基于RBF神经网络的软基短期沉降预测研究

RBF(radial basis function)神经网络是一类比较优越的前向式多层神经网络,比传统的BP网络有较快的收敛速度.以深圳湾西部通道填海软基沉降的预测分析为例,探讨采用RBF神经网络解决这一问题的方法.采用插值方法构建时间间隔统一的时间序列数据并进行归一化处理,在此基础上建立了沉降变形时间序列的RBF神经网络模型,通过训练网络模型来预测沉降量.计算实例表明,模型具有运算速度快、预测精度高的特点,是一种具有应用前景的软基预测新方法.     

神经网络技术在路堤沉降预测中应用探讨

针对传统的BP网络模型的不足，应用了改进的BP神经网络模型，把它应用到软基高速公路的路堤沉降预测中，提出了两种构造神经网络训练样本的思路，并分别进行了计算和对比，指出了各自的优、缺点。结果表明改进的BP网络模型比较稳健、收敛快，而且根据时间与对应的沉降量形成的样本训练的网络预测出的工后沉降误差小、精度高。     

Artificial Neural Network Prediction Models for Soil Compaction and Permeability

This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD) and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions for using these models are discussed in detail in this paper.     

基于PSO-BP神经网络的基坑周边地面沉降预测方法研究

基坑工程施工过程中的周边地面沉降直接关系到周围建筑物的安全,本文根据上海前滩地区某基坑工程的历史监测数据、施工工况和周边地层参数等多源数据对基坑周边地面沉降进行监测和预测。以PSO-BP神经网络为基础,通过将基于时序和基于沉降影响因素的网络模型对比发现：二者预测结果误差较小且基于时序的神经网络预测精度更高,说明利用PSO-BP神经网络能够很好地对基坑周边地面沉降进行分析与预测。为了综合考虑时间效应和空间效应的影响,在基于沉降影响因素的预测模型的基础上加入历史监测数据作为模型输入层进行优化,结果表明：优化后的PSO-BP神经网络模型具有更小的相对误差范围和更高的预测精度,在基坑周边地面沉降预测中有很好的应用前景。     

Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Soil liquefaction as a transformation of granular material from solid to liquid state is a type of ground failure commonly associated with moderate to large earthquakes and refers to the loss of strength in saturated, cohesionless soils due to the build-up of pore water pressures and reduction of the effective stress during dynamic loading. In this paper, assessment and prediction of liquefaction potential of soils subjected to earthquake using two different artificial neural network models based on mechanical and geotechnical related parameters (model A) and earthquake related parameters (model B) have been proposed. In model A the depth, unit weight, SPT-N value, shear wave velocity, soil type and fine contents and in model B the depth, stress reduction factor, cyclic stress ratio, cyclic resistance ratio, pore pressure, total and effective vertical stress were considered as network inputs. Among the numerous tested models, the 6-4-4-2-1 structure correspond to model A and 7-5-4-6-1 for model B due to minimum network root mean square errors were selected as optimized network architecture models in this study. The performance of the network models were controlled approved and evaluated using several statistical criteria, regression analysis as well as detailed comparison with known accepted procedures. The results represented that the model A satisfied almost all the employed criteria and showed better performance than model B. The sensitivity analysis in this study showed that depth, shear wave velocity and SPT-N value for model A and cyclic resistance ratio, cyclic stress ratio and effective vertical stress for model B are the three most effective parameters on liquefaction potential analysis. Moreover, the calculated absolute error for model A represented better performance than model B. The reasonable agreement of network output in comparison with the results from previously accepted methods indicate satisfactory network performance for prediction of liquefaction potential analysis.     

软基沉降的BP神经网络和灰色系统联合预测

使用BP神经网络插值方法对灰色数据进行了预处理,进而建立了预测软基沉降量的BP神经网络和灰色系统联合模型.实例分析表明,该模型短期沉降预测结果的最大相对误差小于2%,最终沉降预测结果的相对偏差小于5%,且灰色预测时取后期沉降瘦导颇算结果准确度高于取前期沉降数据的计算结果准确度.     

