Advancing Civil Engineering with AI and Machine Learning: From Structural Health to Sustainable Development
Year: 2024
Type: article
Abstract: 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.
Author: Dimitrios Sargiotis
Institution: National Technical University of Athens
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405702176