Intell. Infrastruct. Constr., Volume 2, Issue 1 (March 2026) – 3 articles

Disruptive global events such as the COVID-19 pandemic have exposed critical vulnerabilities in the construction industry’s reliance on lean principles and Just-In-Time (JIT) methodologies. These disruptions, categorized as Black Swan Events (BSEs), challenged conventional supply chain management (SCM) and risk management (RM) strategies, resulting in delayed projects and increased costs. This paper explores how BSEs affect construction supply chains and evaluates the industry’s evolving response through RM and resilience-building strategies. A Joanna Briggs Institute (JBI) scoping review of the literature (2000–2024) synthesized evidence across SCM, RM, Lean Construction, JIT, and BSEs, triangulating 86 peer-reviewed studies with authoritative industry reports. The review reveals a lack of integrated research addressing these themes holistically for the construction sector. Key findings show that while JIT and lean approaches optimize efficiency, they fall short during high-impact, low-probability disruptions. Evidence indicates a selective shift toward Just-In-Case (JIC) practices; however, the extent and persistence of this transition vary by project context and merit further study. The study proposes a future research agenda emphasizing interdisciplinary models that integrate lean methods with resilience and anticipatory strategies. These insights aim to support construction firms in developing supply chains that are not only efficient but also adaptable and better prepared for future BSEs. Full article

The EN-HERITAGE project aims to define and prototype an integrated digital platform for the management of virtual models of buildings belonging to the historic built heritage, with a particular focus on slate roofing systems. The platform integrates IoT technologies for environmental monitoring, architectural surveys carried out using laser scanning and photogrammetry, HBIM models, and artificial intelligence algorithms for the analysis of degradation phenomena. The pilot application was conducted on the Albergo dei Poveri complex in Genoa, providing a replicable methodology for the planned conservation of the historic built environment. Preliminary results highlight the effectiveness of the platform in integrating heterogeneous data, providing stakeholders involved in the management of extensive architectural heritage with concrete support for decision-making processes and greater efficiency in planning maintenance and restoration interventions on historic buildings. Full article

This study aims to explore the transformative potential of Artificial Intelligence (AI) in enhancing cost planning and control within Qatar’s construction industry. By examining both opportunities and challenges associated with the adoption of AI, it seeks to uncover that AI can lead to significant improvements in accuracy in cost estimates and optimisation of various resources. The nation faces significant cost-overruns influenced by delays, shifting market conditions, and although AI has demonstrated its benefits in cost-control management globally, there is a lack of research on its practical applications in Qatar’s construction industry. Existing practical applications are more likely to experience errors due to them requiring manual labour and limited pattern recognition. Meanwhile, this study attempts to align AI-driven advancements with Qatar’s Vision 2030, which emphasises sustainable development and economic diversification. It adopts an analysis of semi-structured interviews with a group of experienced professionals from leading construction companies in Qatar, giving a comprehensive picture of the current landscape and future prospect for AI in the construction industry. The findings of this study reveal that AI technologies can significantly mitigate common issues in the construction industry, such as cost overruns, project delays, and resource wastage. On the other hand, this study identifies various obstacles that inhibit AI adoption, including high financial costs and insufficient training data. By weaving together theoretical understandings and practical experiences, it highlights the importance of integrating AI technologies within existing workflows while addressing key concerns. Full article