Machine learning-aided generative design methodology for a Martian regolith habitation shell

Abstract

This study presents a novel design methodology to automate the development process of a regolith habitation shell for a crewed mission on Mars. The integration of generative design, simulation tools, and data science methodologies allows for a systematic and comprehensive iterative design process, which facilitates the development of dual-habitation shells that can resist harsh conditions on Mars.The study establishes a methodology consisting of four fundamental sections. Initially, the environmental conditions on the surface of Mars are assessed and quantified as structural loads, so that the generative design tools effectively transform these stresses into design parameters that might be used in finite element modeling (FEM). Next, this study employs human factors derived from existing literature as input for Social Network Analysis (SNA) and the Label Propagation Algorithm (LPA) to address important design inquiries concerning functional zoning, layout configuration, geometry generation, and habitability variables. These inquiries aim to inform the decision-making process regarding autonomous typology design. Furthermore, the use of space syntax, physics simulations, and Voronoi algorithms improves the accuracy of the habitation planning process by generating floor designs and habitable volumes that align with the distinctive requirements and preferences of individuals. Lastly, the regolith habitation shell is constructed using structural simulation techniques that incorporate Mars' environmental pressures as design considerations. By employing Karamba3D and numerical finite element modeling (FEM) techniques, the structural performance of the regolith habitation shell under the environmental conditions on Mars is simulated.Consequently, this study introduces three distinct habitation shell designs that were developed and exposed to structural simulations to evaluate the effects of environmental stresses on Mars. The study explores the conjunction between generative design, data science, and structural simulations by employing a systematic and comprehensive design process. Its objective is to provide insights into the impact of environmental pressures on habitation shells and improve our understanding of design outcomes for future exploration on Mars.