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弱透水层是含水层系统的重要组成部分,其水文地质参数的准确获取一直以来都是研究难点,传统室内试验难以克服应力状态及环境变化对土层参数的影响,因此如何在原位状态下评价其水文地质参数有着重要意义。选取呼和浩特盆地某淤泥层为研究对象,基于抽水试验和溶质运移试验获得的多源数据,采用基于控制体积有限差分法的非结构化网格（Unstructured Grid）方法,建立三维地下水流-溶质运移耦合模型,对弱透水层水文地质参数进行反演。结果显示:模型计算的抽水井中水位和溶质浓度变化规律与实际观测数据能够较好拟合,模型识别出的抽水含水层渗透参数（4.8 m/d）与通过解析法计算得出的结果（4.17 m/d）相近,建立的数值模型符合实际水文地质条件;反演得到淤泥质弱透水层垂向渗透系数为1.2×10?4 m/d、储水率为1.0×10?5 m?1。基于多源数据的参数反演方法可为弱透水层参数研究提供借鉴。 相似文献
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水文地质参数的正确与否是构建地下水数值模型的根本,而参数寻优结果很大程度上取决于优化算法的选择。禁忌搜索算法是一种广泛应用于组合优化问题的启发式全局寻优算法,但在连续函数优化领域应用比较少。基于上述考虑,本文首先引入求解连续函数优化问题的连续禁忌搜索算法并对其进行改进,进而提出一种连续禁忌搜索改进算法(ICTS),最后将其与地下水模型耦合进行水文地质参数识别。算例研究表明,ICTS算法较其他算法(CTS,SGA,Micro-GA,PSO)求解效率提高1.87～4.64倍,求解精度提高1.08～12.86倍。因此ICTS算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。 相似文献
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将一种新的启发式和谐搜索算法(harmony search algorithm,HS)引入到水文地质领域。在对其音节调整步骤进行改进的基础上,提出快速和谐搜索算法(Fast harmony search algorithm,FHS),并将FHS算法与MODFLOW结合,用于水文地质问题的参数识别。算例研究表明,FHS算法较其他多种算法具有更强的全局搜索能力、更快的收敛速度及求解精度,可用于地下水数值模型的参数反演。 相似文献
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地面沉降主要由深层地下水开采造成,含水层的压缩释水是深层地下水开采量主要构成来源,同时随其压缩变形孔隙比减小造成储水系数、渗透系数的减小,对弱透水层非弹性释水量、越流补给量造成影响。地面沉降过程中的水文地质参数非线性变化及对承压含水层系统的反馈作用也成为水文地质领域的前沿问题。为对地下水开采量、沉降量、地质参数变化以及给水能力变化之间的关系做一个较为定量定性的探究,以含水层压缩过程中的物理机制为依据,并基于沧州地区深层承压含水层的地下水位变化过程和水文地质参数,采用数学手段构建储水系数和渗透系数变化的一维非线性沉降模型。模拟结果显示随承压水头下降,储水系数最高可减小77%,含水层系统的给水能力和储水能力会随压缩变形减小50%甚至更多。研究成果为深入认识水文地质参数与应力变化相关关系、科学评价承压含水层地下水储水调节能力有重要借鉴意义。 相似文献
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Prediction of pile settlement using artificial neural networks based on standard penetration test data 总被引：4，自引：0，他引：4
F. Pooya Nejad Mark B. Jaksa M. Kakhi Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions. 相似文献
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将人工神经网络(ANN)技术引入到地下水含水量预测工作,以华北平原和河套平原为试验场,以若干已知钻孔为验证,采用激电和电阻率测深等地面物探方法获取视电阻率ρS、视极化率ηS、半衰时Th、衰减度D和偏离度σ等参数为输入神经元对单孔单位涌水量建立人工神经网络预测模型。同时,为消除不同地区矿化度的影响,通过实验对比引入综合参数T",改良了输入神经元的配比。最终建立以半衰时Th、衰减度D、偏离度σ和综合参数T"为输入神经元的含水量预测模型,进一步提高了预测精度。通过检验,发现所建立的模型对平原地区进行含水量的定量预测有着较好的效果,为含水量预测工作研究与发展带来了新理念、打开了新思路。 相似文献
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为了解决传统的Theis配线法在求参过程中无法利用全部抽水试验数据、手动配线效率较低、求参过程受人为主观因素影响较大等问题,利用抽水试验数据,以Theis公式和叠加原理为理论基础,以理论计算降深与实测降深的Nach-Sutcliffe效率系数值达到最大为目标函数,基于Matlab软件编程,再利用遍历搜索算法对导水系数（T）、贮水系数（S）在其对应范围内进行遍历,实现了水文地质参数的自动精确求解。将遍历搜索算法应用于2个抽水试验实例的水文地质参数求解,并与传统求参方法的计算结果进行比较分析。结果表明：利用遍历搜索算法求参的计算结果与传统求参方法的计算结果相近,表明利用遍历搜索算法求解水文地质参数有效可靠;2个抽水实例利用遍历搜索算法得到的参数对应的理论计算降深与实测降深的Nach-Sutcliffe效率系数值分别为0.996 5、0.970 8,且相比传统的求参方法而言更接近1.000 0,说明其拟合程度更好。 相似文献
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A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
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广义回归神经网络预测加筋土支挡结构高度 总被引：9，自引：3，他引：9
土工合成材料加筋支挡结构（Geosythetics-Reinforced Retaining Wall, 简称GRW）设计方法主要是建立在似粘聚力理论基础之上的半经验设计法。由于土性及加筋机理的复杂性，常常要对它们进行人为假定，导致计算结果差强人意。神经网络方法与传统方法的不同之处在于不需要主观假定，而是模拟人脑思维，通过数据样本的学习来获得预测结果。引入神经网络技术来预测加筋土支挡结构的设计高度是一种新尝试。由于本问题具有样本容量非常有限、影响因素复杂多样的特点。因此，采用适用于稀土样本数据的广义回归网络（General Regression Neural Network)来预测加筋土支挡结构设计高度。基于MATLAB神经网络工具箱及文献[1]的挡墙离心模型试验结果，建立了一个可用于加筋支挡结构设计高度预测的GRNN网络。通过对足尺试验，实际工程及模型试验结果的检验，表明网络的学习是成功的，具有一定指导意义。 相似文献
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建立于煤矿开采基础之上的矿山开采沉陷理论和预测方法并不适用于象金川这样厚大、陡倾的金属矿床开采的岩移问题,因此,本文探讨利用神经网络来对地表岩移进行预测。根据Elman神经网络能够逼近任意非线性函数的特点和具有反映系统动态特性的能力,提出了利用Elman神经网络建立地表岩移时序预报模型的方法。利用金川二矿区GPS监测所得到的时间序列数据,通过对Elman神经网络模型预测值与GPS实测值之间的比较,结果表明模型预测显示了良好的准确性,特别是在时间步长较短情况下,应用于实际预测一定程度上可以弥补金属矿山岩移预测方法不足的缺憾。 相似文献
