Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.     

Slope stability analysis: a support vector machine approach

Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.     

油气储层裂缝定量描述及其地质意义

在裂缝型油气藏勘探开发过程中,裂缝发育区带、裂缝密度以及连通性是影响生产的关键参数。文章在详细综述储层裂缝定量描述的必要性、地质意义、分维描述、人工智能模糊描述及微观描述等定量描述法的基础上,介绍了裂缝系统各参数之间的关系及裂缝预测方法,进而介绍了定量描述法在裂缝预测中应用的几个实例。     

规划管理智能决策在城市固体废物治理中的应用

对城市固废治理系统规划管理是有效进行治理决策的根本方法,由此提出了GIS支持的城市固废规划管理智能决策方法。该方法用规划管理的思想将"城市固废治理系统"的决策划分为操作性、战术性、战略性三个层次。在此基础上,构建系统的总体规划。同时,将KBS与总体规划中的半结构化?非结构化模型相结合,提高决策的智能化水平;将GIS与模型整体集成,增强决策的直观性和全面性。给出了该方法的一般性决策模型及决策过程与主要创新,并以该方法为指导,初步建立了GIS支持的城市固废规划管理智能决策系统。     

从地理信息系统到智能地理系统

地理系统是自然与人文要素构成的,分布在地球表层空间中,具有生成、维持或转换物质形态和能量形式,或驱动物质和能量流动等功能的有机整体,表征为自然与人文要素及其相互作用的空间分布、结构、格局、演化和功能。地理系统分层级广泛存在于地球表层空间,是客观现实系统。地理信息系统将地理系统用数字投影到信息世界中,进而考察、分析和制定改造地理系统的方案。由此存在2个地理系统,即现实地理系统和信息地理系统,2个地理系统长期独立存在,各自演化。近些年,融合实时观测和物联网技术,现实地理系统的变化或演变已基本能够实时投影到信息地理系统中,即2个系统不再是独立演化,信息地理系统成为现实地理系统的镜像,现实地理系统一旦发生变化,信息地理系统同步变化。当前,人类科技正以前所未有的速度发展,特别是云服务、大数据、人工智能的异常迅猛发展,使得信息地理系统不再满足于成为现实地理系统的镜像,开始利用人工智能和无人自动控制技术,实现与现实世界的融合,即通过信息地理系统的操作,实现现实地理系统的改变。如此,未来现实地理系统与信息地理系统的界线日趋模糊,直至2个系统完全融合,不再区分现实地理系统和信息地理系统。本文暂且以智能地理系统指征这种融合2个世界的系统,并以南海岛礁的无人值守智能系统为例,展示现实地理系统与信息地理系统的融合与操作交互,实现岛礁地理环境和人工设施监测、保护、应对和操作的无人化和智能化。     

MIGFA阵风锋识别算法改进与检验

根据南京CINRAD/SA天气雷达探测的江苏沿江地区阵风锋回波特征,对MIGFA(Machine Intelligence Gust Front Algorithm)阵风锋识别算法进行改进:在考虑平滑算法使用和低仰角数据融合的基础上,根据阵风锋回波特征,改进了0.5°反射率阵风锋细线函数模板,设计了较高仰角(1.5°/2.4°)反射率阵风锋细线函数模板,引入1.5°和2.4°双层反射率阵风锋细线函数模板替代原空间差分反射率函数模板。考虑阵风锋特征与距离测站的关系,设计了动态权重函数组合多组得分值,从而有效识别阵风锋回波。在此基础上通过弧度判断和阵风锋回波平坦度测试的方式,进一步降低虚警率。最后利用2009年6月14日南京雷达阵风锋个例进行效果识别,并采用临界成功指数对南京雷达120个阵风锋样本进行效果评估。结果表明:改进的MIGFA法识别效果良好,将临界成功指数从0.39提高至0.60,引入降低虚警率的做法使得虚假警报率从0.34降至0.16。     

威海小石岛人工鱼礁海域渔获物群落结构特征及其与环境因子相关性

为探究人工鱼礁投放对威海小石岛附近海域渔获物种类组成、渔获量变化、渔获物结构特征的影响,作者于2012~2013年对威海小石岛人工鱼礁区及对照区的渔获物和环境因子进行了调查,并采用聚类分析、单因子相似性分析及相似性百分比分析对渔获物结构特征进行分析,采用典范对应分析(CCA)对礁区主要物种与环境因子的相关性进行分析。结果显示,南、北礁区地笼网调查到渔获物种类数量高于对照区,分别为24、31和18种。南、北礁区鱼类和甲壳类生物资源量高于对照区。南、北礁区全年资源量占优势的种类为日本蟳(Charybdis japonica)和大泷六线鱼(Hexagrammos otakii),对照区为海刺猬(Glyptocidaris crenularis)和大泷六线鱼。聚类分析和单因子相似性分析表明,渔获物组成结构在季节间差异显著(P0.05),在春季和夏季,人工鱼礁区渔获物组成结构和对照区差异显著(P0.05)。典范对应分析表明,水温、叶绿素a和溶解氧是影响礁区主要渔获物种类分布的主要环境因子。投放人工鱼礁改善了威海小石岛礁区水域的渔获物群落结构,并增加主要鱼类、甲壳类经济物种的渔获量。     

A Novel Control Algorithm for Interaction Between Surface Waves and A Permeable Floating Structure

An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment. In the design procedure of the controller, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis is carried out for a real structure system by using Lyapunov method. The corresponding boundary value problems are then incorporated into scattering and radiation problems. They are analytically solved, based on separation of variables, to obtain series solutions in terms of the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width, thickness and mass has been thus drawn with a parametric approach. From which mathematical models are applied for the wave-induced displacement of the surge motion. A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system, but has the robustness against external disturbance.     

基于CBR的智能赤潮预测预警系统研究

基于实例推理机制(CBR),综合运用人工神经网络、知识发现、模糊逻辑及赤潮生态动力学模型库,考虑了影响赤潮发生的因素的多样性与随机性,建立了一个基于实例推理的智能赤潮预测预警系统。