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    Op-Ed
    Projects

    UCL’s Megaproject Delivery Centre: delivery lessons and risk insights for engineers

    January 26, 2026|

    Reviewed by Joe Ashwell

    UCL’s Megaproject Delivery Centre: delivery lessons and risk insights for engineers

    First reported on New Civil Engineer

    30 Second Briefing

    UCL’s Megaproject Delivery Centre founding director Juliano Denicol sets out a research-led approach to improving delivery of multi‑billion‑pound schemes such as HS2 and Crossrail, focusing on governance, contracting models and systems integration. He stresses early definition of scope and interfaces, disciplined change control and portfolio‑level learning from more than 30 international case studies of rail, energy and tunnelling projects. Denicol also calls for clients to build in‑house capability in data analytics and assurance, using consistent performance baselines rather than ad‑hoc KPIs to manage cost and schedule risk.

    Technical Brief

    • For similar complex rail and tunnelling portfolios, such structured cross‑project learning could standardise assurance expectations.

    Our Take

    Within the 533 Infrastructure stories in our database, only a small subset focus on institutional capability-building rather than individual schemes, so UCL’s Megaproject Delivery Centre stands out as an attempt to systematise lessons across projects rather than treating each as a one-off.

    Among the 1,415 Op-Ed/Projects-tagged pieces, most commentary is written by consultants or delivery bodies, so having an academic centre like UCL’s Megaproject Delivery Centre in this space signals that clients and contractors are increasingly looking to university-backed evidence when justifying changes to governance and delivery models.

    For practitioners, engagement with UCL’s Megaproject Delivery Centre suggests a route to benchmark project performance and governance structures against a wider international dataset, which can be useful when arguing for non-standard procurement, alliancing, or staged decision-gates on large infrastructure schemes.

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    Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.

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