三峡库首区某滑坡变形特征及影响因素分析

针对库区地质灾害的特点,以三峡库首区秭归县某滑坡为例,基于近年来GPS位移监测、库区降雨与水位变化资料,分析了滑坡的变形特征,并研究了降雨与库水位变化因素对滑坡变形的影响.结果表明,该滑坡变形区呈推动式变形特征,其水平累积位移曲线具用期性台阶状位移特征,且受降雨和库水位变化影响较大,两者联合作用影响更大.     

三峡库区白家包滑坡变形特征及位移预测分析

为降低白家包滑坡可能造成的危害,收集并分析白家包滑坡自2006年以来的专业监测数据及变形机制,确定降雨和库水位等外界因素是导致滑坡体变形的主要因素。为预测白家包滑坡在外界诱因下的位移发展变化趋势,采用灰色关联法计算滑坡位移与降雨量、库水位升降的关联性,利用BP神经网络算法预测白家包滑坡累计位移,并对比分析了实测位移与预测位移的差异。结果表明,库水位下降和降雨是诱发白家包滑坡变形的主要因素;BP神经网络算法预测效果较好,白家包滑坡在2014年10月~2016年10月的累计位移预测曲线呈台阶状变化,ZG324、ZG326测点的最大位移达到1 031、1 209mm。     

基于小波分析和灰色BP神经网络的滑坡位移预测

滑坡位移时间序列预测对滑坡灾害预警和防治具有重要意义。滑坡位移时间序列具有高度的非线性特征,含有大量噪音且采用常规非线性模型难以准确预测。对此,提出基于小波分析(WA)—灰色BP神经网络的滑坡位移预测模型。该模型先采用小波分析法将滑坡位移时间序列分解为不同频率分量的滑坡子位移,然后采用灰色BP神经网络对各滑坡子位移进行预测,在此基础上将预测得到的各子位移值相加,最终得到预测出的滑坡位移值。以GPS监测获得的郑家大沟滑坡#1监测点的位移时间序列为例,采用WA-灰色BP神经网络模型对其位移进行预测,并与WA-BP神经网络模型及未进行小波分析的单独灰色BP神经网络模型进行对比分析。结果表明,WA-灰色BP神经网络模型准确预测出郑家大沟滑坡#1监测点的位移值,且具有比WA-BP神经网络模型和单独灰色BP神经网络模型更高的预测精度。     

基于小波神经网络的福利院滑坡深部位移预测研究

针对滑坡位移监测较为复杂的问题,基于福利院滑坡处水文地质工程地质条件,将BP小波神经网络预测模型引入滑坡变形监测预报中,预测了福利院滑坡变形趋势,并与BP神经网络的预测结果做了比较.结果表明,BP小波神经网络预测结果明显优于Bp神经网络,且训练次数大幅减少、自适应能力强、预测精度高.     

库水位变化条件下堆积体滑坡变形特征及稳定性分析

为研究库水位变化条件下堆积体滑坡的变形特征及稳定性,以三峡库区某典型堆积体滑坡为例,结合已有的监测资料,分析了变形与时间、变形与水位之间的变化规律,并采用FLAC二维数值模拟方法,建立了数值模型,分析了库水位变化条件下滑坡内的应力应变情况。结果表明,库区回水对滑坡的变形和稳定性影响巨大;二维数值计算的变形位移场与现场监测的位移基本吻合,说明数值计算可以较好地模拟库水位变化条件下的堆积体应力应变情况。     

降雨条件下堆积层滑坡位移响应试验及预测

堆积层滑坡变形演化与降雨关系密切,为研究降雨条件下堆积层滑坡变形演化规律及发展趋势,开展了周期降雨条件下堆积层滑坡模型试验,深入分析堆积层滑坡位移变化特征及其与降雨之间的响应关系,并以此为依据,运用位移时间序列进化-支持向量机方法,建立降雨因子与位移时序趋势项之间的学习预测模型,并通过Matlab编程求解。结果表明,在周期间断性降雨作用下,堆积层滑坡位移响应具有显著的跃阶性和滞后性,建立的周期降雨影响因子与周期项位移之间的预测模型是必要且有效的,模型能产生较符合实际的位移响应趋势。研究成果可为降雨条件下堆积层滑坡位移预测提供参考。     

