Development of measurement of soil hydraulic properties by using in situ permeability tests data

近年来，对深基坑开挖及其资源开发利用的需求不断增加。这就需要预测多层含水层中地下水的行为，以促进其保护。准确确定各含水层的水力学性质对正确预测地下水流具有重要意义。抽水试验通常在多层或越流含水层条件下进行。然而，在这些条件下抽水试验所获得的数据很难进行“解析”分析，在这项研究中，提出了一种新的方法，从抽水试验数据估计含水层系数在复杂的含水层条件。土壤的水力学性质、渗透系数和储水量是预测地下水动态的基本数据。通常进行泵送试验来确定这些性能。本文提出了一种利用抽水试验得到的水位降深曲线来评价土壤水力特性的新方法。在我们开发的方法中，使用了神经网络的模式匹配能力。神经网络的训练识别模式的水位降深数据作为输入和相应的承压含水层的水力特性作为输出。训练好的网络在接收抽水试验数据作为输入模式时产生水力特性的输出。在越流含水层和各向异性承压含水层中观测到的水位下降数据被用来评估我们所提出的方法的有效性。

Recently, the demand for deep underground excavation and the development and utilization of its resources has increased. This raises a need to predict the behavior of the groundwater in multilayered aquifers in order to promote its conservation. The exact determination of hydraulic properties of each aquifers is very important for the correct groundwater flow prediction. Pumping tests are usually performed under the multilayered or leaky aquifer conditions. It is, however, difficult to analyze the data obtained from the pumping test under these conditions "analytically".In this research, a new method of estimating aquifer coefficients from pumping test data in a complicated aquifer conditions is proposed. The soil hydraulic properties, coefficient of permeability and storage are essential data to predict the behavior of groundwater. Pumping tests are usually performed to determine these properties. In this paper, a new approach to evaluate soil hydraulic properties from drawdown curves which are obtained by pumping tests has been developed. In our developed method the pattern-matching capability of a neural network is used. The neural network is trained to recognize patterns of drawdown data as input and corresponding hydraulic properties in the confined aquifer as output. The trained network produces output of hydraulic properties when it receives pumping test data as the input patterns. Drawdown data which are observed in a leaky aquifer or an anisotropic confined aquifer are used to evaluate availability of our proposed method.