Optimizing High-Rise Residential Form for Multi-Source Landscape View Access: A Target-Based Visibility Analysis Under Performance Constraints

In high-density urban environments, residential design often faces a conflict between maximizing landscape access and maintaining energy-oriented compactness. This study proposes a target-based visibility analysis framework to optimize high-rise forms under strict performance constraints. Utilizing a Quad-mesh reconstruction strategy and Inverse Targeted Ray-Casting, the method accurately quantifies visibility via the cumulative Landscape Visible Surface (LVS) on the target building and Viewpoint-Specific Surface Visibility Rate (Rv) for precise verification against specific landscape targets. The framework is applied to evaluate three morphological prototypes: Compact Tower, Dispersed Tower, and Slab–Tower Hybrid. Quantitative simulations identified the Slab–Tower Hybrid as the optimal solution, demonstrating superior “Visual Morphological Efficiency.” While maintaining a moderate Shape Coefficient (SC = 0.326) to satisfy energy standards, the Hybrid achieved a cumulative Park-View LVS approximately 1.8 times that of the Compact Tower. Furthermore, environmental simulations indicated the Hybrid fosters stable wind environments (0.4–0.7 m/s) and equitable sunlight distribution. The research concluded that through differentiated massing, high-rise architecture can achieve a synergistic balance between visual openness and physical compactness, transforming view analysis from a passive check into an active design driver.

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Publication Details

Journal
Buildings
Published
2026-02-14
DOI
https://doi.org/10.3390/buildings16040790
Primary Topic
Architecture and Computational Design
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing High-Rise Residential Form for Multi-Source Landscape View Access: A Target-Based Visibility Analysis Under Performance Constraints

Dongchi Lai, Jiaming Yu, Yuchuan Zheng, Yechang Zou et al.
Buildings
Architecture and Computational Design
article

Optimizing High-Rise Residential Form for Multi-Source Landscape View Access: A Target-Based Visibility Analysis Under Performance Constraints

Dongchi Lai, Jiaming Yu, Yuchuan Zheng, Yechang Zou, Bo Gao, Yang Guo
article en

Abstract

In high-density urban environments, residential design often faces a conflict between maximizing landscape access and maintaining energy-oriented compactness. This study proposes a target-based visibility analysis framework to optimize high-rise forms under strict performance constraints. Utilizing a Quad-mesh reconstruction strategy and Inverse Targeted Ray-Casting, the method accurately quantifies visibility via the cumulative Landscape Visible Surface (LVS) on the target building and Viewpoint-Specific Surface Visibility Rate (Rv) for precise verification against specific landscape targets. The framework is applied to evaluate three morphological prototypes: Compact Tower, Dispersed Tower, and Slab–Tower Hybrid. Quantitative simulations identified the Slab–Tower Hybrid as the optimal solution, demonstrating superior “Visual Morphological Efficiency.” While maintaining a moderate Shape Coefficient (SC = 0.326) to satisfy energy standards, the Hybrid achieved a cumulative Park-View LVS approximately 1.8 times that of the Compact Tower. Furthermore, environmental simulations indicated the Hybrid fosters stable wind environments (0.4–0.7 m/s) and equitable sunlight distribution. The research concluded that through differentiated massing, high-rise architecture can achieve a synergistic balance between visual openness and physical compactness, transforming view analysis from a passive check into an active design driver.

BuildingsVol. 16(4)
Central South University of Forestry and Technology (CN), Central South University (CN), Changsha University (CN), Changsha University of Science and Technology (CN), South China University of Technology (CN)
Central South University, Hunan University, Central South University of Forestry and Technology
Sustainable cities and communities
Openalex Percentile: Top 56%
Architecture and Computational Design
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