This study presents an integrated systematic mapping of artificial intelligence (AI) applications in construction management, clarifying technique-domain pairings, data and validation practices, and deployment readiness across the construction lifecycle, addressing fragmentation in prior reviews.
Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidance for study identification and screening and the structured literature review approach, 148 peer-reviewed articles published between 2007 and 2025 across 14 journals were analyzed through bibliometric and thematic synthesis. An open companion web application (https://cm.electriai.com/) was developed, providing bibliometric visualizations, a searchable paper database, and a retrieval-augmented generation (RAG) AI assistant.
The review shows that AI research in construction management accelerated after 2022 but remains uneven in its technical and methodological development. Technique-domain mapping revealed lock-in patterns: vision-based deep learning (DL) was concentrated in safety, and traditional machine learning (ML) in cost estimation, while optimization, graph neural networks (GNNs), and generative AI remained underexplored. Data constraints persist: 40.5% of studies used datasets below 500 samples, 65.5% relied on private or unreported data, cross-project validation appeared in 1.4% of papers, and explainable AI (XAI) appeared in 3.4%.
Construction stakeholders require coordinated investment in open benchmark datasets, cross-project validation protocols, XAI integration and interoperable standards for building information modeling and Internet of Things systems.
This review integrates AI techniques, construction domains, data and validation practices, supported by an open companion web application for verifying, filtering, and querying the evidence base.
