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Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

机译：使用人工神经网络评估填充泡沫的沟槽的减振性能

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Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R-2) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches. (C) 2019 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society.
机译：城市中的开发活动通常会产生地面振动，这可能会导致附近建筑物中的居住者感到不适，对建筑物中进行的活动造成干扰并可能损坏附近的建筑物。这种地面振动是由诸如打桩，地面压实等建筑活动以及公路和铁路交通引起的。使用沟槽是减轻这种地面振动的不利影响的有效方法。沟槽的有效性取决于许多参数，包括振动源的特性，土壤介质和沟槽内填充材料，沟槽尺寸和接收器的要求。因此，由于许多参数及其数值范围的影响，选择有效的沟槽来减轻振动的过程可能会变得很复杂。本文研究了使用人工神经网络（ANN）作为一种智能，有效的工具来预测填满泡沫塑料的沟槽缓解地面振动的有效性。为此，通过对具有有效的有限元（FE）模型的数值模拟，通过对控制参数的影响进行广泛研究，建立了一个数据库。在距振动源一定距离的地方，引入了填充泡沫泡沫的沟槽，以评估关键参数（如激励频率，载荷振幅，沟槽构型（即深度和宽度），土壤剪切波速度）的变化对减振效果的影响。 ，土壤密度和阻尼比。选择这些作为ANN的输入参数，同时将振幅减小比和峰值粒子速度（PPV）视为输出。使用了多层前馈网络，并使用Levenberg-Marquardt算法对其进行了训练。通过比较确定系数（R-2）和均方误差（MSE）评估具有不同配置的神经网络。然后，将最佳架构用于预测先前的结果，从而揭示了ANN方法的准确性和有效性。这项研究的发现将为使用填埋泡沫的沟槽减轻振动提供有用的信息。 （C）2019年由Elsevier B.V.代表日本岩土工程学会制作和主持。

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