An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

Abstract

The conventional methods used for structural optimization are handled by iterative simulation and evaluation runs, that are developed specifically for each problem, these processes can become computationally expensive very quickly. This work describes an approach to optimization of free form space structures, using Finite Element Analysis and Machine Learning, to overcome long computation periods.

As Hajela and Berke also state, approaching a structural problem as a single optimization problem results in a large dimensionality problem that can be cumbersome to solve by heuristic methods that are commonly used for the optimization of complex problems. Subsampling the problem into smaller independent parts can ease the problem-solving process. Through the use of artificial neural networks, the solutions can be obtained in parallel [1].

Through their modularity, space frames can be decomposed into sub-structures that share the same topology, and the complex problem can be simplified into independent modules. A supervised learning algorithm (in this case Back Propagation) is trained on a set of optimized modules to execute an optimal geometry for each structural node in parallel on a given load.

The trained ANN can be applied to any spaceframe structure regardless of their topology, size or complexity. This strategy shifts the optimization procedure from being a problem-specific process to domain-specific process, where within the same domain different problems can be solved. Thus the procedure becomes more sustainable.