Classification of slopes and prediction of factor of safety using differential evolution neural networks

Slope stability analysis is one of the most important problems in geotechnical engineering. The development in slope stability analysis has followed the development in computational geotechnical engineering. This paper discusses the application of different recently developed artificial neural network models to slope stability analysis based on the actual slope failure database available in the literature. Different ANN models are developed to classify the slope as stable or unstable (failed) and to predict the factor of safety. The developed ANN model is found to be efficient compared with other methods like support vector machine and genetic programming available in literature. Prediction models are presented based on the developed ANN model parameters. Different sensitivity analyses are made to identify the important input parameters.     

土质边坡稳定性影响因素的研究

边坡稳定性涉及到诸多因素,引入人工神经网络预测边坡稳定性的方法--误差逆传播学习算法效果显著.边坡稳定性预测系统的输入信息包括岩土体参数、几何参数等,而输出信息则是网络预测的稳定系数和稳定状态.土质边坡主要以圆弧滑移破坏为主,通过人工神经网络预测的结果与实际监测结果的对比分析,证实了BP神经网络在评价土质边坡稳定性方面的效果显著;并在此基础上分析了土质边坡影响因素对边坡稳定性的影响程度.     

Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN)

The stability problem of natural slopes, filled slopes, and cut slopes are commonly encountered in Civil Engineering Projects. Predicting the slope stability is an everyday task for geotechnical engineers. In this paper, a study has been done to predict the factor of safety (FOS) of the slopes using multiple linear regression (MLR) and artificial neural network (ANN). A total of 200 cases with different geometric and shear strength parameters were analyzed by using the well-known slope stability methods like Fellenius method, Bishop’s method, Janbu method, and Morgenstern and Price method. The FOS values obtained by these slope stability methods were used to develop the prediction models using MLR and ANN. Further, a few case studies have been done along the Jorabat-Shillong Expressway (NH-40) in India, using the finite element method (FEM). The output values of FEM were compared with the developed prediction models to find the best prediction model and the results were discussed.     

Comparison of landslide susceptibility mapping methodologies for Koyulhisar,Turkey: conditional probability,logistic regression,artificial neural networks,and support vector machine

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.     

The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks

This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar (Giresun–Turkey). Digital elevation model was first constructed using a geographical information system software and parameter maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood.     

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R ²), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.     

龙滩水电站左岸边坡二维位移反分析

论述了考虑开挖卸荷效应的二维位移反分析方法。对龙滩水电站左岸岩质边坡1-1剖面进行地质概化、施工开挖分区和开挖扰动分区,建立有限元计算模型。根据试验和现场实测资料,确定待反演得参数和取值范围,通过二维弹塑性有限元方法与神经网络-遗传算法相结合反分析出岩体力学参数。基于该参数的正分析计算结果表明,计算位移与监测位移趋势一致,量值吻合。开挖面附近最大压应力约为1.0 MPa左右,开挖面附近基本上没有拉应力区。开挖完成后的塑性区主要分布在坡顶和开挖面及大断层附近,而且基本上发生在开挖损伤区和部分卸荷影响区。     

The application of artificial neural networks to simulate meltwater runoff of Keqikaer Glacier,south slope of Mt. Tuomuer,western China

Because glacial melting provides a significant amount of surface water resources, especially in cold arid regions, it is critical that effective methods be developed for predicting their behavior. Glacier runoff differs from other types of stream flows, being characterized by large diurnal fluctuations, with maximum discharge during the summer months. Moreover, the size and remoteness of glaciers makes them difficult to study directly. Hence, developing effective modeling techniques is our best hope for understanding and predicting glacial melting phenomena. In the past, physics-based models have been used with some success. In this study, conducted in 2003 and 2004 on the Keqikaer Glacier on the south slope of Mt. Tuomuer, however, we used the newer artificial neural networks (ANNs) modeling technique. As the input nerve cell, we used the hourly wind speed, precipitation, air temperature, radiation balance, and ground temperature; the output nerve cell was the diurnal runoff at the glacial terminus. We then analyzed the simulated results under different scenarios by varying the input-nerve-cell parameters. It was found that ANN can simulate the process of glacier meltwater runoff successfully when basic parameters such as air temperature, precipitation and radiation balance are few. The results indicate that ANN can simulate the process of glacial meltwater runoff quite well, and that meteorological variables could in fact be used successfully to simulate glacier meltwater runoff using the ANN method.     

