A Rule-Based Design Approach to Generate Mass Housing in Rural Areas of the North China Plain

Abstract

Affected by the development strategy of Rural Space Reconstruction in China, the demand for rural mass housing has peaked in the North China Plain in the past 20 years. However, due to the inefficiency of conventional design methods, the rural houses built appear to have a noticeable trend of urbanization and homogeneity. To propose a more effective design approach to change the hitherto unsuccessful homogenized phenomenon of rural design, the study is based on investigating the composition, configuration and characteristics of the dwellings in some traditional villages of the eastern Shandong Province, and it compares and analyzes the differences between conventional methods and generation methods through three design tests: Test 1 is for the reappearance of a general mode of planning, Test 2 is based on the definition of shape-grammar-based rules and Test 3 is mainly used for the optimizing and programming of rules. Furthermore, based on the three prototypes of homestead combination, three-level rules are determined through the three tests mentioned above: Level-1 describes the housing prototype consisting of four homesteads, which generates a variety of spatial relations through the translation of homesteads. Level-2 describes a neighborhood prototype consisting of 16 homesteads, which generates various samples through splitting prototypes and expanding homesteads. Level-3 describes a block prototype consisting of 64 homesteads, which controls open space and identifies a given base during sample filling. Through the analysis of the tests results, the rationality and feasibility of the generative design approach are verified, proving that this approach effectively solves the design monotony problem that commonly exists in rural mass housing in the North China Plain.

Underground Parking Layout Generation Based on the WaveFunctionCollapse Algorithm

“…Veloso and Krishnamurti [12] proposed a method that employs multi-agent deep reinforcement learning to create spatial agents that interact within site to fulfill specific objectives associated with a house layout configuration. Wang et al [13] utilized shape grammar to generate layouts for traditional village dwellings. Filtration rules have been implemented as an optimization approach by reducing low-quality results, thereby ensuring the efficiency of the generative design process.…”

“…On the other hand, rule-based generation methods generate results based on predefined rules. Existing methods in this category encompass L-systems, cellular automata, genetic algorithms, swarm intelligence, reinforcement learning and shape grammars [8][9][10][11][12][13]. However, both approaches have their limitations in practice.…”