Artificial Intelligence in ConstructionLaajuus (5 ECTS)

Objective

The student can identify and explain the basic concepts of neural and symbolic artificial intelligence, and the role of learning, prediction, reasoning, and autonomy in intelligent behaviour. The student understands the principles of machine learning, symbolic reasoning, and autonomous agents, and the potential use cases of large language models, foundation models, knowledge graphs, and robots in the construction domain. The student understands the possibilities and challenges of spatial artificial intelligence. The student knows typical use cases of artificial intelligence in construction, and can evaluate the risks of of applying artificial intelligence technologies to solve them. The student has skills to programmatically utilise artificial intelligence technologies in the context of the construction domain, and utilise it in computational design.

Content

• Introduction to artificial intelligence and agent-based systems
• Basics on neural artificial intelligence: neural networks, bayesian inference, machine learning, deep learning, large language models, and foundation models
• Basics of symbolic artificial intelligence: logical models, ontologies, reasoning, knowledge graphs, and rule-based systems
• Spatial artificial intelligence: object recognition and model construction
• Application of artificial intelligence technologies in the construction domain
• Efficient use of artificial intelligence tools such as language models
• Programming exercises on artificial intelligence technologies
• Exercises in integration of artificial intelligence technologies with computational design

Qualifications

TX00FE95 Computational Representations of Built Environment
TX00FE96 Data Gathering over Construction Lifecycle
TX00FE99 Computational Design and Optimisation

Assessment criteria, satisfactory (1)

The student has achieved the minimal objectives of the course. The student can identify and explain the basic concepts and definitions of artificial intelligence and approaches related to machine learning, reasoning, and rule-based systems. The student can explain the potential use scenarios in the construction domain. The student knows and is familiar with some available tools in the area and can use them. The student has completed the required learning exercises and online courses at the minimum requirement level. The competencies acquired form the basis for the student to improve knowledge in artificial intelligence in construction domain, eventually enabling to achieve a related job position.

Assessment criteria, good (3)

The student has achieved the objectives of the course well, even though the knowledge and skills need improvement on some areas. In addition to the competences of the satisfactory level, the student is well-prepared to utilize existing artificial intelligence tools and understands the underlying technologies and approaches. The student has completed the required learning exercises and online courses at good or satisfactory level. The student is able to create software solutions incorporating machine learning and reasoning functionalities. The student has the capability to apply the knowledge in further studies and in software development work related to artificial intelligence in construction.

Assessment criteria, excellent (5)

The student has achieved the objectives of the course with excellence. In addition to the previous levels, the student can explain and elaborate on the important concepts, approaches and tools in artificial intelligence. The student knows and can apply and combine multiple software tools in the area. The student has completed the required learning exercises and online courses at excellent or good level. The student is able to integrate well-placed artificial intelligence functionalities in software solutions in a justified manner. The student has an excellent basis to apply the knowledge in further studies and in software development jobs related to artificial intelligence in construction.