Data-driven transformation in construction management – From artificial intelligence to network modeling

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.

Department dc:contributor.department

Rakennustekniikan laitos

Grantor dc:publisher

Aalto University

Year dc:date.issued

2025

Author dc:creator

• Nyqvist, Roope

Advisors dc:contributor.advisor

• Seppänen, Olli, Prof., Aalto University, Department of Civil Engineering, Finland
• Peltokorpi, Antti, Prof., Aalto University, Department of Civil Engineering, Finland

Contributors dc:contributor

• Aalto-yliopisto
• Aalto University

Language dc:language.iso

en

Repository record dc:identifier.uri

https://aaltodoc.aalto.fi/handle/123456789/140571

OAI record oai:identifier

https://aaltodoc.aalto.fi/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aaaltodoc.aalto.fi%3A123456789%2F140571