Generative Artificial Intelligence for Smart Civil Engineering: A Comprehensive Review of Applications, Challenges, and Future Trends

https://doi.org/10.46610/JoST.2026.v011i02.005

Authors

  • Mahadeva M.
  • Abhijith C C
  • Sriram A V
  • Chandana C

DOI:

https://doi.org/10.46610/JoST.2026.v011i02.005

Keywords:

Building information modeling (BIM), Civil engineering, Construction management,, Deep learning, Digital twins, Generative artificial intelligence (GenAI), Large language models (LLMs)

Abstract

Generative Artificial Intelligence (GenAI) is emerging as a transformative technology in civil engineering, offering advanced computational capabilities that enhance engineering design, project planning, infrastructure management, and decision-making. Unlike traditional artificial intelligence, GenAI can generate text, technical documentation, designs, and analytical outputs, thereby improving productivity and reducing the time required for complex engineering tasks. This review provides a comprehensive overview of recent advancements in GenAI and its applications across key civil engineering domains, including structural engineering, construction management, Building Information Modeling (BIM), transportation engineering, geotechnical engineering, and infrastructure monitoring. The study synthesizes findings from recent literature to examine the benefits, challenges, research gaps, and future trends associated with GenAI adoption. The review highlights the significant role of GenAI in design optimization, project scheduling, predictive maintenance, risk assessment, technical documentation, and smart infrastructure development. It also discusses critical challenges such as data quality, model reliability, explainability, cybersecurity, ethical concerns, and professional accountability. Overall, the review demonstrates that GenAI has substantial potential to support intelligent, sustainable, and data-driven civil engineering practices while emphasizing the need for human expertise and responsible AI implementation.

Published

2026-08-05

Issue

Section

Articles