The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South.

Despite advances in 3D clash detection during preconstruction, mechanical, electrical, and plumbing (MEP) installations are still prone to the detection of unforeseen clashes during construction. These issues must be resolved as quickly as possible to prevent significant schedule delays. Through interviews and field observations, this case study investigates the impact of mixed reality (MR) on the inspection and resolution of field-detected MEP issues from product, organization, and process (POP) perspectives. For the product impact, preliminary findings from the field interviews show that MR-based inspection would increase the quality of MEP installation by identifying errors easily and resolving them faster. For the organizational impact, we modeled and compared the current (as-is) and MR-integrated (to-be) MEP field issue resolution workflows using Business Process Model and Notation and determined that MR-based inspections can decrease the coordination overhead between MEP engineers and superintendents by up to 75%. This translates into at least a 50% faster resolution of an MEP issue for the process impact. The paper contributes to the practice of MR-based field inspection by providing a method to quantify potential time savings by integrating MR into the MEP field issue resolution workflow and field interview questions for MEP engineers and superintendents to further examine the use of MR during inspection activities in construction projects. Our observations of MEP superintendents and engineers during field inspection showed that not all building information visualized in MR is useful for their inspection tasks. We developed a classification for building information usefulness to help construction project managers who are deploying MR determine useful information for the task at hand that needs to be integrated into the 3D MR model for MR-based inspections.

The construction industry faces complex decisionmaking challenges that require the integration of extensive domain knowledge and data analysis. Traditional methods are inadequate to effectively address these challenges due to the dynamic nature of construction projects. In the fast-evolving landscape of construction technology, artificial intelligence (AI) plays a crucial role in enhancing decision-making and prediction processes. Therefore, the main purpose of this research is to examine how AI, particularly Large Language Models (LLMs), can be integrated in the analysis and management of emergency scenarios to improve decision-making, prediction, and optimization through knowledge-based systems. This research follows the CommonKADS method for knowledge engineering and embeds sub-symbolic and symbolic AI techniques. By integrating LLMs, this research conceptualizes and operationalizes tacit construction knowledge into structured knowledge systems. This approach has been applied in a case study analysing National Fire Protection Association (NFPA) fire incidents to test possible interactions to extract related data for the knowledge-based systems. The findings inspire future research on the scalability of AI-integrated systems across different segments of the construction industry, their potential in regulatory compliance scenarios, and the development of automated knowledge-based systems in construction industry.