Intelligent Concrete: AI-Driven Design and Optimization of Reinforced Concrete Structures

Traditional reinforced concrete (RC) design has historically relied on time-consuming, iterative manual calculations to ensure structural safety. While effective for maintaining security, this method often hampers efficiency and innovation. Consequently, it has struggled to fully meet modern demands for sustainable and optimized construction. These approaches rely heavily on conservative assumptions, standardized material properties, and empirical rules. As a result, they lead to time-intensive design cycles, over-reinforced sections, and suboptimal outcomes in complex or non-standard scenarios. The inherent limitations in exploring high-dimensional design spaces and integrating multi-objective criteria (e.g., cost, durability, and environmental impact) restrict the potential for performance-based, adaptive, and resource-efficient structures. This chapter introduces artificial intelligence (AI)-driven design and optimization as a transformative paradigm shift. With the help of machine learning (ML), deep neural networks, genetic algorithms, and hybrid computational frameworks, it enables data-driven, predictive, and highly optimized RC systems. By automating generative design, topology optimization, and lifecycle performance forecasting, AI produces intelligent concrete structures that are stronger, lighter, and more sustainable. Despite its promise, the chapter also reviews key challenges such as data scarcity, interpretability of AI models, computational costs, regulatory compliance, and ethical concerns, underscoring the need for explainable AI and human–AI collaboration. Real-world case studies and meta-analyses highlight measurable environmental and economic benefits, advocating AI as a transformative paradigm for future sustainable construction. The chapter closes by calling for interdisciplinary efforts to advance reliable, transparent, and equitable AI adoption in structural engineering.