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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Exploring the Intersection of Building Information Modeling (BIM) and Artificial Intelligence in Modern Infrastructure Projects

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  • Exploring the Intersection of Building Information Modeling (BIM) and Artificial Intelligence in Modern Infrastructure Projects

Rasheed O. Ajirotutu 1, *, Abiodun Benedict Adeyemi 2, Gil-Ozoudeh Ifechukwu 3, Tochi Chimaobi Ohakawa 4, Obinna Iwuanyanwu 5, and Baalah Matthew Patrick Garba 6

1 Independent Researcher, New York, USA.
2 Coupons Retail Ltd, Lagos. Nigeria.
3 Department of Architecture.
Enugu State University of Science and Technology.
4 Independent Researcher, Washington DC, USA.
5 Independent Researcher, Delta State, Nigeria.
6 Cypress & Myrtles Real Estate Limited, Abuja, Nigeria.

Review Article
 

International Journal of Science and Research Archive, 2024, 13(02), 2414–2427.
Article DOI: 10.30574/ijsra.2024.13.2.2421
DOI url: https://doi.org/10.30574/ijsra.2024.13.2.2421

Received on 28 October 2024; revised on 04 December 2024; accepted on 07 December 2024

The integration of Building Information Modeling (BIM) and Artificial Intelligence (AI) is revolutionizing infrastructure development, offering innovative solutions for design, construction, and operational challenges. This study explores the synergy between BIM and AI, examining their conceptual framework, impact on sustainability and decision-making, and the challenges associated with their integration. Through a comprehensive review of recent literature and industry practices, the study elucidates the transformative potential of these technologies in modern infrastructure projects.
The findings reveal that BIM and AI enhance project efficiency, resource management, and stakeholder collaboration by leveraging AI’s predictive analytics and automation alongside BIM’s digital modeling capabilities. These technologies enable precise resource allocation, dynamic risk mitigation, and sustainable energy solutions, aligning infrastructure projects with global environmental goals. However, challenges such as data interoperability, high implementation costs, and ethical concerns remain significant barriers to their widespread adoption. The study underscores the need for industry-wide data standards, advanced cybersecurity frameworks, and targeted workforce upskilling to address these challenges.
Future trends identified include the integration of generative AI, blockchain technology, and the Internet of Things (IoT) into BIM systems, promising to further enhance the scope and functionality of these technologies. These advancements hold the potential to redefine infrastructure practices, driving innovation and sustainability.
In conclusion, BIM and AI represent a paradigm shift in infrastructure development, fostering efficiency, sustainability, and resilience. It is recommended that stakeholders invest in research, adopt ethical AI practices, and build collaborative ecosystems to fully realize the benefits of these technologies. By addressing identified challenges and embracing emerging trends, BIM and AI can become foundational tools in achieving sustainable and technologically advanced infrastructure solutions.

Building Information Modeling (BIM); Artificial Intelligence (AI); Sustainability; Infrastructure Development; Predictive Analytics; Smart Infrastructure

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2024-2421.pdf

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Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Gil-Ozoudeh Ifechukwu, Tochi Chimaobi Ohakawa, Obinna Iwuanyanwu and Baalah Matthew Patrick Garba. Exploring the Intersection of Building Information Modeling (BIM) and Artificial Intelligence in Modern Infrastructure Projects. International Journal of Science and Research Archive, 2024, 13(02), 2414–2427. https://doi.org/10.30574/ijsra.2024.13.2.2421

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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