地表下沉系数计算方法研究

地表下沉系数计算方法研究邹友峰（焦作工学院测工系，４５４１５９）１引言地表下沉系数是预计地表沉陷的关键性参数，其取值的准确性决定了地表移动变形的精图１水平煤层半无限开采平面应变问题度。目前，大量的实测资料已证实下沉系数与覆岩岩性有关，岩体越坚硬，...     

超深沉井下沉周边环境效应与控制措施

针对镇江大港水厂一期取水工程中的超深沉井施工,利用耦合欧拉-拉格朗日(CEL)方法建立了三维数值计算模型,考虑沉井下沉深度、井内土体高度、井-土界面极限摩阻力等因素,分析超深沉井下沉周边环境效应。计算结果表明:沉井下沉影响范围约为距沉井侧壁0.5倍下沉深度;随着井-土界面极限摩阻力提高,土体变形和下沉阻力均增大;井内留有一定土体高度可有效降低环境变形,但会使下沉阻力增大。根据计算结果,提出了沉井下沉地表沉降计算公式,并从加固范围、注浆、井内土体高度等方面提出了沉井下沉控制措施,可为今后类似工程提供借鉴。     

基于倾角变化的最大下沉值计算方法

最大下沉值是度量层状矿体(如煤层)开采引起的地表移动变形程度的重要参量。地表最大下沉值与开采工作面几何要素相关，基于倾角变化的最大下沉值计算模型综合反映了这些因素的影响关系，在一定程度上从岩层移动机理揭示了开采沉陷的层面效应和尺寸效应，为建立不同级别倾角煤层开采沉陷的统一模型打下了基础。     

隧道开挖引起地表下沉及其影响分析

针对地下隧道开挖引起的地表下沉问题,运用概率统计理论建立了数学模型,并根据BP神经网络理论,通过对BP神经网络算法的改进,采用反分析方法确定岩体移动变形参数。利用所建模型对隧道开挖引起的地表垂直移动(下沉)进行了具体的计算分析,将理论计算值与实测下沉值进行对比,二者十分吻合。对比结果表明,所给出的数学分析模型及参数确定方法符合工程实际,为解决地下隧道开挖引起地表下沉预计分析问题开辟了新的途径。     

盾构隧道变位复变函数解的验证与参数分析

针对不排水黏土浅埋盾构隧道的一种(均匀)收敛-椭(卵)变(形)-(刚体)下沉复合体积损失模型,分别引入椭变系数与下沉系数,采用改进的复变函数求解方法,求解对应的地层变位和地层应力,并分析复合体积损失模型参数的影响。参数分析表明:(1)随着隧道下沉系数或椭变系数的增大,地表沉降值增大,沉降槽宽度也增大,但增加的量值不大;(2)隧道椭变系数主要会影响地层水平变位的大小及方向,而隧道下沉降系数则主要影响地层水平变位分布曲线的曲率;(3)最大环向应力出现在开挖边界的顶部,主因是隧道的下沉,次因是隧道的椭变。本文方法可以计算分析粘土浅埋盾构隧道开挖引起的地层变位以及应力分布。     

某厂房项目基坑支护结构的地表沉降破坏分析

在破坏力学中,土壤破坏可以认为是将未扰动的土壤不断转化为破坏的土壤的过程。为进一步准确计算深基坑中桩锚支护结构的地表沉降,本研究考虑深基坑开挖过程中土体破坏的发生,建立土体破坏模型和破坏演化方程。且使用有限元分析软件来计算现场情况下的地表沉降,并将数值结果与现场监测数据进行比较。结果表明,土壤破坏在一定程度上影响了基坑开挖的表层沉降分布。随着土壤破坏的发展,土壤的力学性能逐渐下降,导致地表沉降增加和开挖坑的方向发生变化。且计算方法、PLAXIS和监测结果得出的最大沉降点距离基坑边缘分别为10、14.8和11m,距离基坑边缘1m处的沉降值分别为0.01、2.10和6.00mm。使用提出的计算方法得到的地表下沉范围约为0～32m,而PLAXIS模拟的地表下沉范围约为0～34m。本文提出的地表沉降计算方法更接近监测结果,表明本文所提出的地表沉降计算方法适用于实际施工地表沉降监测。     

最近邻抽样回归模型在纵向地表沉降预测中的应用

针对传统方法在盾构法施工地表沉降预测中过于形式化和数学化的缺点,本文引入最近邻抽样回归模型(NNBR)。将盾构法施工引起地表沉降的因素分为三类:结构类、地质类和施工参数类,结合NNBR模型的计算特点对不同种类的因素采取不同的处理措施:结构类因素视为常量;按照能否形成免压拱将地质土层分为两类,计算距离系数时同类地层中的地质类因素视为常量;施工参数类因素的影响近似用隧道埋深和盾构平均掘进速度来衡量。由于NNBR模型对纵向地表沉降进行预测时可忽略常量因素,从而大大简化诸多次要因素对预测结果的影响,最终可以隧道埋深和盾构平均掘进速度为预测因子来预测纵向地表沉降。结合工程实例,证明预测因子选择的合理性以及NNBR模型在纵向地表沉降预测中的实用性。     

浅层地下水位预测的小波网络模型

针对浅层地下水位时间序列动态变化的非线性和复杂性 ,提出了基于小波分析与人工神经网络相结合的预测方法———小波网络模型。小波网络模型吸取了小波分析的多分辨功能和人工神经网络的非线性逼近能力。实例计算结果表明 ,建议模型不同预见期的拟合和检验精度很高。小波网络模型延长了预见期 ,提高了预报精度。     

