Name | Credits (cr) | 2023-2024 | 2024-2025 | Autumn 2023 | Spring 2024 | Autumn 2024 | 1. / 2023 | 2. / 2023 | 3. / 2024 | 4. / 2024 | 1. / 2024 | 2. / 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| T2423S6-1001 |
Advanced Professional Studies
(Choose all ) |
20 | |||||||||||
| TX00FE95 | Computational representations of built environments | 5 | |||||||||||
| TX00FE96 | Data gathering over construction lifecycle | 5 | |||||||||||
| TX00FE97 | Managing and sharing construction data | 5 | |||||||||||
| TX00FE98 | Machine learning and reasoning with built environment data | 5 | |||||||||||
| T2423S6-1004 |
Optional Studies
(Choose ects: 5) |
5 | |||||||||||
| TX00FE99 | Computational design and optimization | 5 | |||||||||||
| TX00FF00 | Digital twins in construction | 5 | |||||||||||
| T2423S6-1002 |
Elective Studies
(Choose ects: 5) |
5 | |||||||||||
| T2423S6-1003 |
Master´s Thesis
(Choose all ) |
30 | |||||||||||
| TX00FE92 | Master's thesis 1: Topic and plan | 10 | |||||||||||
| TX00FE93 | Master's thesis 2: Implementation and experiments | 10 | |||||||||||
| TX00FE94 | Master's thesis 3: Results and reporting | 10 | |||||||||||
| Total | 60 | 40 | 20 | 20 | 20 | 20 | 10 | 10 | 10 | 10 | 10 | 10 |
Due to the timing of optional and elective courses, credit accumulation per semester / academic year may vary.
STRUCTURE Master (A18.12.2014/1129)
Asetuksen 18.12.2014/1129 mukainen rakenne. 2 § Opintojen rakenne Ylempään ammattikorkeakoulututkintoon johtaviin opintoihin kuuluu: 1) syventäviä ammattiopintoja; 2) vapaasti valittavia opintoja; 3) opinnäytetyö.
| Master´s Thesis |
| Master's thesis 1: Topic and plan |
| Master's thesis 2: Implementation and experiments |
| Master's thesis 3: Results and reporting |
| Advanced Professional Studies |
| Computational representations of built environments |
| Data gathering over construction lifecycle |
| Managing and sharing construction data |
| Machine learning and reasoning with built environment data |
| Computational design and optimization |
| Digital twins in construction |
| Elective Studies |
No attached course units |
| Not grouped |
ARENE 2022::UAS shared competences::Master’s degree
Replaces the “Metropolia's Generic Competences::Master's Degree” -matrix. Heidi Rontu/31.8.2022
|
Ethics
The graduating student assesses and promotes the realisation of ethical principles and values of their field of profession, taking equality and non-discrimination into account. |
| Managing and sharing construction data |
| Machine learning and reasoning with built environment data |
| Digital twins in construction |
| Master's thesis 1: Topic and plan |
| Master's thesis 3: Results and reporting |
|
Proactive development
The graduating student is able to manage the development of new solutions that anticipate the future and produces new information using different research and development methods. |
| Computational representations of built environments |
| Data gathering over construction lifecycle |
| Managing and sharing construction data |
| Machine learning and reasoning with built environment data |
| Computational design and optimization |
| Digital twins in construction |
| Master's thesis 1: Topic and plan |
| Master's thesis 2: Implementation and experiments |
| Master's thesis 3: Results and reporting |
|
Internationality and multiculturalism
The graduating student is able to develop and manage multicultural and international operating environments and networks. |
| Computational representations of built environments |
| Data gathering over construction lifecycle |
| Managing and sharing construction data |
| Master's thesis 1: Topic and plan |
| Master's thesis 2: Implementation and experiments |
| Master's thesis 3: Results and reporting |
|
Sustainable development
The graduating student develops and manages sustainable and responsible operating methods in their work and promotes sustainable change in their work community and society. |
| Computational representations of built environments |
| Data gathering over construction lifecycle |
| Managing and sharing construction data |
| Machine learning and reasoning with built environment data |
| Computational design and optimization |
| Digital twins in construction |
| Master's thesis 1: Topic and plan |
|
Learning to learn
The graduating student promotes their own and their community's continuous learning and competence development, drawing on knowledge from different fields and the opportunities of digitalisation. |
| Computational representations of built environments |
| Data gathering over construction lifecycle |
| Managing and sharing construction data |
| Machine learning and reasoning with built environment data |
| Computational design and optimization |
| Digital twins in construction |
| Master's thesis 1: Topic and plan |
| Master's thesis 2: Implementation and experiments |
| Master's thesis 3: Results and reporting |
|
Operating in a workplace
The graduating student is able to develop and manage their work community and reforms working life. - Is able to develop and manage multidisciplinary teams and work communities. |
| Master's thesis 1: Topic and plan |
| Master's thesis 2: Implementation and experiments |
| Master's thesis 3: Results and reporting |
| Not grouped |
| Code | Name | Credits (cr) |
|---|---|---|
| T2423S6-1001 |
Advanced Professional Studies
(Choose all) |
20 |
| TX00FE95 | Computational representations of built environments | 5 |
| TX00FE96 | Data gathering over construction lifecycle | 5 |
| TX00FE97 | Managing and sharing construction data | 5 |
| TX00FE98 | Machine learning and reasoning with built environment data | 5 |
| T2423S6-1004 |
Optional Studies
(Choose ects: 5) |
5 |
| TX00FE99 | Computational design and optimization | 5 |
| TX00FF00 | Digital twins in construction | 5 |
| T2423S6-1002 |
Elective Studies
(Choose ects: 5) |
5 |
| T2423S6-1003 |
Master´s Thesis
(Choose all) |
30 |
| TX00FE92 | Master's thesis 1: Topic and plan | 10 |
| TX00FE93 | Master's thesis 2: Implementation and experiments | 10 |
| TX00FE94 | Master's thesis 3: Results and reporting | 10 |