Machine-Learning-Driven Bioinspired Generative Design for 3D Concrete Printing: A Conceptual Framework

Recent advances in machine learning (ML) and additive manufacturing have created new opportunities for generating multifunctional engineering designs. One emerging application is the integration of ML-driven generative design with bioinspired concepts for 3D concrete printing (3DCP). By learning geometric features and design principles from biological systems, ML models can generate novel design candidates that satisfy predefined performance objectives and manufacturing constraints. A conceptual framework is proposed that integrates biological inspiration, dataset preparation, generative modeling, design evaluation, and fabrication through 3DCP. Key challenge for implementation includes the printability constraints associated with overhanging geometries. This short communication discusses the potential of combining ML-based generative design and 3DCP to develop bioinspired concrete structures and outlines future research directions for applications in extreme environments.