From Digital Documentation to Rule-Based Regeneration: A Shape Grammar Approach to Decorative Patterns Derived from a Huizhou Chastity Archway, China
Abstract
The digital preservation of architectural heritage often remains limited to static documentation, with insufficient support for the structured analysis and contemporary reuse of decorative patterns. This study proposes a shape grammar-based digital approach for analyzing, generating, and applying decorative patterns from Huizhou chastity archways, a representative form of Chinese architectural stone-carving heritage. Pattern images from the Zhang Family Chaste Widow Archway were collected, vectorized, formally decomposed, and encoded as structured design units. These units were organized into a pattern resource library and used to establish eight generative and derivational rules describing the compositional logic of the original carvings. The rule system generated new decorative patterns that retained recognizable cultural and morphological characteristics while allowing formal variation. Expert evaluation of three representative schemes revealed a trade-off between formal innovation and cultural recognizability—the continuity-oriented scheme preserved stronger cultural recognizability, whereas the more variation-oriented scheme achieved higher innovation scores but showed reduced cultural recognizability. The generated patterns were subsequently refined through color and compositional adjustments and applied to textile-based design prototypes. The results demonstrate the feasibility of transforming architectural decorative patterns from static visual records into reusable, rule-based design resources. This study provides a case-based methodological reference for the digital documentation, formal interpretation, and contemporary design translation of two-dimensional decorative patterns extracted from architectural stone surfaces.
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Authors: Niu Liu, 戈晚晴, Wenhui Gu, Xuebing Fang, Duhua Zhao, Tong Yu, Mengqi Zhao
Institutions: Huainan Normal University, Anhui Jianzhu University, Pratt Institute