Revolutionizing Timber Structures: A Comprehensive Review On Ai-Driven Structural Design, Performance Optimization, And Future Perspectives

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Abstract

With a focus on developments in AI-driven design, performance optimization, and future directions, this thorough review explores the revolutionary effects of artificial intelligence (AI) on timber structural engineering. With the rise of engineered wood products like Cross-Laminated Timber (CLT) and glued laminated timber (Glulam), which provide improved structural performance, aesthetic versatility, and environmental benefits, timber has seen a resurgence as a sustainable building material. Traditional timber design techniques, which mostly rely on empirical methodologies, are becoming less and less capable of handling complicated architectural geometries and modern performance needs. Timber engineering is undergoing a revolution because of artificial intelligence (AI) tools like machine learning, deep learning, and genetic algorithms, which make it possible to precisely characterize materials, optimize load paths, and detect failures in real time. Researchers have greatly shortened calculation times and increased simulation accuracy by combining these techniques with traditional finite element analysis (FEA). Long-term structural integrity is also ensured by the integration of sensor-based data collection and Building Information Modeling (BIM), which makes dynamic monitoring and predictive maintenance easier. The literature on AI applications in timber design is summarized in this review, which also describes how conventional methodologies gave way to modern digital practices. It talks about the advantages and difficulties of incorporating AI into performance monitoring and simulation workflows. By bridging the gap between traditional construction practices and advanced computational technologies, this review offers valuable insights for researchers, engineers, and practitioners dedicated to advancing modern timber engineering.

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