Artificial Intelligence in Civil Engineering: A Comprehensive Review of Applications in Structural Design, Construction, and Infrastructure Management

Review Article

Abstract

The integration of Artificial Intelligence (AI) into civil engineering has marked a significant paradigm shift from conventional methods to intelligent, data-driven approaches in the planning, design, construction, and management of infrastructure. This comprehensive review explores how AI technologies—including machine learning (ML), deep learning (DL), computer vision, and evolutionary algorithms—are revolutionizing key domains such as structural design optimization, construction automation, and infrastructure lifecycle management. By analyzing recent research developments and industrial applications, the paper highlights how AI enhances accuracy in structural analysis, improves safety and efficiency in construction processes, and enables predictive maintenance strategies for civil infrastructure systems. Moreover, it emphasizes the pivotal role of AI in facilitating smart cities and sustainable urban development through real-time data integration and adaptive decision-making. Several real-world case studies are discussed to illustrate the practical benefits and challenges associated with AI implementation. The review also addresses critical issues such as data scarcity, algorithmic bias, lack of model generalizability, and the need for interdisciplinary collaboration between engineers, computer scientists, and policymakers. Finally, it proposes future research directions to ensure responsible, scalable, and ethically-aligned adoption of AI in civil engineering practices.