While significant importance is given to establishing formal organizational and contractual hierarchies, existing project management techniques neglect the management of self-organizing networks in large-infrastructure projects. We offer a case-specific illustration of self-organization using network theory as an investigative lens. The findings have shown that these networks exhibit a high degree of sparseness, short path lengths, and clustering in dense “functional” communities around highly connected actors, thus demonstrating the small-world topology observed in diverse real-world self-organized networks. The study underlines the need for these non-contractual functions and roles to be identified and sponsored, allowing the self-organizing network the space and capacity to evolve.

The AEC industry is a highly intricate ecosystem involving architects, structural engineers, civil consultants, and contractors working under intense time pressures, where the success of projects hinges on sound decision-making. One of the most persistent challenges is regulatory compliance—not merely understanding the rules, but accurately interpreting and applying them within real-world constraints. This research paper aims to enhance regulatory decision-making through AI, specifically a Q&A model, and stands out in five key ways: it tackles the widespread issue of building code violations that cause costly delays; it empowers professionals to verify compliance during the design phase, minimizing errors and saving resources; it proves that effective AI doesn’t require massive datasets, instead leveraging domain expertise and smart data strategies; and it introduces a scalable framework that can extend to broader regulatory domains and integrate with BIM tools for automated checks, offering a transformative approach to compliance in the AEC sector.

Megaprojects are crucial for developing urban systems, yet their governance is often characterised by disputed authority, shifting coalitions, and uneven stakeholder influence. This paper examines the evolution of power structures in megaproject governance through an integrated methodological approach, that combines temporal social network analysis with the PowerCube framework. Using the Bank Station Capacity Upgrade project in London as a case study, the research traces how visible, hidden, and invisible forms of power interacted across two stages of the project, identifying turning points where stakeholder centrality and influence shifted. This study contributes new knowledge to urban management and governance in three ways. First, it demonstrates that power in large infrastructure projects is not fixed but changes over time as new actors join, coalitions change, and governance spaces are reconfigured. Second, it introduces a novel way of studying these changes by combining social network analysis, which maps relationships between actors, with the PowerCube framework, which highlights visible, hidden, and invisible forms of power. Third, the paper provides original evidence from the Bank Station Capacity Upgrade project, demonstrating how governance mechanisms shaped decision-making and stakeholder legitimacy in one of London’s most complex transport systems. By connecting megaproject governance to urban management concerns, the paper offers both scholarly insights and practical recommendations for policymakers and practitioners seeking to enhance transparency, adaptability, and stakeholder engagement in complex infrastructure delivery.