基于斜坡变形演化的岩体强度估算方法

由于斜坡岩体所受的应力场在空间上具有分区性,导致相同岩性与结构特征的岩体其强度参数是不同的。研究斜坡变形演化过程中应力场的空间分布特征,揭示斜坡演化过程对岩体强度参数的影响,对斜坡岩体稳定性评价与优化设计起着举足轻重的作用。以北盘江善泥坡水电站上坝址岩体为例,在工程地质岩组划分的基础上,利用Hoek-Brown强度准则初步估算各岩组强度参数;恢复斜坡的原始地形并建立相应的数学力学模型,根据斜坡不同时期的变形演化特点,应用FLAC3D软件模拟斜坡岩体变形演化过程中应力场的空间分布特点,以此为基础进一步划分上述岩组,最后根据岩体所受的真实应力与Hoek-brown准则估算新分组后的斜坡岩体强度参数,得出更加符合实际情况的强度参数。      

Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels

Collapse settlement is one of the main geotechnical hazards, which should be controlled during first impoundment stage in embankment dams. Imposing large deformations and significant damages to dams makes it an important phenomenon, which should be checked during design phases. Also, existence of a variety of contributing parameters in this phenomenon makes it difficult and complicated to well predict the potential of collapse settlement. Thus, artificial neural networks, which are commonly applied by majority of geotechnical engineers in predicting various perplexing problems, can be efficiently used to calculate the value of collapse settlement. In this paper, feedforward backpropagation neural networks are considered. And three-layered FFBPNNs with the architectures of 4–6–2 and 4–9–2 accurately predicted the coefficient of stress release and collapse settlement value, respectively. These networks were trained using 180 datasets gained from large-scale direct shear test, which were carried out on gravel materials. High correlation between measured and predicted values for both collapse settlement and coefficient of stress release can be easily understood from the coefficient of determination and root mean square error. It is shown that sand content and normal stress applied to the specimens, respectively, are most effective parameters on the collapse settlement value and coefficient of stress release.     

A comparative study of artificial neural networks,Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction

Predictive modeling of hydrological time series is essential for groundwater resource development and management. Here, we examined the comparative merits and demerits of three modern soft computing techniques, namely, artificial neural networks (ANN) optimized by scaled conjugate gradient (SCG) (ANN.SCG), Bayesian neural networks (BNN) optimized by SCG (BNN.SCG) with evidence approximation and adaptive neuro-fuzzy inference system (ANFIS) in the predictive modeling of groundwater level fluctuations. As a first step of our analysis, a sensitivity analysis was carried out using automatic relevance determination scheme to examine the relative influence of each of the hydro-meteorological attributes on groundwater level fluctuations. Secondly, the result of stability analysis was studied by perturbing the underlying data sets with different levels of correlated red noise. Finally, guided by the ensuing theoretical experiments, the above techniques were applied to model the groundwater level fluctuation time series of six wells from a hard rock area of Dindigul in Southern India. We used four standard quantitative statistical measures to compare the robustness of the different models. These measures are (1) root mean square error, (2) reduction of error, (3) index of agreement (IA), and (4) Pearson’s correlation coefficient (R). Based on the above analyses, it is found that the ANFIS model performed better in modeling noise-free data than the BNN.SCG and ANN.SCG models. However, modeling of hydrological time series correlated with significant amount of red noise, the BNN.SCG models performed better than both the ANFIS and ANN.SCG models. Hence, appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series. These results may be used to constrain the model of groundwater level fluctuations, which would in turn, facilitate the development and implementation of more effective sustainable groundwater management and planning strategies in semi-arid hard rock area of Dindigul, Southern India and alike.     

