Bio-Inspired, AI-Optimised Timber Architectures: Integrating Material Intelligence and Energy Performance in the Design Process

Introduction
Timber has re-emerged as a primary structural material in low-carbon architecture, yet its full potential remains underutilised when energy performance, structural behaviour, and fabrication constraints are treated in isolation. This paper presents a bio-inspired, AI-optimised design framework that couples material intelligence in engineered timber systems with energy-driven morphogenesis. Drawing on biological analogues of branching, lattices, and shells, this approach seeks to derive envelope and structural logics that simultaneously minimise operational, embodied energy while preserving architectural expression.

Methods
A parametric environment is developed for CLT and glulam assemblies, encoding grain direction, cross-section variation, connection logic, and panelisation rules. Bio-inspired typologies (e.g., venation-like shading lattices, ribbed shells, and branching supports) are translated into editable generative scripts. Energy and structural performance are evaluated via linked simulation engines for dynamic thermal behaviour, daylight, and FE analysis. Multi-objective evolutionary algorithms and machine learning surrogates (e.g., gradient-boosted trees) search and approximate the design space, optimising annual energy use, daylight autonomy, structural utilisation, and material volume. An architect-in-the-loop interface allows control over formal language, tectonic articulation, and programmatic requirements.

Results
When applied to pavilion-scale and mid-rise prototypes, the workflow yields timber morphologies that reduce annual heating and cooling loads by 20–30% relative to conventional rectilinear envelopes, while cutting material mass by 10–18% through structurally efficient, bio-inspired geometries. Shading lattices derived from venation logics improve daylight uniformity and reduce glare without sacrificing transparency. Sensitivity analyses show that joint topology and grain-aware orientation strongly influence both stiffness and thermal bridging, highlighting the necessity of integrated material and energy modelling.

Conclusions
This study demonstrates that bio-inspired, AI-optimised timber architectures can transform wood from a merely “sustainable” substitute into an active driver of energy-aware form-finding. Embedding material intelligence within AI-assisted parametric workflows enables architects to negotiate structural, environmental, and fabrication criteria as co-equal agents in design, advancing a holistic paradigm for high-performance timber architecture.