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This chapter examines artificial intelligence’s (AI) transformative impact on infrastructure engineering across design, construction, operation, and maintenance. We explore how deep learning, reinforcement learning, natural language processing, and generative models reshape infrastructure development while addressing ethical considerations and technical challenges. The discussion covers advances in generative AI for design, digital twins, and AI-enhanced operations through graph-based approaches and multi-agent systems. We examine key challenges in AI adoption, including algorithmic bias in object detection across different socioeconomic environments and the importance of explainable AI in natural hazard engineering. This chapter emphasizes balancing AI capabilities with human expertise and oversight, arguing that successful AI implementation in infrastructure requires careful consideration of equity, transparency, and societal impact. Through case studies and applications, we provide insights into how AI can address infrastructure challenges while promoting sustainable, resilient, and equitable solutions.
Cite
Kumar, K. (2026). Artificial Intelligence Supporting Infrastructure Decisions. In K. Stephens and K. Faust (Eds.), The Handbook of Infrastructure Communication (pp. 237-254). Wiley.