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.

翻译：本研究从反问题视角出发，开发了一种针对连续梁体系的机器学习结构设计模型。在明确区分前向、优化与反问题机器学习算子后，本文提出了一种基于最新发展出的影响区概念的新方法论，该方法相较于传统结构设计方法具有根本性转变。该方法的核心理念在于构建一种非迭代的结构设计模型，能够预测任意规模连续梁体系的截面需求。在生成已知解数据集后，我们确定了合适的神经网络架构，并对其进行了训练和测试。结果表明，截面属性预测的平均绝对百分比测试误差为1.6%，且神经网络对变规模结构体系展现出良好的泛化能力。本研究生成的CBeamXP数据集及配套的基于Python的神经网络训练脚本已在开源数据仓库中公开，以确保结果可重复性并促进后续研究。