Design Optimization of Modularized Construction
Enhancing modularized design in college dorms through simulation techniques
Group Research Project
Partner: Hairuo Zhao
Advisor: Nathan Brown
2024
The modular construction method is particularly effective for designs with repetitive patterns. It reduces the overall project timeline by leveraging lead time, minimizes on-site labor by limiting construction activities to the installation of prefabricated units, and enhances quality through fabrication in a controlled environment. Parametric modeling is also beneficial as it allows for the exploration of different design alternatives, assessment of configuration trade-offs, and management of irregular layouts. By integrating these tools, there is potential to optimize the design of college dorms, balancing various factors effectively.
This study explores the potential implementation of computational optimization methods in designing the layout of modular college dorm units and arranging the landscape of residential halls.
Motivation and Methodology
More than 45% college students are facing housing insecurity due to:
Very limited available space on-campus for student apartments
Increased desire among the younger population to pursue higher education
More students want on-campus living experiences post COVID-19 pandemic
The goal of this research is to increase the efficiency of solving this problem, the project wants to use simulation and optimization tools to help determine the layout of modularized college dorm units to optimize space utilization within a limited space while finding a compromised solution of acceptable living comfort.
The project's development is structured into three distinct stages: basic geometric model development, simulation, and optimization analysis.
Algorithm and Optimization
The 3D modeling software Rhino and Grasshopper are used for developing basic geometric models and linking design variables. Throughout the development phase, we tested several algorithms and strategies, including the implementation of Python and C++ scripts in Grasshopper, and plugins like OpenNest and Anemone. Following the testing and result evaluation, OpenNest was selected for packing placement due to its straightforward approach to the 2D packing problem. Additionally, Radical and Wallacei X are utilized for simulating housing capacity and performing multi-objective optimization.
The simulation is analyzed by implementing Radical and Wallacei X in Grasshopper, which automatically decides the ratio compared to the best design from the previous selection. The objective functions will undergo analysis using Radical, followed by an examination of varying numbers of components that being packed in the assigned boundary space. The optimal design uses Pareto data generated through Wallacei X's multi-objective analysis.
The selection of the optimal design is achieved through the analysis of objective functions using Wallacei X, a Grasshopper plugin designed specifically for data analysis. These two key objectives are input into Wallacei X for advanced analysis, aiding in decision-making and evaluating the design effectiveness.
Pareto Front designs represent the optimal solutions derived from the objectives set for a project, using algorithmic calculations to recommend potential designs. Visual diagrams are used to help the construction design team and stakeholders understand the benefits of different design options. These diagrams help in evaluating various expectations and constraints, facilitating informed decision-making throughout the design process.
