Multi-Criteria Decision-Making in Construction Management: A Bibliometric Analysis of the Integration of MCDM, BIM, and Artificial Intelligence (2015–2025)

The construction industry faces growing challenges that require better decision-making frameworks. Combining Multi-Criteria Decision-Making (MCDM) with digital tools like Building Information Modeling (BIM) and Artificial Intelligence (AI) offers a promising way forward. Yet their integration remains fragmented across research efforts, revealing a significant gap in theory and practice. As a result, their potential remains underutilized, limiting the development of flexible, data-driven decision-making tools for complex construction projects. This study conducts a systematic bibliometric analysis to map the development and structure of the MCDM-BIM-AI connection in construction management from 2015 to 2025. Using a PRISMA-compliant protocol, we analyzed 253 peer-reviewed articles from the Scopus database. Our approach combines performance analysis with science mapping through keyword co-occurrence networks and thematic evolution analysis across three time periods: 2015-2018, 2019-2021, 2022-2025, along with a country-level co-authorship network analysis. Our findings identify five main thematic clusters and show a significant increase in publications since 2019, mainly driven by the Asia-Pacific region. The analysis highlights a notable knowledge gap in the deep connection between BIM and AI within MCDM frameworks. The temporal mapping reveals three phases of evolution: pre-integration (2015-2018), Integration (2019-2021), and Innovation (2022-present), with AI and Machine Learning becoming key focus areas in the latest phase. The study suggests promising future directions for combining MCDM, BIM, and AI to promote more sustainable, data-driven, and resilient construction practices. It proposes a conceptual outline for the Intelligent Cognitive Decision Framework (ICDF), where BIM serves as a semantic data backbone, MCDM acts as the evaluation engine, and AI provides cognitive support for flexible, data-driven decision-making in construction 4.0 applications. This intellectual mapping offers researchers data-driven guidance for prioritizing impactful investigations, practitioners with technology investment plans that match research developments, and policymakers with informed priorities for research and development investment in underrepresented regions.