Advancing Civil Engineering with AI and Machine Learning: From Structural Health to Sustainable Development
Open accessAbstract
The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed civil engineering, offering innovative solutions that enhance the efficiency, accuracy, and sustainability of various engineering practices. AI technologies, including neural networks and deep learning, coupled with ML techniques, are automating complex tasks, optimizing designs, and improving decision-making processes. This paper explores the pivotal role AI and ML play across multiple domains of civil engineering, including structural health monitoring, predictive maintenance, earthquake engineering, and environmental sustainability. By employing AI-driven technologies such as convolutional neural networks and genetic algorithms, this study highlights how these innovations facilitate early detection of structural damage, enhance predictive modeling in seismic areas, and contribute to optimizing renewable energy systems. Additionally, the integration of AI with finite element analysis is examined for its impact on improving simulation accuracy and infrastructure resilience. Challenges related to data quality, ethical considerations, and system integration are also discussed, emphasizing the need for continued research to unlock AI's full potential in civil engineering. The paper concludes by addressing future trends, including digital twins, autonomous construction technologies, and the potential for smart infrastructure systems to support sustainable urban development.
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Convolutional neural networks can detect and classify cracks in concrete images with high accuracy.
“For instance, convolutional neural networks (a DL subclass) can detect and classify cracks in concrete images with high accuracy (Sargiotis, 2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Sargiotis, 2024
The Conclusion’s Figure 4 description says CNNs are used to detect cracks in structures, and §3.2.2 describes CNNs as effective for image classification. The paper does not establish that CNNs classify cracks specifically in concrete images with high accuracy; its high-accuracy statement concerns vibration-based damage detection more generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrevious reviews on AI in civil engineering do not reflect the latest AI paradigms such as generative design or large language models.
“While previous reviews have addressed AI in civil engineering (e.g., Sargiotis, 2024; Baghbani et al., 2022), many do not reflect the latest AI paradigms such as generative design or large language models.”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Sargiotis, 2024
Common ML techniques have proven effective in civil engineering contexts.
“Common ML techniques such as regression models, decision trees, support vector machines, random forests, and neural networks have proven effective in civil engineering contexts (Sargiotis, 2024; Sargiotis, 2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Sargiotis, 2024
Ethical issues related to data privacy, autonomous systems, and workforce impacts are paramount.
“Ethical issues related to data privacy, autonomous systems, and workforce impacts are also paramount (Megdad et al., 2024; Sargiotis, 2024).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Sargiotis, 2024
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Artificial intelligence in civil engineering: emerging applications and opportunities
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CONVOLUTIONAL NEURAL NETWORK FOR THE DETECTION OF CRACKS IN CONCRETE STRUCTURES
Farias, M., Endara, J. C., Cevallos, J., Colpari, M., Ordóñez, T., & Espinales, F. (2026) · Proceedings of International Structural Engineering and Construction · doi:10.14455/isec.2026.13(1).con-18
- Authors
- Sargiotis, D.
- Affiliations
- National Technical University of Athens (GR)
- Corresponding author
- Sargiotis, Dimitrios
- ORCID
- Sargiotis, Dimitrios
- Venue
- International Journal of Architecture and Planning
- Publisher
- SvedbergOpen
- Published
- 2024-09-05
- Volume
- 4
- Issue
- 2
- Pages
- 54-81
- ISSN
- 2788-5046 (ISSN-L)
- OpenAlex
- W4405702176
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 7 · 2025: 5
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Architecture and Planning (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Civil and Structural Engineering Research
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- artificial intelligence in civil engineering, machine learning, structural health monitoring, predictive maintenance, earthquake engineering, structural damage detection, convolutional neural networks, genetic algorithm, renewable energy optimization, finite element analysis, infrastructure resilience, digital twin, autonomous construction, smart infrastructure, sustainable urban development, data quality, ethical considerations
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