斜坡倾倒变形的工程地质分析

近10年来,在山区,尤其是西部山区的工程建设和灾害防治实践中,我们发现越来越多的以“倾倒”为特征的岩质边坡变形破坏和稳定性问题,其出现的频度和造成的危害大有比肩“滑动”破坏这一边坡失稳的传统主题,成为困扰地质工程师和岩石力学工作者的又一难题。这类问题之所以难,是因为建立在以“滑动”为基础的传统边坡稳定性分析方法不再适用这类边坡。本文在大量工程实例的基础上,分析了边坡倾倒变形和破坏的基本特征,从“倾倒”变形破坏的地质过程和变形稳定性分析的基本理念出发,建立了描述倾倒边坡不同变形程度的工程地质模型,这个模型将倾倒边坡分为倾倒-坠覆、倾倒-错动、倾倒-张裂、倾倒-松弛4个区,分别对应不同的变形程度和稳定性状况,提出了各个区的具体特征和定性指标与量化指标相结合的描述指标体系,从而将倾倒的地质显现、力学机理和变形稳定性有机统一,实现了对倾倒边坡稳定性的工程地质评价。与传统的“滑动”问题不同的是,本文没有强调对这类问题采用强度稳定性的评价思路,而建议采用变形稳定性评价的理念,这似乎更适合倾倒变形这类问题的分析和评价。     

Probabilistic stability analyses of slopes using the ANN-based response surface

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating a commercial finite difference method into a probabilistic analysis of slope stability is presented. Given that the limit state function cannot be expressed in an explicit form, an artificial neural network (ANN)-based response surface is adopted to approximate the limit state function, thereby reducing the number of stability analysis calculations. A trained ANN model is used to calculate the probability of failure through the first- and second-order reliability methods and a Monte Carlo simulation technique. Probabilistic stability assessments for a hypothetical two-layer slope as well as for the Cannon Dam in Missouri, USA are performed to verify the application potential of the proposed method.     

基于融合算法的土石混合体斜坡稳定性预测

土石混合体是物理力学性质较为复杂的地质材料,因此该类斜坡的稳定性评价是工程地质领域的重要课题。为提高斜坡稳定性预测的能力,本文将粒子群算法和果蝇优化算法相互耦合,形成融合算法,并结合机器学习模型,使用决定系数、均方误差和平均绝对误差3个评价指标,构建并评价土石混合体斜坡稳定性的预测模型,最终采用基于融合算法的梯度提升决策树模型对输入参数进行了重要性分析。结果表明：(1)相比于粒子群和果蝇优化算法,融合算法能够有效优化机器学习模型的参数,从而较为明显地提升模型预测精度。(2)基于融合算法的梯度提升决策树模型预测精度最高,达到93.33%,明显优于融合算法下的决策树模型和Stacking模型。(3)影响土石混合体斜坡稳定性的结构因素,其重要性从高到低分别为基覆面倾角、含石率、总体坡角、坡高。     

An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.     

水电站高边坡变形及强度稳定性的系统分析研究

如何准确地研究水电站高边坡的稳定性问题一直是水电工程地质学者专家的研究热点。在此提出工程地质原型分 析、离散元二维数值模拟和极限平衡法相结合的变形稳定性和强度稳定性的系统工程分析方法。以吉林台水电站坝址区高边坡BT20的稳定性问题为例,首先详细分析了该高边坡的工程地质特性,并利用工程地质定性分析法预测了该高边坡的最危险滑动面,然后利用离散元软件UDEC对高边坡的变形特征进行数值模拟分析,数值模拟结果与工程地质分析得到的结论十分吻合。确定了高边坡的危险滑动面就为后面的强度稳定性分析奠定了基础,采用Modified Sarma法计算了边坡在不同工况下的安全系数,敏感性分析结果表明地震作用和地下水位上升对边坡稳定性的威胁最大。     

