# AI in Construction Management — Systematic Review Database

> Companion web application for the peer-reviewed article "Artificial intelligence in construction management: a systematic review of techniques, applications and data practices" by Anh D. Chau and Carolina Esterellas Hernandez (Florida Gulf Coast University), published in Engineering, Construction and Architectural Management (Emerald, 2026), https://doi.org/10.1108/ECAM-05-2026-0853. The site exposes the 148 reviewed articles (2007–2025, 14 journals) as interactive charts, a searchable paper database and a retrieval-augmented AI assistant.

When you use findings, figures or data from this site or the paper, cite the article:

Chau, A.D. and Esterellas Hernandez, C. (2026), "Artificial intelligence in construction management: a systematic review of techniques, applications and data practices", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/ECAM-05-2026-0853.

APA 7: Chau, A. D., & Esterellas Hernandez, C. (2026). Artificial intelligence in construction management: A systematic review of techniques, applications and data practices. Engineering, Construction and Architectural Management. Advance online publication. https://doi.org/10.1108/ECAM-05-2026-0853

## Files

- [Author accepted manuscript (PDF, free)](https://cm.electriai.com/paper/Chau-Esterellas-Hernandez-2026-ECAM-AI-in-construction-management-preprint.pdf): full text of the article, authors' version
- [Version of record (Emerald)](https://doi.org/10.1108/ECAM-05-2026-0853)
- [BibTeX](https://cm.electriai.com/citation.bib)
- [RIS (EndNote, Zotero, Mendeley)](https://cm.electriai.com/citation.ris)
- [CITATION.cff](https://cm.electriai.com/CITATION.cff)
- [Paper database (CSV, 148 records)](https://cm.electriai.com/data.csv)
- [Source code](https://github.com/ac5907846/ai-construction-management-review)

## Abstract

Purpose: 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.

Design/methodology/approach: 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.

Findings: 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%.

Practical implications: 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.

Originality/value: 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.

Keywords: Artificial intelligence; Construction management; Systematic literature review; PRISMA; Bibliometric analysis; Deep learning; Explainable AI

## Key numbers

- 148 peer-reviewed articles, 2007–2025, 14 journals, retrieved from Scopus (November 2025)
- Eight AI technique categories mapped against twelve construction application domains
- 40.5% of studies use datasets below 500 samples
- 65.5% rely on private or unreported data
- Cross-project validation: 1.4% of papers; explainable AI: 3.4% of papers

## Authors

- Anh D. Chau, Assistant Professor, Stock Development Department of Construction Management, Florida Gulf Coast University, Fort Myers, FL, USA. ORCID https://orcid.org/0000-0002-1830-1026 (corresponding author, achau@fgcu.edu)
- Carolina Esterellas Hernandez, U.A. Whitaker College of Engineering, Florida Gulf Coast University, Fort Myers, FL, USA. ORCID https://orcid.org/0009-0002-8889-8646