Machine learning for structural design models of continuous beam systems via influence zones

arXiv:2403.09454v1 Announce Type: new
Abstract: 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 …

abstract arxiv concept continuous cs.lg design influence investigation machine machine learning methodology novel operators optimisation perspective systems type via work