Artificial Intelligence in Civil Infrastructures: Methods, Applications, and Emerging Trends

The swift progression of artificial intelligence (AI) is transforming civil infrastructures (CIs) by improving the design, monitoring, maintenance, and administration of intricate infrastructure systems. This review adopted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) with a focus on the Scopus database. This review thoroughly evaluated essential AI methodologies, including deep learning (DL), machine learning (ML), natural language processing (NLP), computer vision (CV), and reinforcement learning (RL), along with their specific applications in CIs areas such as structural health monitoring (SHM), disaster risk assessment, and construction automation. Moreover, emerging trends like the integration of AI with digital twins, the Internet of Things, and generative AI are examined for their capacity to provide real-time analytics, predictive maintenance, and adaptive infrastructure resilience. Although AI presents transformative advantages, difficulties concerning data quality, model interpretability, system integration, and ethical considerations persist as substantial obstacles to mainstream use. The contribution of this study is to facilitate the advancement of intelligent, sustainable, and resilient infrastructure systems by offering a comprehensive overview of existing capabilities and future prospects, addressing the increasing challenges posed by urbanization and climate change.