Artificial Intelligence and Machine Learning for Structural Analysis and Production Optimization of Autoclaved Aerated Concrete Elements

Autoclaved aerated concrete (AAC) is a lightweight and resource-efficient construction material widely recognized for its thermal insulation, fire resistance, and favorable strength-to-weight ratio. While artificial intelligence (AI) and machine learning (ML) have seen increasing adoption in many areas of construction and concrete technology, their application to AAC engineering and manufacturing remains largely unexplored. This presents significant opportunities to improve design processes, production efficiency, material utilization, and structural performance throughout the AAC value chain. This contribution discusses the potential implementation of AI and ML techniques in three key areas of AAC applications. First, in material development and quality control, data-driven models can support the prediction of mechanical properties, density, compressive strength, and optimal mix proportions based on constituent materials and production parameters. Such approaches have the potential to significantly reduce the time and experimental effort traditionally required for material characterization and optimization. Second, AI-assisted structural engineering workflows can accelerate the design and assessment of AAC elements by enabling rapid prediction of structural behavior, load-bearing capacity, and failure mechanisms. ML models may serve as surrogate tools that complement conventional experimental investigations and numerical simulations, thereby reducing computational effort during preliminary design and optimization stages. Third, AI-based optimization methods can support production and construction planning by aligning building geometries with standardized AAC panel and block dimensions. Such an approach could facilitate the increased use of standard elements, minimize cutting waste, reduce material consumption, simplify logistics, and accelerate installation on site. Furthermore, optimization algorithms may contribute to improved panel geometries and layouts, leading to enhanced structural efficiency while maintaining economical manufacturing processes. Rather than presenting extensive case studies, this work provides an outlook on promising application areas and future research directions for AI and ML in the AAC industry. The paper aims to highlight how data-driven methods could support faster decision-making, more efficient production planning, improved structural performance, and greater sustainability. By outlining these opportunities, the contribution seeks to stimulate further research and encourage the adoption of AI-enabled solutions across the design, manufacturing, and application of AAC elements.