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.

中文翻译：

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通过影响区域进行连续梁系统结构设计模型的机器学习

这项工作从反问题的角度开发了连续梁系统的机器学习结构设计模型。在区分正向、优化和逆向机器学习算子之后，该研究提出了一种基于最近开发的影响区概念的新颖方法，与传统的结构设计方法相比，该方法代表了方法的根本转变。这种方法的目的是概念化一个非迭代结构设计模型，预测任意系统尺寸的连续梁系统的横截面要求。生成已知解决方案的数据集后，将针对未见过的数据识别、训练和测试适当的神经网络架构。结果显示，横截面属性预测的平均绝对百分比测试误差为 1.6%，并且神经网络具有良好的泛化到可变尺寸结构系统的能力。本工作中生成的 CBeamXP 数据集和相关的基于 Python 的神经网络训练脚本可在开源数据存储库中获取，以实现结果的可重复性并鼓励进一步的研究。