Nyqvist, Roope (2025) Data-driven transformation in construction management: From artificial intelligence to network modeling. PhD thesis, Aalto University, Finland.
Abstract
Digital technologies like artificial intelligence (AI) and digital platforms are transforming the construction industry towards data-driven management, offering pathways to address long-standing issues like inefficiency, cost overruns, and project delays while enhancing quality and sustainability. However, realizing this potential can benefit from integrating digital solutions with complementary knowledge-structuring innovations for a holistic approach. This dissertation tackles key knowledge gaps by exploring the integration of data-driven digital innovations and knowledge-structuring innovations within construction. It addresses the research pitfall of isolating technology innovations from business model development and ecosystem evolution, emphasizing the need for an interconnected perspective. Furthermore, the dissertation investigates the under-explored potentiality of practical applications of generative AI across construction management, and particularly focuses on evaluating construction project risk management (CPRM) capabilities of humans and AI, and the need for management innovations complementing digital tools. Employing a mixed-methods approach, this dissertation addresses five key questions: (1) What are the primary barriers, drivers, and their associated actions for construction industry companies to consider in managing digital transformation? (2) What are the implications of databased digital innovations on the companies' business models in the construction industry? (3) What are the potentials of generative AI to enhance construction project management? (4) Taking a particular use case, what are the capabilities of generative AI in CPRM compared to human professionals? (5) How can a network-based approach provide a knowledge-structuring innovation for CPRM, and how can such innovations also support digital data-driven innovations? Key findings reveal: (1) synthesized barriers, drivers, and actionable strategies for navigating digital transformation; (2) how AI-driven platforms can reshape business models and leverage data, despite challenges like operational integration complexities and data security; (3) that generative AI shows substantial potential across seven construction management areas, notably outperforming human experts in CPRM, though practical use still needs human oversight; (4) the uncertainty network modeling (UNM) method introduced complements digital innovation by providing a knowledge-structuring approach to visualizing and managing interconnected risks, enhancing stakeholder collaboration, improving risk management, and providing AI with explicit project data. These findings demonstrate how construction management can be advanced by integrating digital innovations with methods that formalize human expertise. The results establish that the potential of AI is best unlocked through a partnership with human-centered approaches that make tacit knowledge explicit for machine utilization. Specifically, this research synthesizes these findings into an integrated four-layer framework: (1) digital transformation strategies to guide high-level adoption by overcoming key barriers; (2) digital solutions for implementing practical AI tools and platforms; (3) an integration layer where digital tools and knowledge-structuring methods are combined to foster human-machine collaboration; and (4) knowledge-structuring solutions, like the UNM method, which provide the structured, human-validated data essential for this synergy. This framework provides a transferable model for managing the synergy between technological innovation and human expertise in construction.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Seppänen, Olli |
| Index terms: | artificial intelligence, synergy, construction industry, dissertation, business model, technological innovation, construction project management, holistic approach, risk management, management innovation, technology innovation, transformation, partnership, cost overrun, platform, complexity, project delay, tacit knowledge, digital technology, network modelling, integration, evolution, collaboration, construction project, project data, strategy |
| Subjects: | production management, automation and robotics, digital design, networking, research dissemination and communication, environmental science, innovation and technology management, management, project management theory and practice, professional development, project controls, partnership management, organizational analysis, industry analysis, systems engineering, risk assessment, data collection methods, business, computing systems, artificial intelligence, financial and cost management |
| Topics: | Digital Applications, Time Control, Organizational Design, Cost Management, Business Strategy, Information Management, Research Practice, Stakeholder Management, Engineering Principles, Project Management, Risk Management, Sustainability |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here