旁压试验成果应用

通过几项工程对旁压试验的应用及其资料的整理分析,给出了估算软土不排水抗剪强度,砂土内摩擦角等参数的经验关系式以及应用旁压试验成果估算地基土强度、单桩承载力和基础沉降量等。     

Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks

An unsupervised neural network technique, Growing Cell Structures (GCS) was used to visualize geochemical differences between four different island arc volcanic rock types: basalts, andesites, dacites and rhyolites. The output of the method shows the cluster structure of the dataset clearly, and the relevant geochemical patterns and relationships between its variables. The data can be separated into four clusters, each associated with a specific volcanic rock type (basalt, andesite, dacite and rhyolite), according to a unique combination of major element concentrations. Following clustering, performance of the trained GCS network as a classifier of volcanic rock type was tested using two test datasets with major element concentration data for 312 and 496 island arc volcanic rock samples of known volcanic type. Preliminary classification results are promising. In the first test dataset 94% of basalts, 76% of andesites, 83% of dacites and 100% of the rhyolites were classified correctly. Successful classification rates in the second dataset were 100%, 80%, 77%, and 98% respectively. The success of the analysis suggests that neural networks analysis constitutes a useful analytical tool for identification of natural clusters and examination of the relationships between numeric variables in large datasets, and that can be used for automatic classification of new data.     

冻土旁压试验与单轴试验对比

为了认识冻土旁压试验结果与常规试验结果之间的关系,在室内分别开展了冻结重塑黏土的旁压试验和单轴压缩试验,并对试验结果进行了对比分析。结果表明,在各级荷载作用下旁压曲线一般都呈现应变速率衰减的趋势,而单轴曲线在冻土破坏时会出现渐进流动阶段。旁压试验的应力-应变曲线呈现应变硬化型,而且出现初始拟弹性阶段;而单轴试验的应力-应变曲线则属于应变软化型,并在轴向应变大约为10%时达到剪应力峰值。温度相同时,旁压试验的剪切强度以及初始弹性模量都要大于单轴试验,且温度越低差值越大。     

隧道围岩压力的神经网络时间序列分析

围岩压力是隧道开挖后重要的反馈信息之一,受不确定性因素影响,围岩压力监测数据是一个不平稳的时间序列,包括趋势项和随机项。采用BP网络对不平稳时间序列进行数据拟合,处理趋势部分,利用ARMA模型处理随机部分。结合累进算法,对浙江某新建隧道围岩压力进行时间序列预测。结果表明该方法具有较高的预测精度,最大相对误差为3.73%,能够应用于工程实际当中。     

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model-based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model-based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi-layer feed-forward network (MLFN) and radial basic function network (RBFN).     

人工神经网络与分析测试技术的研究与发展

回顾了人工神经网络研究的发展历程，简要介绍了神经网络模型与算法，对分析测试技术和相关学科中的人工神经网络研究及在流程控制、错误诊断、参数估计、传感器模型、模式识别与分类、环境监测与治理及光谱与化学分析中的应用等作了评述。引用参考文献１１３篇。     

基于粗糙集和人工神经网络的洞室岩体质量评价

针对洞室岩体质量问题,从洞室工程的角度选取能够反映岩体综合工程特性的6个参数,用可拓评判和专家审定的方法构建了决策样本集;再利用粗糙集理论对原始决策样本集进行约简操作,并分析各指标对决策的相对重要性;最后将约简结果生成的规则作为人工神经网络的输入,建立了洞室岩体质量评价模型。通过工程实例分析对比,该模型有效地简化神经网络的网络结构,减少网络的训练步数,提高网络的学习效率,能够较准确地反映洞室岩体的工程特性。     

Three-Dimensional Structural Geological Modeling Using Graph Neural Networks

Mathematical Geosciences - Three-dimensional structural geomodels are increasingly being used for a wide variety of scientific and societal purposes. Most advanced methods for generating these...     

人工神经网络在水文资料插补延长中的应用

水文资料的插补延长一直是水文计算中的一个难题.本文针对水文资料的插补和水文资料的延长问题进行系统的研究.插补水文资料时,采用人工神经网络双向时间序列插补模型,打破了传统的单向时间序列识别模式,应用缺测时段前后已知时段水文资料,插补出缺测水文资料;展延长系列水文资料则应用人工神经网络参证站模型,并应用流量较大年份的径流资料预测未知年份的径流资料,来进一步提高预测精度,并结合紫坪铺流量资料插补延长实例,检验模型的可行性.结果表明该模型对水文资料的插补或对未知年份的径流量都能够进行较好的预测.     

Residual and Fully Softened Strength Evaluation of Soils using Artificial Neural Networks

A backpropagation artificial neural network (ANN) model is developed to predict the secant friction angle of residual and fully softened soils, using data reported by Stark et al. (J Geotech Geoenviron Eng ASCE 131:575–588, 2005). In the ANN model, index properties such as liquid limit, plastic limit, activity, clay fraction and effective normal stress are used as input variables while secant residual friction angle is used as output variable. The model is verified using data that were not used for model training and testing. The results also indicate that the secant residual friction angle of cohesive soils can be predicted quite accurately using liquid limit, clay fraction and effective normal stress as input variables with R ² = 0.93. The sensitivity analysis results indicate that plastic limit and activity have no appreciable effect on ANN predicted secant friction angles. The secant friction angle predictions of the ANN model were also compared with those of Stark’s et al. (2005) curves and the empirical formulas suggested for the same data sets by Wright (Evaluation of soil shear strengths for slope and retaining wall stability with emphasis on high plasticity clays, 2005). The comparison shows that the ANN model predictions are very close to those suggested by the Stark et al. (2005) curves but much better than the prediction of Wright’s (2005) empirical equations. The results also show that ANN is an alternative powerful tool to predict the secant friction angle of soils.     

Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability

The interactions between factors that affect slope instability are complex, multi-factorial, and often difficult to describe mathematically, imposing a challenge for prediction using traditional methods. The power of the ANN and Grey Systems approaches lies in employing the behaviour of the system rather than knowledge of explicit relations. Published data has been used to illustrate the application of these techniques to predicting the state of slope stability. This has been developed into a tool for analysing and predicting future ground movement based on geotechnical properties and historical behaviour.     

Forward Modeling with Forced Neural Networks for Gravity Anomaly Profıle

In this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas–Gürün basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas–Gürün basin, are very close to those obtained from the proposed method.     

Forward Modeling with Forced Neural Networks for Gravity Anomaly Prof?le

In this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas–Gürün basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas–Gürün basin, are very close to those obtained from the proposed method.     

高温-高含冰量冻土原位旁压蠕变试验

为了研究高温-高含冰量冻土的蠕变特性,在青藏高原多年冻土区开展了旁压蠕变试验。试验结果表明：旁压蠕变曲线可以借鉴Ladanyi的工程蠕变理论来表示;每个试验中瞬时弹性应变在各级压力下的总应变中所占比例的平均值为3%～7%,冻土温度轻微的改变都会引起剪切模量的大幅变化;冻土的初始蠕变应变可以采用幂函数形式来计算,其强化系数k=1.87,应变系数与负温绝对值之间呈线性正相关关系。稳定蠕变阶段的应变速率可以用Glen蠕变模型来表示,其中参数n随体积含冰量的增加而线性增加;若体积含冰量外推至100%（纯冰）时,n=3.85,这与前人通过室内试验得出的结果有些差异。温度较低时,蠕变速率较小;当温度趋近于0 ℃时,蠕变速率迅速增大。     

Prediction of Elastic Modulus of Jointed Rock Mass Using Artificial Neural Networks

Two artificial neural network models for the prediction of elastic modulus of jointed rock mass from the elastic modulus of corresponding intact rock and joint parameters have been demonstrated in this paper. The data collected from uniaxial and triaxial compression tests on different rocks with different joint configurations and different confining pressure conditions, reported in the literature are used as input for training the networks. Important joint properties like joint frequency, joint inclination and roughness of joints are considered separately for making the network more versatile. Two different techniques of artificial neural networks namely feed forward back propagation (FFBP) and radial basis function (RBF) are used to predict the elastic modulus ratio.     

基于神经网络对有限元强度折减法分析

基于改进BP神经网络方法对目前较为流行的强度折减法的理论进行分析。人工神经网络可以模拟人脑的思维,可以在完全不知道数据分布形式和变量之间确切关系的的情况下处理边坡各参数之间的复杂非线性映射,利用这一优势预测分析不同的理论在实际应用中的准确性。将已经通过工程手段计算出结果的数据以输入单元、隐含层和输出单元的形式代入系统进行神经网络训练,不同的屈服准则对应训练出不同的网络系统。用训练好的网络对检验数据进行预测分析,还使用这一方法预测了不同剪胀角对边坡破坏的影响程度及趋势,结果显示,对于平面应变问题,在有限元强度折减法中使用DP4和DP5准则所得到的效果较好,DP1准则的误差最大。     

旁压试验确定上海软土地区的单桩承载力

该文搜集上海地区三十多项旁压试验资料工程，以及近133组静载荷试验，经过分析、统计、比较，得出根据旁压试验确定单桩竖向极限承载力的公式，表明该公式的普遍适用性，是对静载试验方法的有益补充。同时对379组分别采用旁压和静探试验估算的单桩承载力的比较，表明采用旁压试验方法估算的单桩承载力与静探方法基本吻合。     

Free-Swell and Swelling Pressure of Unsaturated Compacted Clays; Experiments and Neural Networks Modeling

Expansive soils have received attentions of several investigators in the past half of century in the problematic soils context. Volume change behavior of unsaturated compacted soils in presence of water and change of degree of saturation was observed in two form of heave or collapse. Low water content and low density compacted soils in presence of enough surcharge pressure lose stability and collapse, because of their metastable and susceptible structure to change of degree of saturation. Free-swell and swelling pressure of five compacted clays, covering low to high plastic clays have been investigated in respect to compaction states and swelling pressure was compared with collapse pressure threshold. The results of experiments were utilized in two Artificial Neural Networks to predict free-swell percent and swelling pressure of a soil sample based on index properties and compaction state.     

深基坑工程变形预报神经网络法的初步研究

提出了深基坑变形预报的人工神经网络法,详细介绍了该方法的建模和应用实例。预报结果与实测值较为吻合,从而表现在深基坑工程中利用该方法进行变形预报是可行的。