Emerging artificial intelligence methods in civil engineering: A Comprehensive Review
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Abstract
The integration of Artificial Intelligence (AI) in civil engineering is reshaping traditional practices and driving innovation across the field. This comprehensive review explores emerging AI methods, including machine learning, deep learning, natural language processing, computer vision, generative AI, and reinforcement learning, highlighting their applications in key civil engineering domains. AI is revolutionizing structural engineering through predictive maintenance and design optimization, enhancing construction management with intelligent scheduling and automation, and transforming geotechnical, transportation, environmental, and water resources engineering with advanced modeling and predictive analytics. Despite its transformative potential, the adoption of AI in civil engineering faces significant challenges, such as data standardization, model interpretability, integration with established practices, and computational demands. Addressing these challenges requires continued research, ethical governance, and collaboration among academia, industry, and policymakers. This review underscores the importance of integrating AI with emerging technologies, such as IoT, blockchain, and digital twins, to unlock new possibilities for sustainable and resilient infrastructure. By addressing existing limitations and embracing advancements in AI algorithms, civil engineering is poised to achieve unprecedented levels of efficiency, sustainability, and innovation. This paper concludes with a call for ongoing research and development to fully harness the transformative potential of AI in building the infrastructure of the future.
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