Abstract The traditional structural optimal design methods aiming to generate a global optimum may fall into the unfeasible domain due to the presence of uncertainty. This issue can be addressed by generating a group of satisfactory design or sub-design regions rather than a single optimal one. A data mining method has been recently developed based on the decision tree technique and applied to the engineering structural design by learning from a big design dataset. It solves the design problems in an explainable way and helps designers understand design problems efficiently. This method, based on the traditional decision tree algorithm, however, cannot handle uncertain data. In this work, a new decision tree for uncertain data (DTUD) method is developed based on the joint probability distribution of design variables for the engineering design. Its high accuracy is verified by comparing it with the traditional decision tree using nine datasets selected from a publicly available repository. To demonstrate the performance of this method in structural design problems, it is implemented in the design of a thin-walled energy-absorbing structure subjected to crash loading. With assumed probability distribution on the uncertain data, an uncertain decision tree is built, which generates designs with expected performance effectively and efficiently. Besides, the deterioration of design performance due to uncertainty can be captured by the new decision tree. This further helps improve the reliability of the new designs.

中文翻译：

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一种设计变量不确定的结构设计数据挖掘方法

摘要 以产生全局最优为目标的传统结构优化设计方法可能由于存在不确定性而陷入不可行域。这个问题可以通过生成一组令人满意的设计或子设计区域而不是单个最佳区域来解决。最近开发了一种基于决策树技术的数据挖掘方法，并通过从大设计数据集中学习将其应用于工程结构设计。它以可解释的方式解决设计问题，帮助设计人员有效地理解设计问题。然而，这种基于传统决策树算法的方法无法处理不确定数据。在这项工作中，基于工程设计的设计变量的联合概率分布，开发了一种新的不确定数据决策树（DTUD）方法。通过使用从公开可用的存储库中选择的九个数据集将其与传统决策树进行比较，验证了其高精度。为了证明这种方法在结构设计问题中的性能，它在承受碰撞载荷的薄壁吸能结构的设计中实施。利用不确定数据的假设概率分布，构建不确定决策树，从而有效且高效地生成具有预期性能的设计。此外，新决策树可以捕获由于不确定性而导致的设计性能恶化。这进一步有助于提高新设计的可靠性。为了证明这种方法在结构设计问题中的性能，它在承受碰撞载荷的薄壁吸能结构的设计中实施。利用不确定数据的假设概率分布，构建不确定决策树，从而有效且高效地生成具有预期性能的设计。此外，新决策树可以捕获由于不确定性而导致的设计性能恶化。这进一步有助于提高新设计的可靠性。为了证明这种方法在结构设计问题中的性能，它在承受碰撞载荷的薄壁吸能结构的设计中实施。利用不确定数据的假设概率分布，构建不确定决策树，从而有效且高效地生成具有预期性能的设计。此外，新决策树可以捕获由于不确定性而导致的设计性能恶化。这进一步有助于提高新设计的可靠性。新的决策树可以捕获由于不确定性而导致的设计性能恶化。这进一步有助于提高新设计的可靠性。新的决策树可以捕获由于不确定性而导致的设计性能恶化。这进一步有助于提高新设计的可靠性。