岩土非线性流变本构模型的辨识算法研究

从岩土非线性流变本构模型通式——P.Perzyna方程出发,研制了岩土工程围岩流变本构模型辨识用有限元计算程序EVP2D,并将其和均匀设计(UD)法结合,获得了用于ANN辨识模型训练用的具有丰富本构信息的全局性输入输出有效数据。尔后,设计了基于实际监测位移数据辨识围岩流变本构模型参数的ANN模型,并在matlab软件平台中研制了辨识用相关程序CYJBS3.M。有关实例验证,从待辨识参数向量UD设计、训练数据的有限元计算获取、ANN模型的训练和辨识这一整体计算过程的实现,表明了UD-FEM-ANN本构模型辨识方法的可行性。      

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

Analytic network process model for landslide hazard zonation

Various controlling factors such as lithology, slope angle, slope aspect, landuse, channel proximity etc. are generally considered for the landslide hazard assessment. Although outer dependence of these parameters to a landslide is inevitably taken into account, inter-dependence among the factors is seldom addressed. Analytic Network Process (ANP) is the multi-criteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this research, an ANP model for landslide susceptibility is proposed, priority weights for each parameter controlling the landslide were determined, and a hazard map was prepared of an area in a fragile mountainous terrain in the eastern part of Nepal. The data used in the example were derived from published sources, aerial photographs and a topographic map. However, the procedures developed can readily incorporate additional information from more detailed investigations.     

水库群优化调度函数的人工神经网络方法研究

提出了研究水库群优化调度函数的人工神经网络方法,并探讨了神经网络的训练参数、训练方法和训练样本的改变对网络训练和应用效果的影响。实例研究表明,模型及其算法是可行的、有效的。     

Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks

An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2, there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions. Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems.     

数字高程模型分辨率对流域地形特征参数的影响

地形特征(如高程和坡度)和水文特征(如河流长度和河流坡度)是分布式流域水文水质模型的基础输入参数,用于量化描述模型模拟流域的自然特征。这些特征参数的准确性直接影响水文水质过程模拟的准确性。应用数字高程模型(Digital Elevation Model,DEM)在4个不同地形的子流域研究了10种不同分辨率DEM对平均高程、流域面积、坡度、河流坡度、最长河长等参数的影响。结果表明,随着DEM分辨率降低,流域地形变缓,流域平均坡度逐渐减小;随着DEM网格分辨率的变化,子流域划分范围和河道位置也都可能发生变化,且该变化在地形起伏较小的丘陵平原地区较明显,子流域集水面积和河长进一步随之改变;河流坡度随DEM分辨率降低则呈无规则变化。从地形和水文参数两方面揭示了DEM 分辨率在分布式流域模型中的不确定性影响。     

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.     

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd.     

Advanced Mathematical Models to Predict the Compaction Properties of Coarse-Grained Soils from Various Physical Properties

An essential task in the process of construction is the determination of compaction properties of soils. Many years of laboratory test experience strengthen our belief in the existence of predictive equations that govern the compaction characteristics of soils. An advanced mathematical model developed in this research in order to uncertain the governing equations. An advanced mathematical model developed in this research in order to uncertain the governing equations. Through a comparative study among a Multiple Linear Regression (MLR) model, an Artificial Neural Network (ANN) model, Extreme Learning Machine (ELM) and a Support Vector Machine (SVM) model, the best predicting model was determined. For this purpose, Six hundred and six (606) samples collected and split into a dataset used for training the models and another used for validation of the derived model. 8 neural networks with a varying number of hidden layers and a varying number of nodes in hidden layers were employed. In ELM 1 hidden layer with varying number of units were employed. It was found that the equations derived from the ELM models described the relationship with superiority over multiple regression, ANN and SVM models for Maximum Dry Density and MLR models described the relationship with superiority over ANN, ELM and SVM models for Optimum Moisture Content.     

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.     

识别所钻地层的人工神经网络法应用

对用人工神经网络方法来解决钻探生产的实际问题, 在不取心的情况下识别所钻地层的岩性进行了研究.根据钻探生产的特点, 设计了人工神经网络的结构和输出方式, 开发了人工神经网络识别所钻地层的软件, 分析了影响人工神经网络应用效果的各因素, 在人工神经网络的优化设计方面作了较深入的研究.研究表明: 人工神经网络用于识别所钻地层有很好的效果; 人工神经网络的参数, 如学习率、隐含层层数、隐含层单元数和数据处理方式等对人工神经网络的应用效果有影响.      

A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey

This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R²). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions.     

融合地形和土壤特征的流域蓄水容量模型

以van Genuchten模型表述的土壤水分特征曲线为基础,推导出流域单点缺水量,并结合TOPMODEL模型中地形指数与地下水位关系,建立了反映地形和土壤特征共同影响的蓄水容量模型,通过统计方法从栅格尺度蓄水容量获得流域尺度蓄水容量曲线,取代传统新安江模型中率定的蓄水容量曲线。以淮河流域紫罗山子流域为例,分析地形特征与土壤类型对蓄水容量的影响;并与实测流量过程以及原新安江模型模拟的流量过程对比,表明模型能较好地模拟场次洪水过程。模型将蓄水容量曲线显式表述,减少了新安江模型参数,为无资料地区的水文模拟提供了分析方法。     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

南水北调中线工程水源区老鹳河流域农业非点源污染关键源区识别

识别关键源区可以为非点源污染的优先管理和控制提供决策依据。本次研究以南水北调中线工程水源区老鹳河流域为研究区,比较全面地考虑了氮磷流失的主要影响因素,建立了老鹳河流域农业非点源污染关键源区识别模型,进行流域农业非点源污染风险评价和污染关键源区识别。结果表明,流域内处于高风险区以上的地区占流域总面积的13.4%,主要分布于老鹳河中上游地区,相对集中分布于老鹳河干流及其支流沿河地区,零星分布于西峡境内东部和北部局部,为该流域地表水环境农业非点源污染的关键源区。其中,污染风险最高的区域只占流域总面积的3.75%,可划定为重点关键源区进行重点治理,同时应兼顾污染风险次之的区域。基于GIS的指标体系法能够快速而方便地识别流域非点源污染高风险区域并量化污染风险大小,从宏观上掌握非点源污染的空间分布特征并实施有效管理和治理。     

Application of an Expert System for the Assessment of Blast Vibration

The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.     

Hydrological modeling of typhoon-induced extreme storm runoffs from Shihmen watershed to reservoir, Taiwan

Typhoon-induced extreme storm runoffs often cause flood hazards. In this study, a hydrological model (HEC-HMS) was applied to Shihmen watershed located in Taiwan. Three typhoon-induced storm events, with return period ranging from 1 to 90 years, were used in case studies to characterize storm runoff. With a 5-year storm for model calibration, model parameters were carefully calibrated through the comparison between model simulated and observed flows at a stream gage station. The calibrated model was then verified for a 90-year storm and a 1-year storm event. Results indicate that the calibrated and verified HEC-HMS hydrological model is capable of providing satisfactory predictions of the typhoon-induced extreme storm runoff to support reservoir operation and flood hazard mitigation. Based on model simulations, typhoon-induced water table increases for different initial water volumes at Shihmen Reservoir was derived by adding storm-runoff volume to the reservoir’s initial elevation-volume rating curve. Water tables above the top elevation of the dam in the reservoir indicate the need for immediate water releases to avoid the risk of overflow over the dam.