基于ANN的岩土体热阻系数预测模型研究

为准确预测土体热阻系数,通过室内热探针测试与数据分析,简要分析了含水量、干密度、矿物成分和颗粒形态等因素对土体热传导特性的影响,利用人工神经网络(ANN)技术,建立了计算土体热阻系数的预测模型,并与传统经验关系模型进行对比,明确所提计算模型的可靠性与优越性.结果表明:土体传热性能受众多因素影响,其热阻系数难以准确估算,基于ANN的计算模型可以较好地解决这一问题;以含水量和干密度为输入参数的单个模型适用于特定类型土体,而4个输入参数(含水量、干密度、黏粒含量和石英含量)的广义模型不受此限制,增加相关输入参数可有效保证模型计算结果的精确度;单个模型和广义模型的计算结果与实测结果吻合良好,预测能力均显著优于传统经验关系模型;对于工程性质差异显著、沉积环境复杂的不同类型土体,建议优先选用广义模型来估算其热阻系数.     

基于改进BP网络的盾构隧道开挖地表沉降预测

盾构隧道施工中引起的地表沉降是衡量开挖方式是否合适的关键指标。文中在介绍BP神经网络及盾构施工引起变形情况的基础上,对基于BP神经网络的盾构隧道开挖引起的地表沉降预测进行了研究,考虑了训练样本中奇异数据的剔除,采用变步长的方法,并选取适当的动量项系数,综合考虑各种影响因素,建立了盾构隧道开挖引起的地表沉降预测的BP网络模型,并对广州地铁二号线进行了具体的预测分析。分析结果表明:理论计算结果与工程实际情况一致,误差小于5%,所建立的预测模型是令人满意的。     

临近基坑建筑物沉降神经网络动态预报

将BP神经网络与遗传算法结合,建立了建筑物沉降的动态预报模型。实例分析表明所建立的模型预测精度较高,预报值与实测值吻合较好,该方法对建筑物沉降的实时预报有一定的实用性。     

开采影响下建筑物损害程度的人工神经网络预测模型

在综合分析开采影响下建筑物损坏程度影响因素的基础上,采用自适应BP神经网络技术建立了建筑物采动损坏程度的预测模型。以大量的建筑物采动损坏实例作为学习训练样本和测试样本,对模型预测结果与实际值进行了对比分析。结果表明,用人工神经网络方法预测建筑物采动损害程度是可行的。为开采影响下建筑物损坏程度预测和评价探索出了一种新的方法。     

基于BP神经网络改进算法在地铁隧道施工中的沉降预测

为使地铁隧道在施工中沉降监测数据具有一定的预见性,分别采用了BP神经网络改进算法的预测模型、传统BP神经网络预模型以及基于时间序列的三次指数平滑法预测模型对地铁隧道施工中的沉降监测数据进行了预测。对其预测结果进行分析,得出了BP神经网络改进算法模型预测精度优于传统BP神经网络模型以及基于时间序列的三次指数平滑法模型预测精度的结论。     

Artificial neural network for forecasting rainfall pattern in Prince Edward Island,Canada

The forecast performance of alternative artificial neural network (ANN) models was studied to compare their forecast accuracy to the fractionally integrated autoregressive moving average model using monthly rainfall data for the Canadian province of Prince Edward Island (PEI). A multilayer feed-forward back-propagation ANN algorithm is implemented to evaluate the forecast accuracy and to analyse the statistical characteristics of the ANN model for original data and for data pre-processed with moving average and exponential smoothing transformations. The prediction performance of these models is compared to that of a seasonal autoregressive fractionally integrated moving average time series model. The statistical results show that the ANN model with exponential smoothing of the data has the smallest root mean square error and the highest correlation coefficient and thus, outperforms the alternative models investigated in this study.     

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.     

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge.     

人工神经网络在油漆废水混凝氧化处理建模中的应用

利用改进的B－P算法，对油漆废水混凝氧化处理系统建立了人工神经网络模型，并利用该模型拟合、预测了一些实验数据。结果表明，模型的计算值与实测数据之间的误差很小，而且能正确反映各影响因素作用的内部机理。     

基于神经网络的区域地壳稳定性评价

在重大工程的选址中,由于多种影响因素的控制和复杂的非线性相关关系,如何建立统一的数学模型,充分利用已有地质资料,对区域地壳稳定性做出早期的准确评价一直是一个难题。基于人工神经网络方法,建立了区域地壳稳定性评价的数学模型,并介绍了计算方法。以沂沭断裂带区域地壳稳定性定量评价为例,介绍了模型结构和关联系数矩阵的确定方法,在模型中,考虑地形地貌、含水层岩性、地下水活动、地质构造、地震活动等多种因素的影响,对区域地壳稳定性进行了定量评价。同时,选取多种运算参数,分析了模型参数对运算的影响。依据计算结果,对研究区的铁路选线和今后的研究工作提出了几点认识和建议。     

预报模型在GPS测沉降中的应用

变形监测一定程度上可以预测沉降发展趋势,本文采用灰色模型和神经网络的建模理论,介绍了GPS测沉降数据的预报处理流程,并利用上海市CORS网的数据分析GPS沉降预报,运用模型对GPS测沉降数据进行预测,最后利用中误差理论对精度进行评定,得出了灰色模型和神经网络模型在GPS沉降数据预报方面精度是可靠、精确的结论,从本文数据看神经网络预测的精度比灰色模型预测精度更符合实际。     

Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers

In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.