盾构施工地面长期沉降的神经网络预测

基于逆传播人工神经网络方法，建立了盾构施工地面长期沉降的非线性预测模型，建立了沉降与诸多影响因素：所处位置、时间、上覆土性参数及盾构施工参数等的关系模型。通过在上海地铁2号线龙东路一中央公园站区间资料的验证，发现与实际比较吻合。     

应用遗传-神经网络方法预测软土路基沉降

理论方法预测软土地基沉降与实际存在较大的差距,使得预测结果很难达到设计要求,不利于指导施工。将现有的理论方法同现场观测信息相结合,对软土地基变形作出更为准确的预测,有利于指导和控制工程施工。采用遗传算法和BP最优化法相结合的算法来训练网络,用遗传算法来优化BP神经网络中权值;用龚帕斯曲线来分解沉降时序,通过沉降趋势线偏移量来训练网络。采用这种方法预测软土路基沉降取得了较好的应用效果。     

Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model

Accurate prediction of ore grade is essential for many basic mine operations, including mine planning and design, pit optimization, and ore grade control. Preference is given to the neural network over other interpolation techniques for ore grade estimation because of its ability to learn any linear or non-linear relationship between inputs and outputs. In many cases, ensembles of neural networks have been shown, both theoretically and empirically, to outperform a single network. The performance of an ensemble model largely depends on the accuracy and diversity of member networks. In this study, techniques of a genetic algorithm (GA) and k-means clustering are used for the ensemble neural network modeling of a lead–zinc deposit. Two types of ensemble neural network modeling are investigated, a resampling-based neural ensemble and a parameter-based neural ensemble. The k-means clustering is used for selecting diversified ensemble members. The GA is used for improving accuracy by calculating ensemble weights. Results are compared with average ensemble, weighted ensemble, best individual networks, and ordinary kriging models. It is observed that the developed method works fairly well for predicting zinc grades, but shows no significant improvement in predicting lead grades. It is also observed that, while a resampling-based neural ensemble model performs better than the parameter-based neural ensemble model for predicting lead grades, the parameter-based ensemble model performs better for predicting zinc grades.     

基于多变量LSTM神经网络的地下水水位预测

为解决以往模型未考虑地下水位相关影响因素的问题,探讨长短期记忆（LSTM）神经网络在地下水位预测中的应用,利用长短期记忆神经网络,采用多变量输入的方式,构建了基于多变量LSTM神经网络的地下水水位预测模型。以泰安市岱岳区J1号监测井为例,采用2001-2014年地下水水位动态监测资料与相关影响因素数据,利用多变量LSTM神经网络对2015-2016年地下水位进行预测,并与单变量LSTM神经网络和反向传播（BP）神经网络进行对比。研究结果表明：以相关影响变量为输入的BP神经网络无法考虑时序变化规律,预测均方根误差最大,为2.399 3;以地下水位为变量输入的单变量LSTM神经网络仅能根据时序变化作出相应预测,无法考虑相关变量影响,预测均方根误差为2.102 2;基于多变量输入的LSTM神经网络的预测精度显著高于单变量LSTM神经网络和BP神经网络,预测均方根误差最小,仅为1.919 1。总体上,多变量LSTM神经网络地下水位预测模型仅在某些峰值处误差较大,但总体预测效果较为理想。     

Artificial Neural Network as a Tool for Backbreak Prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R² = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively.     

Development of a Neural Network for Earth Surface Deformation Prediction

Coal industry is one of the largest parts of the world fuel and energy complex, and 36% of the world coal reserves are extracted by underground mining. The process of undermining leads to Earth surface deformations, and it can cause damage and destruction of buildings and structures. Therefore, it is necessary to monitor undermined objects with certain frequency and predict deformations of the territory. The article describes the solution of the prediction problem with the help of neural network technology. The creation of a neural network includes several steps, described in the article in detail: choice of network architecture, preparation and normalization of input data, development of a mathematical model for network calculation, training and testing of the network. Training and testing were done based on the materials of the gas pipeline undermining project. The developed tool allows predicting the Z coordinate for profile line benchmarks for any day of displacement based on the data of instrumental observations.     

