Public defence in civil engineering, MSc Roope Nyqvist

Title of the thesis: "Data-driven transformation in construction management  - From artificial intelligence to network modeling”

Thesis defender: Roope Nyqvist
Opponent: Prof. Daniel Hall, Delft University of Technology, Holland
Custos: Prof. Antti Peltokorpi, Aalto University School of Engineering

Inefficiency, delays, and cost overruns are common problems in the construction industry, making a digital transformation necessary. This dissertation explores how data-driven innovations, from artificial intelligence (AI) to network modeling, could address these problems.

The dissertation identifies the barriers to and drivers of digital transformation. It also explores the shift toward AI-driven platform business models. It examines the integration of generative AI in management actions and introduces a new method called uncertainty network modeling (UNM). UNM is a method for structuring human knowledge, also necessary for guiding digital tools.

In a blind test, generative AI outperformed humans in risk management. However, while the AI's analysis was comprehensive, it was also generic. Some human experts, on the other hand, offered more practical, context-specific insights. These results highlight the current limitations of AI and the need for deep knowledge, which the UNM method helps to reveal.

The findings offer construction industry companies research results for achieving digital transformation, including the use of technology and the development of new AI-driven services and platform business models. The results aid in the integration of AI into project management and the structuring of knowledge, thereby ensuring that critical decisions are guided by expertise.

In conclusion, a successful data-driven transformation requires an integrated, multi-layer framework. The dissertation proposes a framework that links digital transformation strategy to the practical implementation of digital solutions and knowledge structuring. The framework’s foundation is providing digital solutions domain expertise. This happens through knowledge structuring layer, where methods like UNM make expert knowledge explicit and machine-readable. This structured data is fed via an integration layer into digital tools to create a human-machine synthesis, forming the foundation for the successful data-driven transformation.

Key words: Artificial intelligence, digital transformation, business model innovation, platform economics, construction management, risk management, network-based methods

Thesis available for public display 7 days prior to the defence at Aaltodoc. 

Contact information: roope.nyqvist@aalto.fi