Artificial Intelligence in Civil Engineering a Comprehensive Review of Applications, Challenges, and Future Directions
4 affiliations
- 1Construction Engineering Department, Faulty of Engineering, Egyptian Russian University Badr City, Cairo 11829, Egypt
- 2Artificial Intelligence Department, Faculty of Artificial Intelligence, Egyptian Russian University Badr City, Cairo 11829, Egypt.
- 3Construction Engineering Department, Faculty of Engineering, Egyptian Russian University / President of the Egyptian Russian University, Badr City, Cairo-Suez Road, 11829, Cairo, Egypt.
- 4Artificial Intelligence Department, Faculty of Artificial Intelligence, Egyptian Russian University Badr City, Cairo 11829, Egypt. /Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
Abstract
The civil engineering industry, long associated with complexity, high costs, and safety risks, is being transformed by Artificial Intelligence (AI). This review presents a comprehensive state-of-the-art analysis of AI applications across structural, geotechnical, transportation, construction, and water resources engineering. Core technologies—machine learning, deep learning, reinforcement learning, computer vision, and generative AI—are examined in relation to design optimization, structural health monitoring, soil classification, traffic flow prediction, project management, and climate resilience. Findings from over 80 studies reveal that AI consistently outperforms traditional methods, improving prediction accuracy by 25–40% and reducing computational effort by several orders of magnitude. Quantitative case studies demonstrate its impact, from CNNs achieving over 98% accuracy in crack detection to LSTM models reducing traffic forecasting errors below 7%. The review also incorporates mathematical formulations, model architecture, and comparative performance tables. However, challenges remain, including limited data availability, model interpretability, and computational intensity. Looking ahead, research opportunities lie in digital twins, explainable AI, generative models for design, and robotics for autonomous construction. The study concludes that AI is evolving from a supporting tool into a foundational technology, driving efficiency, sustainability, and resilience in civil engineering worldwide.
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BibTeX
@article{ibrahim2026artificial,
title = {{Artificial Intelligence in Civil Engineering a Comprehensive Review of Applications, Challenges, and Future Directions}},
author = {Osama Ahmed Ibrahim and Hesham Hamed and Sherif Abdelnaby and Mostafa Gamal},
journal = {ERU Research Journal},
year = {2026},
month = oct,
doi = {10.21608/erurj.2026.424430.1347},
url = {https://doi.org/10.21608/erurj.2026.424430.1347},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Civil Engineering a Comprehensive Review of Applications, Challenges, and Future Directions
AU - Osama Ahmed Ibrahim
AU - Hesham Hamed
AU - Sherif Abdelnaby
AU - Mostafa Gamal
PY - 2026
DA - 2026/10/25
JO - ERU Research Journal
DO - 10.21608/erurj.2026.424430.1347
UR - https://doi.org/10.21608/erurj.2026.424430.1347
AB - The civil engineering industry, long associated with complexity, high costs, and safety risks, is being transformed by Artificial Intelligence (AI). This review presents a comprehensive state-of-the-art analysis of AI applications across structural, geotechnical, transportation, construction, and water resources engineering. Core technologies—machine learning, deep learning, reinforcement learning, computer vision, and generative AI—are examined in relation to design optimization, structural health monitoring, soil classification, traffic flow prediction, project management, and climate resilience. Findings from over 80 studies reveal that AI consistently outperforms traditional methods, improving prediction accuracy by 25–40% and reducing computational effort by several orders of magnitude. Quantitative case studies demonstrate its impact, from CNNs achieving over 98% accuracy in crack detection to LSTM models reducing traffic forecasting errors below 7%. The review also incorporates mathematical formulations, model architecture, and comparative performance tables. However, challenges remain, including limited data availability, model interpretability, and computational intensity. Looking ahead, research opportunities lie in digital twins, explainable AI, generative models for design, and robotics for autonomous construction. The study concludes that AI is evolving from a supporting tool into a foundational technology, driving efficiency, sustainability, and resilience in civil engineering worldwide.
SN - 2812-622X
SN - 2812-6211
ER -