Python-driven sensitivity analysis of geometric parameters: Evaluating the impact of geometric variations on environmental performance of large office in Boston

This research conducted a quantitative analysis of the impacts of design decisions made in the early stages of the design process, specifically focusing on their environmental effects. Through Sensitivity analysis, this study explores the relationship between design parameters of spatial structures and environmental consequences for each geometric form within a large office space in Boston, employing a multidisciplinary approach that integrates Python with parametric modeling software. Specifically, it aims to determine which variables—such as length, width, and height for a cube, and height, radius, and length for a cylinder—most significantly influence the environmental outcomes. The research primarily employs Rhino and Grasshopper for parametric modeling of a cube and a cylinder, followed by climate analysis using Honeybee and Ladybug tools. Subsequently, the Environmental Impact of energy consumption during the operational phase (B6 stage) is assessed through OpenLCA. The findings indicate that the cylinder configuration offers significantly better energy efficiency and 5.3% lower environmental impact compared to the cube. Sensitivity analysis through Scatter plot, FRE, XGBoost, RF, and SHAP values diagrams highlight that among the cube’s parameters (length, width, height), length is a critical factor for its sustainable design, while for the cylinder varieties (height, radius), height holds greater significance. Among the various environmental impacts assessed, fossil fuel depletion emerged as the most crucial category. The investigation conclusively underlines the imperative of optimizing geometric parameters to significantly influence reduce the ecological footprint, thereby advocating for strategic, evidence-based design decisions in the sustainable architecture field.