jismart2024044: Window View Quality Assessment Framework for Multi-Objective Optimization

Abstract

Windows are important elements that connect indoor and outdoor environments, and the quality of views has a significant impact on health and well-being. However, there is currently a lack of systematic indicators that can quantify window view quality, and existing research is scattered across multiple disciplines, making it difficult to form an evaluation tool suitable for architectural design. At the same time, existing landscape evaluation is difficult to embed into a multi-objective optimization platform, and it is impossible to balance landscape quality with other design goals (such as daylighting, energy consumption, and shading) during the design phase. Based on literature analysis, we propose a comprehensive landscape quality evaluation framework. It includes three core variables: view content (evaluating the composition and quality of the landscape features outside the window), view access (measuring the coverage of the view visible from the window), and view clarity (evaluating the unobstructedness and clarity of the landscape). Based on this framework, using Python programming and the Grasshopper platform, we combined landscape quality with other design goals and constructed a multi-objective optimization tool, which provides a scientific and efficient solution for the optimization of early design and layout.