基于Stacking模型融合的深基坑地面沉降预测

为了提高机器学习对深基坑地面沉降的预测能力,本文提出了一种基于Stacking集成学习方式的多模型融合的地面沉降预测方法,并以深圳某深基坑为例,采用斯皮尔曼相关性系数对基坑地面沉降的影响因子进行筛选;运用筛选后的8个影响因子建立Stacking深基坑地面沉降预测模型,以验证该方法的适用性。结果表明：Stacking预测模型的平均绝对误差为0.34、平均绝对误差百分比为2.22%,均方根误差为0.13,相较于传统基模型（随机森林、支持向量机和人工神经网络）,Stacking预测模型的平均绝对误差、平均绝对误差百分比和均方根误差值皆为最小。     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

SOM-RBF神经网络模型在地下水位预测中的应用

利用自组织映射(SOM)聚类模型优化径向基函数神经网络(RBFN)隐层节点的方法,减小了RBFN由于自身结构问题在地下水水位预测中产生的误差。采用SOM对已有样本进行聚类,利用聚类后的二维分布图确定隐层节点的数目,并根据聚类结果计算径向基函数的宽度,确定径向基函数的中心,由此建立SOM-RBFN模型。以吉林市丰满区二道乡为例,采用2000—2009年观测的地下水位动态资料,利用SOM-RBFN模型对地下水位进行预测,验证其准确性,并分别以5、7、10a的地下水位动态数据为研究样本建立模型,考查样本数量对预测结果的影响。研究结果表明:SOM-RBFN模型预测地下水水位过程中,均方根误差(RMSE)的均值为0.43,有效系数(CE)的均值为0.52,均达到较高标准,因此SOM-RBFN模型可以作为有效而准确的地下水水位预测方法;同时RBF7的RMSE和CE均值分别为0.38和0.68,结果优于RBF5和RBF10,这就意味着在模型计算中样本数量不会直接影响预测结果的精度。     

基于半监督神经网络的铜矿预测方法

将人工智能技术引入成矿预测研究中,可以提高预测效率,挖掘探测数据与结果之间的隐藏信息.利用半监督学习方法对样本构建要求低的优点,结合其在异常识别方面的应用效果,设计了基于分割准则的孤立森林与深度自编码网络的神经网络结构;基于西藏冈底斯地区的化探元素数据,对研究区内的铜矿进行了成矿预测工作,预测结果与已知矿区数据叠加效果较好,说明本文的神经网络结构能够完成成矿远景区的预测工作.      

论地下水超采与地面沉降

地面沉降是地面高程缓慢降低的环境地质现象,严重时就会演变成一种地质灾害。造成地面沉降的原因有天然和人为因素,常见的地面沉降绝大部分属于人为因素。由于地下水过量开采,从而造成地面沉降变形、出现裂缝、导致地面建筑物破坏等,影响人们的正常生活,危害巨大。控制地面沉降主要因素在于加强地下水资源的管理,科学合理开发利用,建立区域地面沉降模型,预测不同地下水开采方案和开采量所可能带来的沉降量,优化最佳开采量,有效防控地下水超采引起的地面沉降。     

人工神经网络方法在基坑变形预测中的应用研究

分析研究了人工神经网络方法在基坑变形预测中的建模方法,并通过实例应用,证明这种方法是切实可行的.同时将人工神经网络方法预测结果和灰色系统模型及时序模型预测进行比较,充分证明人工神经网络方法在变形预测中的优越性.     

地下水动态预测的径向基函数法

地下水系统是一个复杂的随机系统，根据地下水位与其影响因素之间存在的映射关系，建立了一个RBF人工神经网络模型，并将其用于地下水位的动态预测，实例表明，该方法预测精度较高，具有一定的推广价值。     

改进的BP神经网络在地震砂土液化预测中的应用

地震砂土液化的影响因素具有非线性关系,至今没有形成规范的预测标准。人工神经网络在砂土液化预测中有较好的应用,尤其是BP神经网络,但由于其本身存在缺陷:学习收敛速度慢,易陷入局部极小;遗传算法具有良好的搜索全局最优解的能力。探讨利用遗传算法优化BP神经网络权值和初始阈值来预测地震砂土液化,其效果比传统的BP网络有显著提高。