开挖条件下大礼溪滑坡变形机理分析

为分析开挖条件下大礼溪滑坡变形机理,根据现场调查,并结合滑坡变形特征,运用定性评价方法分析了滑坡的变形发展过程及其机理;发现软弱易风化的粉砂质泥岩坡体、不利的层状同向坡体结构及发育的节理构造是滑坡发生的内因;人工开挖与爆破引发的地震是引发滑坡的外因,降雨作用加速了滑坡变形。最后,通过数值模拟分析开挖条件下滑坡的应力场与位移场的变化,发现滑坡的应力调整与位移变化均具有渐进式变化的特点,分布在滑坡前缘、中部和后缘观察点最小总应力的平均值分别为18.347、-21.888、-76.450kPa;位移平均值分别为147.83、111.14、39.29mm。研究成果对防治滑坡灾害有重要意义。     

三峡水库蓄水条件下树坪滑坡变形特征及稳定性分析

为了解三峡水库蓄水对树坪滑坡的影响,基于监测资料分析了滑坡位移随水库蓄水变化的规律,采用有限元分析软件Geo-studio分析了各蓄水阶段树坪滑坡的渗流场、位移场和稳定性变化特征。结果表明,水库蓄水过程可分为125~139~135m、135~156~145m和145~175~145m三个阶段,每个蓄水阶段对树坪滑坡的影响程度不同;库水位上升时滑体内地下水浸润线呈内凹趋势,库水位下降时滑体内地下水浸润线呈外凸趋势,且凹凸程度与运行水位差正相关;第一阶段蓄水过程主要引起树坪滑坡前缘局部变形,第二阶段蓄水过程中滑坡前缘变形量增大且变形逐步向滑坡中部发展,第三阶段蓄水过程中滑坡前缘及中部变形量明显增大,变形逐渐向后发展。最后根据树坪滑坡变形机理,提出了防治措施。研究成果可为三峡库区管理提供理论依据。     

高烈度区水库堆积层滑坡地震动力响应及稳定性分析

以高烈度区水库堆积层滑坡为研究对象,基于Geo-studio软件动力有限元法,分析了三类凸形堆积层滑坡在高低库水位运行条件下的地震动力响应特征和稳定性变化。结果表明,鞭梢效应对参量的影响程度大于竖向放大效应对参量的影响,对中凸形滑坡的加速度响应比上凸形与下凸形滑坡要大,上凸形滑坡的阶段位移响应峰值大于中、下凸形滑坡阶段位移峰值;凸形堆积层滑坡在高水位时对地震的响应要大,在所列库水位和0.05g地震动力条件下整体稳定性系数最大减小约0.037%,下凸形滑坡受此影响较大,且随库水位高度增加,地震加速度增加,整体稳定性系数减小程度增大。该分析结果对高烈度区水库堆积层滑坡灾害防治具有重要参考价值。     

汶川地震诱发干河口巨型反倾滑坡成因机制研究

以汶川地震诱发的干河口巨型滑坡为例,基于现场调查,通过调研滑坡结构和滑动后揭露的地貌特征,研究分析了滑坡的成因机理、破坏过程及运动特性,为同类灾害预测提供了参考。     

Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique

In this work, the experiments of the transesterification process were carried out on jatropha-algae oil blend and the prediction of the synthesized biodiesel was investigated. The study was divided into two parts. In the first part, a series of experiments were employed practically and in the second part, the prediction is made with the artificial neural network (ANN). The ANN with Levenberg–Marquardt (LM) algorithm was trained with topology 4–10-1. The estimated results were compared with the experimental results. An ANN model was developed based on a back-propagation learning algorithm. An R-square value of the model from ANN was 0.9976. The results confirmed that the use of an ANN technique is quite suitable. The artificial neural network gave acceptable results.     

