The construction industry remains one of the main contributors to environmental degradation due to its high material consumption and massive waste generation. This study introduces Granizzo, a hybrid methodological framework that integrates artificial intelligence (AI), parametric design, and digital fabrication to transform construction and demolition waste (CDW) into sustainable architectural mosaics. The workflow involves material selection, AI-driven classification of fragments, generative design algorithms for pattern optimization, and CNC-based experimental prototyping. A dataset comprising brick, cement, marble, glass, and stone fragments was analyzed using a Random Forest classifier, achieving an average accuracy above 90%. Parametric design algorithms based on circle packing and tessellation achieved up to 92% surface coverage, reducing voids and optimizing formal diversity compared to manually assembled mosaics. Prototypes fabricated with CNC molds exhibited 35% shorter assembly times and 20% fewer voids, confirming the technical feasibility of the proposed process. A preliminary Life Cycle Assessment (LCA) revealed measurable environmental benefits in energy savings and CO₂ reduction. The findings suggest that Granizzo constitutes a replicable methodological platform that merges digital precision and sustainable materiality, enabling a circular approach to architectural production and aligning with contemporary challenges of design innovation, material reuse, and computational creativity.

The construction industry remains one of the main contributors to environmental degradation due to its high material consumption and massive waste generation. This study introduces Granizzo, a hybrid methodological framework that integrates artificial intelligence (AI), parametric design, and digital fabrication to transform construction and demolition waste (CDW) into sustainable architectural mosaics. The workflow involves material selection, AI-driven classification of fragments, generative design algorithms for pattern optimization, and CNC-based experimental prototyping. A dataset comprising brick, cement, marble, glass, and stone fragments was analyzed using a Random Forest classifier, achieving an average accuracy above 90%. Parametric design algorithms based on circle packing and tessellation achieved up to 92% surface coverage, reducing voids and optimizing formal diversity compared to manually assembled mosaics. Prototypes fabricated with CNC molds exhibited 35% shorter assembly times and 20% fewer voids, confirming the technical feasibility of the proposed process. A preliminary Life Cycle Assessment (LCA) revealed measurable environmental benefits in energy savings and CO₂ reduction. The findings suggest that Granizzo constitutes a replicable methodological platform that merges digital precision and sustainable materiality, enabling a circular approach to architectural production and aligning with contemporary challenges of design innovation, material reuse, and computational creativity.