Digital Form Generation of Heritages in Historical District Based on Plan Typology and Shape Grammar: Case Study on Kulangsu Islet

Abstract

Architectural heritage in historic districts, as a complex type of heritage, encompasses both the uniqueness of the building itself and also shows cultural and regional characteristics as a group, especially for the heritage site that contains multi-culture features. The digitalization research of this type of heritage often focuses on the digital archiving and modeling of heritages but rarely considers the combination of culture analysis and digitalization. This paper develops a digital form generation method for the heritages in historical districts by means of typological plan analysis, the Shape Grammar method, and Grasshopper software. Based on the case study of the modern Western-style house on Kulangsu, a world heritage site and historical district, this paper include three results: (1) dividing the layout plans of Kulangsu modern Western-style houses into three types, that is, native prototypes, foreign prototypes, and mixed prototypes, with 39 sub-types in total; (2) establishing shape grammar for the layout plans of Kulangsu modern Western-style houses with shape grammar sets and “S, L, R, I” expression rules; (3) creating a digital form generation method based on shape grammar result by Grasshopper software, including function cluster creation, function cluster connection and final model generation. This paper presents an example of quantitative analysis of heritage culture and a rapid modeling method of heritage, providing a reference for the construction of a heritage culture database and digital heritage management in historic districts.

Quantitative Research of Street Interface Morphology in Urban Historic Districts:A Case Study of West Street Historic District,Quanzhou

“…Firstly, regarding the research on the spatial morphological features of historical districts: Zhao [58] conducted research based on the "constructive authenticity" theory, consciously analyzing the authenticity of the spatial morphology of specific heritage by categorizing historical districts into three levels: (1) overall layout, (2) street landscapes, and (3) courtyard internal patterns;Zhang et al [59] traced the temporal and spatial changes in street textures of historical districts at different periods, but primarily focusing on the overall structural analysis of the entire district;Both Teng et al [60] and Zhou et al [61] also analysed the spatial morphological characteristics of the historic district.In addition to the analytical discussion of spatial morphological features, some scholars' studies have focused on quantifying morphological features:Yin et al [62] propose a polarized attention-based landscape feature segmentation network (PALESNet),addressing limitations in automatically extracting landscape features;Zhang et al [63] presented a method for the digital generation of heritage in historical areas; Zhang et al [64] established a systematic 3D spatial diagnostic framework;Wu et al [65] explored the application of high-resolution remote sensing technology in the monitoring of historical urban conservation.However, research on Spatial Morphological Features has not deeply explored the spatial geometric Morphological at the street level.…”

Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D Objects

“…Shape grammar, proposed in the 1970s [ 24 ], provides a formal framework for generating and analyzing complex shapes and designs. As as a shape-based visual description grammar and a rule-based automated design grammar, shape grammar has been applied to many domains, including urban planning [ 12 , 13 ], industrial design [ 14 ], and computer-aided design [ 15 ]. Over the years, advancements in shape grammar have led to the development of sophisticated methods for shape generation [ 25 ], analysis [ 26 ], and optimization [ 27 ], integrating computational techniques such as procedural modeling [ 19 ], parametric design [ 28 ], and machine learning [ 29 ].…”

“…Notably, techniques such as Variational Autoencoders (VAEs) [ 1 , 2 , 3 ], 3D Generative Adversarial Networks (3D-GANs) [ 4 , 5 , 6 ], and 3D Stable Diffusion [ 7 , 8 , 9 , 10 ] have shown promise in autonomously producing realistic and diverse 3D shapes. Shape grammars have also been demonstrated as a powerful approach for formal model generation by providing a rule-based framework for generating complex geometric structures and enforcing constraints within objects [ 11 , 12 , 13 ]. Each methodology has its advantages and limitations.…”

Knowledge-Driven and Diffusion Model-Based Methods for Generating Historical Building Facades: A Case Study of Traditional Minnan Residences in China

“…Focusing on the digitalization of heritage in historic districts, a study develops a method combining typological plan analysis, Shape Grammar, and Grasshopper software (based on Rhino 7.0). Using Kulangsu's modern Western-style houses as a case study, the method classifies layout plans into prototypes and creates digital forms, aiding heritage culture databases and digital management [6]. These studies balance traditional architectural conservation theory and practice with new digital opportunities and AI applications.…”