Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require that all training data conform to the same parametrization (e.g., design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this article proposes graph-based surrogate models (GSMs) for trusses. The GSM can accurately predict displacement fields from static loads given the structure’s geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning, which have led to the ability to learn on undirected graphs: a natural representation for trusses. To further promote flexible surrogate models, this article explores transfer learning within the context of engineering design and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads, and applications, resulting in more flexible and data-efficient surrogate models for trusses.

Funicular geometries, which follow the idealized shapes of hanging chains under a given loading, are recognized as materially efficient structural solutions because they exhibit no bending under design loading, usually self-weight. However, there are circumstances in which nonstructural conditions make a funicular geometry difficult or impossible. This paper presents a new design philosophy, based on graphic statics, that shows how bending moments in a nonfunicular two-dimensional curved geometry can be eliminated by adding forces through an external posttensioning system. An interactive parametric tool is introduced for finding the layout of a posttensioning tendon for any structural geometry. The effectiveness of this approach is shown with several new design proposals.

Sports bras are critical to the comfort and performance of female athletes, yet mechanical models of sports bras are generally not used to guide their design. Typically, assessing any sports bra’s performance requires time-consuming and expensive biomechanical testing, which limits the number of designs considered. To more broadly advance knowledge on how different design properties of sports bras affect their performance, this paper presents a new design framework to explore and evaluate the sports bra design space. The framework incorporates methods for body scan analysis, fast simulation, design generation, and performance evaluation. Using these methods together enables the rapid exploration of hundreds, or thousands, of designs—each one having been evaluated on key metrics related to sports bra performance, namely, range of motion and average pressure. With this framework, designers can potentially discover a diverse set of new, high-performing sports bra concepts, as well as gain insights into how design decisions affect performance.

The connections of a spatial truss structure play a critical role in the safe and efficient transfer of axial forces between members. For discrete connections, they can also improve construction efficiency by acting as registration devices that lock members in precise orientations. As more geometrically complex spatial trusses are enabled by computational workflows and the demand for material-efficient spanning systems, there is a need to understand the effects of global form on the demands at the connections. For large-scale structures with irregular geometry, customizing individual nodes to meet exact member orientations and force demands may be infeasible; conversely, standardizing all connections results in oversized nodes and a compromise in registration potential. We propose a method for quantifying the complexity of spatial truss designs by the variation in nodal force demands. By representing nodal forces as a geometric object, we leverage the spherical harmonic shape descriptor, developed for applications in computational geometry, to characterize each node by a rotation and translation-invariant fixed-length vector. We define a complexity score for spatial truss design by the variance in the positions of the feature vectors in higher-dimensional space, providing an additional performance metric during early stage design exploration. We then develop a pathway towards reducing complexity by clustering nodes with respect to their feature vectors to reduce the number of unique connectors for design while minimizing the effects of mass standardization.

Much like their Earth-based counterparts, the requirements of future space habitat structures can be defined by their ability to protect their occupants and provide usable space to live and work in an extreme, isolated environment. Due to the high cost of transporting resources off of Earth’s surface, recent efforts focus on developing increasingly Earth-independent structural designs. These new designs use local regolith-based materials as a possible solution for long-term extraterrestrial sustainability. With a focus on an Earth-independent habitat, this research looks at architectures that use spherical regolith-based concrete shells with carbon fiber polymer reinforcement. The research approach is to formulate the structural design problem as a multi-objective optimization of the habitat shell. The objectives that apply to the shell geometry and cross section include the minimization of transportation and construction costs, and the minimization of the probability of loss due to radiation and micrometeorite events. Direct trade-offs arise. The multi-objective optimization applies Pareto optimization to determine which design elements or options afford the greatest effectiveness or efficiency. The authors examine candidate design solutions based on priorities and performance thresholds which indicate that ISRU-based reinforced concrete may be a valuable future investment. While the cases presented here are limited to lunar surface systems, both the general architectures and the methodology for analysis and design are applicable to future Mars settlements.