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Yao Yevenyo Ziggah Hu Youjian Alfonso Tierra Ahmed Amara Konaté Zhenyang Hui 《Arabian Journal of Geosciences》2016,9(17):698
Two national horizontal geodetic datums, namely, the Accra and Leigon datum, have been the only available datum used in Ghana. These two datums are non-geocentric and were established based on astro-geodetic observations. Relating these different geodetic datums mostly involves the use of conformal transformation techniques which could produce results that are not very often satisfactory for certain geodetic, surveying and mapping purposes. This has been ascribed to the incapability of the conformal models to absorb more of the heterogeneous and local character of deformations existing within the local geodetic networks. Presently, application of new approaches such as artificial neural network (ANN) is highly recommended. Whereas the ANN has been gaining much popularity to solving coordinate transformation-related problems in recent times, the existing researches carried out in Ghana have shown that only three-dimensional conformal transformation methods have been utilized. To the best of our knowledge, plane coordinate transformation between the two local geodetic datums in Ghana has not been investigated. In this paper, an attempt has been made to explore the plane coordinate transformation performance of two different ANN approaches (backpropagation neural network (BPNN) and radial basis function neural network (RBFNN)) compared with two different traditional techniques (six- and four-parameter models) in the Ghana national geodetic reference network. The results revealed that transforming plane coordinates from Leigon to Accra datum, the RBFNN was better than the BPNN and traditional techniques. Transforming from Accra to Leigon datum, both the BPNN and RBFNN produced closely related results and were better than the traditional methods. Therefore, this study will create the opportunity for Ghana to recognize the significance and strength of the ANN technology in solving coordinate transformation problems. 相似文献
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Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks 总被引：2，自引：1，他引：1
An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity
distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their
ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer
parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested
on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates
of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity
and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed
under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2,
there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity
distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions.
Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems. 相似文献
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基于MODFLOW参数不确定性的地下水水流数值模拟方法 总被引：1，自引：0，他引：1
考虑到模型不确定性引起的地下水数值模拟不确定性对模拟过程的影响,在简要介绍含水层水文地质参数变异性研究进展和地质统计学的基础上,基于常用的确定性地下水流数值模拟软件MODFLOW开发了MODFLOW-Gslib软件,相较于传统的数值模拟方法,将地质统计学与数值模拟结合的方法能够模拟非均质含水层中的参数变异性问题。将MODFLOW-Gslib软件运用于模拟实例中,选择常见的不确定性因素进行模拟,并对其模拟产生的数据进行统计分析,结果表明,软件转化后的参数符合水文地质参数不确定性的相关特征;与原模拟结果进行对比,该软件能够更加真实地刻画含水层参数变异性特征。 相似文献
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人工神经网络在地球科学中的应用综述 总被引：7，自引：0，他引：7
人工神经网络（ANN）是一种动态信息处理系统，它具有联想记忆、自组织、自适应、自学习和容错性等特性。ANN可对地质环境和各类地质对象进行判别分类；可根据地震及测井中的各种波谱曲线进行地质构造模式识别，并实现自动编辑和解释；可对各种遥感信息进行图像处理和地质构造解译；在勘查地球物理方面，可解决某些反演问题；在矿产勘查中可用于矿产资源定量预测与评价勘查过程中的组合优化及多目标决策等问题。 相似文献