Machine learning for structural design models of continuous beam systems
  via influence zones
Adrien Gallet Andrew Liew Iman Hajirasouliha Danny Smyl
This work develops a machine learned structural design model for continuous
beam systems from the inverse problem perspective. After demarcating between
forward, optimisation and inverse machine learned operators, the investigation
proposes a novel methodology based on the recently developed influence zone
concept which represents a fundamental shift in approach compared to
traditional structural design methods. The aim of this approach is to
conceptualise a non-iterative structural design model that predicts
cross-section requirements for continuous beam systems of arbitrary system
size. After generating a dataset of known solutions, an appropriate neural
network architecture is identified, trained, and tested against unseen data.
The results show a mean absolute percentage testing error of 1.6% for
cross-section property predictions, along with a good ability of the neural
network to generalise well to structural systems of variable size. The CBeamXP
dataset generated in this work and an associated python-based neural network
training script are available at an open-source data repository to allow for
the reproducibility of results and to encourage further investigations.