Residual and Fully Softened Strength Evaluation of Soils using Artificial Neural Networks

A backpropagation artificial neural network (ANN) model is developed to predict the secant friction angle of residual and fully softened soils, using data reported by Stark et al. (J Geotech Geoenviron Eng ASCE 131:575–588, 2005). In the ANN model, index properties such as liquid limit, plastic limit, activity, clay fraction and effective normal stress are used as input variables while secant residual friction angle is used as output variable. The model is verified using data that were not used for model training and testing. The results also indicate that the secant residual friction angle of cohesive soils can be predicted quite accurately using liquid limit, clay fraction and effective normal stress as input variables with R ² = 0.93. The sensitivity analysis results indicate that plastic limit and activity have no appreciable effect on ANN predicted secant friction angles. The secant friction angle predictions of the ANN model were also compared with those of Stark’s et al. (2005) curves and the empirical formulas suggested for the same data sets by Wright (Evaluation of soil shear strengths for slope and retaining wall stability with emphasis on high plasticity clays, 2005). The comparison shows that the ANN model predictions are very close to those suggested by the Stark et al. (2005) curves but much better than the prediction of Wright’s (2005) empirical equations. The results also show that ANN is an alternative powerful tool to predict the secant friction angle of soils.     

An extreme learning machine approach for slope stability evaluation and prediction

This paper presents slope stability evaluation and prediction with the approach of a fast robust neural network named the extreme learning machine (ELM). The circular failure mechanism of a slope is formulated based on its material, geometrical and environmental parameters such as the unit weight, the cohesion, the internal friction angle, the slope inclination, slope height and the pore water ratio. The ELM is proposed to evaluate the stability of slopes subjected to potential circular failures by means of prediction of the factor of safety (FS). Substantial slope cases collected worldwide are utilized to illustrate and assess the capability and predictability of the ELM on slope stability analysis. Based on the mean absolute percentage errors and the correlation coefficients between the original and predicted FS values, comparisons are demonstrated between the ELM and the generalized regression neural network (GRNN) as well as the prediction models generated from the genetic algorithms. Moreover, sensitivity analysis of the slope parameters and the ELM model parameters is carried out based on the two utilized evaluation functions. The time expense of the ELM on slope stability analysis is also investigated. The results prove that the ELM is advantageous to the GRNN and the genetic algorithm based models in the analysis of slope stability. Hence, the ELM can be a promising technique for approaching the problems in geotechnical engineering.     

Reservoir geomechanical parameters identification based on ground surface movements

Determination of geomechanical parameters of petroleum reservoir and surrounding rock is important for coupled reservoir–geomechanical modeling, borehole stability analysis and hydraulic fracturing design. A displacement back analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) combination is investigated in this paper to identify reservoir geomechanical parameters based on ground surface displacements. An ANN is used to map the nonlinear relationship between Young’s modulus, E, Poisson’s ratio, v, internal friction angle, Φ, cohesion, c and ground surface displacements. The necessary training and testing samples for ANN are created by using numerical analysis. GA is used to search the set of unknown reservoir geomechanical parameters. Results of the numerical experiment show that the displacement back analysis technique based on ANN–GA combination can effectively identify reservoir geomechanical parameters based on ground surface movements as a result of oil and gas production.     

基于非线性理论的边坡稳定性评价模型

边坡稳定问题涉及到各类水利、港工、铁路和工业民用建筑工程。由于影响边坡稳定性的因素较多，而且其变形破坏机理复杂，边坡稳定性问题迄今仍然受到理论研究和工程实践的关注。本文采用非线性理论和方法来研究边坡的变形破坏机理，并建立稳定性评价模型。以分叉和突变理论引出突变级数来表征边坡的状态，并用神经网络从中获取稳定性评价和判断的知识，进而构建系统，并对各类边坡稳定状态做出分析评价。实例分析证明了该方法的有效性。