Machine learning for structural design models of continuous beam systems via influence zones
This provides a novel, non-iterative design method for structural engineers, representing a fundamental shift from traditional approaches.
The paper tackled the problem of designing continuous beam systems by developing a machine learning model that predicts cross-section requirements non-iteratively, achieving a mean absolute percentage testing error of 1.6% and generalizing well to systems of variable size.
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.