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Using multiple neural networks to estimate the screening effect of surface waves by in-filled trenches

机译：使用多个神经网络来估计填充沟槽对表面波的屏蔽效果

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摘要

Trenching is an economical and effective method to reduce surface vibrations and isolate structures from shaking. Previous reports on vibration screening concentrated on either experimental work or analytical study. Due to the construction of more complex structures in the last two decades, presenting more complicated boundary conditions, a variety of numerical methods have been used. Complexity of formulation, the large number of parameters involved, and the difficulty and time required to analyze an effective vibration screening makes the direct numerical approach impractical. The purpose of this paper is to explore the use of an artificial neural network to estimate the effectiveness of a vibration screening trench. Three artificial neural networks, BPN, GRNN, and RBF, are used to evaluate the performance of a chosen physical model. The results show that all three models can be used to evaluate effectiveness of screening trenches with varying accuracy, with GRNN having the highest accuracy. There is much stronger agreement with data of numerically calculated results for neural networks than for empirical multi-variate regression methods.
机译：开沟是一种经济有效的方法，可以减少表面振动并使结构免受振动。先前有关振动筛查的报告主要集中在实验工作或分析研究上。由于在过去的二十年中建造了更加复杂的结构，呈现出更加复杂的边界条件，因此已经使用了多种数值方法。公式的复杂性，所涉及的大量参数以及分析有效振动筛分所需的难度和时间使得直接数值方法不切实际。本文的目的是探索使用人工神经网络来评估振动筛查沟槽的有效性。三种人工神经网络BPN，GRNN和RBF用于评估所选物理模型的性能。结果表明，这三种模型均可用于评估精度各异的筛沟的有效性，其中GRNN的精度最高。与经验多元回归方法相比，神经网络与数值计算结果的数据具有更强的一致性。

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