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Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems

机译：坝体工程问题的材料和建模不确定量化效果效果

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摘要

Due to complex nature of nearly all infrastructures (and more specifically concrete dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in dam design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications.
机译：由于近乎所有基础设施（更具体地说的混凝土坝）的复杂性，不确定量化是风险评估的不可分割部分。根据其相对重要性，如认知和梯度，或空间和时间，可能在不同方面传播不确定性。本文的目的是专注于材料和建模不确定性，并将它们与旨在减少传统蒙特卡罗的有限元模拟的计算负担的软计算技术。考虑了几种情况，其中混凝土和基础材料特性，水位和坝几何形状被认为是随机变量。五种软计算技术（即，随机森林，提升回归树，多自适应回归花键，人工神经网络和支持向量机）用于基于不同的训练尺寸来预测各种数量的兴趣。认为人工神经网络是大多数情况下最准确的算法，足够的准确性，可用于可靠性分析作为对数值模型的补充。在训练集中200个样本的结果足以在大多数情况下达到有用的准确性。对于简单的预测任务，误差小于1％的结果。观察到，增加输入参数的数量增加了预测误差。部分依赖性地块提供了大坝设计中最敏感的变量，这与问题的物理一致。最后，为未来的申请提供了几种实际建议。

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