A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm.     

Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks

This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.     

Prediction of heat transfer due to presence of copper–water nanofluid using resilient-propagation neural network

Heat transfer due to laminar natural convection of copper–water nanofluid in a differentially heated square cavity has been predicted by Artificial Neural Network (ANN). The nanofluid has been considered as non-Newtonian. The ANN has been trained by a resilient-propagation (RPROP) algorithm. The required input and output data to train the ANN has been taken from the results of numerical simulation that was performed simultaneously where the transport equations has been solved numerically using finite volume approach incorporating SIMPLER algorithm. Results from simulation and resilient-propagation (RPROP) based ANN have been compared. It has been observed that the ANN predicts the heat transfer correctly within the given range of training data. It is further observed that resilient-propagation (RPROP) based ANN is an efficient tool to predict the heat transfer than simulation, which takes much longer time to compute.     

Use of artificial neural networks for transport energy demand modeling

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem.     

Development of a neural network based saturation model forsynchronous generator analysis

This paper presents a new approach to model synchronous generator saturation based on a feedforward artificial neural network (ANN) model. The machine loading conditions, excitation levels and rotor positions are all included in the modeling process. The nonlinear saturation characteristics of a three-phase salient-pole synchronous machine rated at 5 kVA and 240 V is studied using the ANN model. An appropriate selection of input/output pattern for the ANN model training based on an error back-propagation scheme is developed using the on-line small-disturbance responses and the well-known maximum-likelihood estimation algorithm. The developed ANN model is implemented in the generator dynamic transient stability study requiring only small computational alteration in saturation model representation     

Prediction of Heat Transfer Rates for Shell-and-Tube Heat Exchangers by Artificial Neural Networks Approach

This work used artificial neural network(ANN)to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles,based on limited experimental data.The BackPropagation (BP) algorithm was used in training the networks.Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2%.Comparison withcorrelation for prediction shows ANN superiority.It is recommended that ANN can be easily used to predict theperformances of thermal systems in engineering applications,especially to model heat exchangers for heattransfer analysis.     

Artificial-Neural-Network-Based Sensorless Nonlinear Control of Induction Motors

In this paper, two architectures of artificial neural networks (ANNs) are developed and used to correct the performance of sensorless nonlinear control of induction motor systems. Feedforward multilayer perception, an Elman recurrent ANN, and a two-layer feedforward ANN is used in the control process. The method is based on the use of ANN to get an appropriate correction for improving the estimated speed. Simulation and experimental results were carried out for the proposed control system. An induction motor fed by voltage source inverter was used in the experimental system. A digital signal processor and field-programmable gate arrays were used to implement the control algorithm.     

A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive

Artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm makes use of the advantages of ANNs such as noise rejection capability and not requiring any prior knowledge of the physical parameters relating to PV system. This paper proposes a genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive. The major objective of this design is to eliminate dc–dc converter and its accompanying losses. Implementing off-line ANN in DSP needs optimization of ANN structure to obtain an ideal size. GA optimization was used in this study to determine neuron numbers in multi-layer perceptron neural network. Another objective of this work is to prevent the necessity of the trade-off between the tracking speed and the oscillations around the maximum power point. Hence, varying step size is used in MPPT algorithm and PI-controller is adopted for simple implementation. Simulation and experimental results have been used to demonstrate effectiveness of the proposed method.     

Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks

Artificial neural network (ANN) is applied for exergy analysis of a direct expansion solar‐assisted heat pump (DXSAHP) in the present study. The experiments were conducted in a DXSAHP under the meteorological conditions of Calicut city in India. An ANN model was developed based on backpropagation learning algorithm for predicting the exergy destruction and exergy efficiency of each component of the system at different ambient conditions (ambient temperature and solar intensity). The experimental data acquired are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the use of an ANN analysis for the exergy evolution of DXSAHP is quite suitable. Copyright © 2009 John Wiley & Sons, Ltd.