Bézier-Based Biased Random-Key Genetic Algorithm to Address Printability-Constraints in the Topology-Optimization of Concrete Structures (2022-01)¶

The advancements of additive manufacturing (AM) technologies are typically coupled with research addressing topology optimization, whose aim is to use optimization methods to achieve effective expressions of free-form design. While many studies emphasize the breakthroughs that topology optimization could bring into structural engineering, there are just a few scientific contributions that address design feasibility, accounting for the technological constraints that characterize the different AM techniques. By formulating a stress-constrained topology optimization problem with a more technologically oriented approach, this study aims to optimize concrete structures while enforcing the cross-section width and path-traceability restrictions that affect the feasibility and performance of geometries obtained through the layered extrusion technique. In particular, this paper proposes a curve-based Biased Random-Key Genetic Algorithm that optimizes stress-constrained structures and generates topologies that can be implemented without post-processing operations. The proposed algorithm, when tested on a diverse set of concrete beam configurations, effectively achieved optimized solutions that used between 81% and 75% less material than the full beam configuration. Additionally, each one of the designed topologies adequately met the stress requirements and process-specific constraints. Lastly, two experimental cases also highlighted the printability effectiveness of the proposed approach in conjunction with design of optimized solutions.