NeuralWood

NEURALWOOD: NEURAL NETWORKS FOR THE PREDICTION AND UTILIZATION OF NATURAL MATERIAL VARIATIONS IN TIMBER CONSTRUCTION

Wood’s natural variations, such as fiber orientation and growth rings, lead to nonlinear material behavior. In construction, this complexity is usually simplified by grading timber into narrow categories for standard models (e.g., FEM). While effective for structural safety, this approach often wastes resources, particularly in laminated timber like self-shaping bilayers, where oversized sections are used to compensate for uncertainty.

Our project seeks to improve material efficiency by developing a machine learning–based model that embraces, rather than ignores, wood’s natural variability. Using physics-informed neural networks (PINNs), artificial neural networks guided by physical principles, we can train accurate models with relatively small datasets. Coupled with inverse design optimization (IDO), this approach enables selecting the best timber boards based on their unique material features to meet performance goals with minimal waste.

We will (1) create a dataset of bilayer specimens using digital wood grading, 3D scanning, and computer vision; (2) develop PINN models to predict hygroscopic and mechanical behavior; (3) integrate PINN into IDO to design lightweight bilayer structures; and (4) fabricate and evaluate the results. By aligning computational design with natural variation, this research advances sustainable use of wood through smarter, data-driven material allocation.

PARTICIPATING RESEARCHERS

Tenure-Track Prof. Dr. Thomas Wortmann
Department for Computing in Architecture/Institute for Computational Design and Construction (ICD/CA), University of Stuttgart
Dr.-Ing. Serena Gambarelli
Materials Testing Institute (MPA)

RESEARCHER
Zuardin Akbar (ICD/CA)

PARTNERS
Microtec Srl
Ante-Holz GmbH

FUNDING
Forschungsinitiative Zukunft Bau. Projektnummer:  10.08.18.7-24.40