Parametric Landscape Facilities Aesthetic Design Method Based on SOR Model and Hybrid Kansei Engineering: A Case of Landscape Corridors

Parametric design stands out in contemporary landscape facilities design with its distinctive beauty sense. However, understanding this beauty sense and establishing an aesthetic design method is one of the problems needed to be solved. In this context, this study integrates the Stimulus-Organism-Response (SOR) model and hybrid Kansei Engineering establish the aesthetic design method for parametric landscape facilities from the perspectives of cognition and positivist design. Firstly, the SOR model is used to reveal the aesthetic cognitive mechanism of parametric landscape facilities. Secondly, the forward Kansei Engineering is used to extract design features. Thirdly, the extracted design features are combined with shape grammar for parametric modeling in the Grasshopper platform. Fourthly, backward Kansei Engineering is used to evaluate design schemes and analyze their data of beauty sense. Finally, this study takes the landscape corridor as a case to illustrate the proposed method. The results show that (1) in aesthetic cognition, dynamic visual forms, transparency of spatial feeling, and abstract style have a significant positive impact on the beauty perception of parametric landscape facilities, and the beauty perception of parametric design has a unique appeal to the general public. (2) The design case verified the effectiveness of this method, and this study can provide a valuable reference for parametric landscape facilities.

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Publication Details

Journal
Buildings
Published
2025-08-27
DOI
https://doi.org/10.3390/buildings15173065
Primary Topic
Color perception and design
Type
article
Field-Weighted Citation Impact
0.00

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article

Parametric Landscape Facilities Aesthetic Design Method Based on SOR Model and Hybrid Kansei Engineering: A Case of Landscape Corridors

Peilin Hong, Yexin Chen, Yunpeng Xu, Shilin Guo et al.
Buildings
Color perception and design
article

Parametric Landscape Facilities Aesthetic Design Method Based on SOR Model and Hybrid Kansei Engineering: A Case of Landscape Corridors

Peilin Hong, Yexin Chen, Yunpeng Xu, Shilin Guo, Hongyang Zhu, Huiran Yan, Xuan-Hui Xie
article en

Abstract

Parametric design stands out in contemporary landscape facilities design with its distinctive beauty sense. However, understanding this beauty sense and establishing an aesthetic design method is one of the problems needed to be solved. In this context, this study integrates the Stimulus-Organism-Response (SOR) model and hybrid Kansei Engineering establish the aesthetic design method for parametric landscape facilities from the perspectives of cognition and positivist design. Firstly, the SOR model is used to reveal the aesthetic cognitive mechanism of parametric landscape facilities. Secondly, the forward Kansei Engineering is used to extract design features. Thirdly, the extracted design features are combined with shape grammar for parametric modeling in the Grasshopper platform. Fourthly, backward Kansei Engineering is used to evaluate design schemes and analyze their data of beauty sense. Finally, this study takes the landscape corridor as a case to illustrate the proposed method. The results show that (1) in aesthetic cognition, dynamic visual forms, transparency of spatial feeling, and abstract style have a significant positive impact on the beauty perception of parametric landscape facilities, and the beauty perception of parametric design has a unique appeal to the general public. (2) The design case verified the effectiveness of this method, and this study can provide a valuable reference for parametric landscape facilities.

BuildingsVol. 15(17)
Hubei University of Technology (CN)
Department of Education of Hebei Province
Openalex Percentile: Top 74%
Color perception and design
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Parametric Landscape Facilities Aesthetic Design Method Based on SOR Model and Hybrid Kansei Engineering: A Case of Landscape Corridors — Peilin Hong, Yexin Chen, et al. · Buildings (2025) | TGRS Research Map | TGRS