Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model’s validity.     

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.     

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.     

Probabilistic analysis of rock slope stability and random properties of discontinuity parameters, Interstate Highway 40, Western North Carolina, USA

Probabilistic analysis has been used as an effective tool to evaluate uncertainty so prevalent in variables governing rock slope stability. In this study a probabilistic analysis procedure and related algorithms were developed by extending the Monte Carlo simulation. The approach was used to analyze rock slope stability for Interstate Highway 40 (I-40), North Carolina, USA. This probabilistic approach consists of two parts: analysis of available geotechnical data to obtain random properties of discontinuity parameters; and probabilistic analysis of slope stability based on parameters with random properties. Random geometric and strength parameters for discontinuities were derived from field measurements and analysis using the statistical inference method or obtained from experience and engineering judgment of parameters. Specifically, this study shows that a certain amount of experience and engineering judgment can be utilized to determine random properties of discontinuity parameters. Probabilistic stability analysis is accomplished using statistical parameters and probability density functions for each discontinuity parameter. Then, the two requisite conditions, kinematic and kinetic instability for evaluating rock slope stability, are determined and evaluated separately, and subsequently the two probabilities are combined to provide an overall stability measure. Following the probabilistic analysis to account for variation in parameters, results of the probabilistic analyses were compared to those of a deterministic analysis, illustrating deficiencies in the latter procedure. Two geometries for the cut slopes on I-40 were evaluated, the original 75° slope and the 50° slope which has developed over the past 40 years of weathering.     

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.     

Drucker-Prager系列屈服准则在稳定分析中的应用研究

Drucker-Prager（下简称D-P）系列屈服准则作为Mohr-Coulomb（下简称M-C）准则的修正模型在岩土工程中得到了广泛的使用。然而采用不同的D-P系列屈服准则可能会得到差距较大的结果,因此,选用合适的D-P系列屈服准则十分重要。在研究了D-P系列屈服准则与M-C准则之间的对应关系之后,指出D-P系列屈服准则对应的Lode角取值较为有限,难以反映出材料在丰富受力状态下的M-C准则屈服强度,将其取值范围扩大至－30°～30°,并以某边坡和向家坝泄12坝段抗滑稳定分析为例,对如何选用合适的D-P系列屈服准则这一问题进行了研究,提出了具体的选择方法。研究结果表明,只要选用的D-P系列屈服准则对应的Lode角能反映影响边坡和坝基稳定的关键部位的受力状态,就能够得到较好的结果,将D-P系列屈服准则对应的Lode角取值范围扩大至－30°～30°也是必要的。     

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.     

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.     

基于DOFS的水位变化下土质边坡模型稳定性试验研究

作为实现分布式监测技术(DOFS)的重要手段,光纤感测技术近年来得到了迅速的发展。这一技术在地质灾害和岩土工程监测领域有着巨大的应用潜力。本文基于布里渊光时域分析(BOTDA)和布喇格光栅(FBG)两种感测技术,设计了土质边坡稳定性分布式监测室内模型。通过模型试验,重点研究了水位变化作用下土质边坡内部应力分布以及边坡的破坏过程,根据FBG与BOTDA的应变数据,对土质边坡稳定性进行了评价,验证了BOTDA和FBG分布式光纤感测技术应用于土质边坡变形监测的可行性。研究成果对于光纤分布式感测技术的应用研发和在多场作用下的土质边坡稳定性分析具有重要的意义。     

先验概率分布及似然函数模型的选择对边坡可靠度评价影响的定量评估

受工程勘察成本及试验场地限制,可获得的试验数据通常有限,基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是,目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布,似然函数为多维正态分布,这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型,以一个不排水黏土边坡为例,采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明:参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响,选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险,选用对数正态分布获得的边坡可靠度计算结果居中。相比之下,似然函数的影响更加显著。与其他类型似然函数相比,由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时,获得与场地信息更为吻合的计算结果。另外,构建似然...     

Development of a model for analysis of slope stability for circular mode failure using genetic algorithm

Slope stability estimation is an engineering problem that involves several parameters. The interactions between factors that affect slope instability are complex and multi-factorial, so often it is difficult to describe the slope stability mathematically. This paper, proposes the use of a genetic algorithm (GA) as a heuristic search method to find a regression model for analyzing the slope stability. For this purpose, an evolutionary algorithm based on GA was used to develop a regression model for prediction of factor of safety (FS) for circular mode failure. The proposed GA uses the root mean squared error as the fitness function and searches among a large number of possible regression models to choose the best for estimation of FS from six geotechnical and geometrical parameters. For validation of the model and checking its efficiency, a validation dataset was used to evaluate FS using the proposed model and a previously developed mathematical GA based model in the literature. Results have shown that the presented model in this study was capable of evaluating FS at a higher level of confidence regarding the other model (R = 0.89 for presented model in this study comparing R = 0.78 for the other model) and can be efficient enough to be used as a simple mathematical tool for evaluation of factor of safety for circular mode failure especially in preliminary stages of the designing phase.     

Geographical information system-based computational implementation and application of spatial three-dimensional slope stability analysis

Three-dimensional (3D) evaluation of slope stability is a widely addressed problem in the domain of geotechnical engineering. The growing popularity of the geographical information system (GIS) approach, with capacities ranging from conventional data storage to complex spatial analysis and graphical presentation, means that it is also becoming a powerful tool for geotechnical engineers. In this study, in which we combine GIS grid-based data with four proposed column-based models of 3D slope stability analysis, we have devised new correspondent GIS grid-based 3D deterministic models to calculate the safety factor of the slope. Based on the four GIS-based 3D slope stability analysis models, a GIS-based program, 3DSlopeGIS, has been developed to implement the algorithm where all the input data are in the same format as the GIS dataset. The 3DSlopeGIS system, which is an extension of the widely used GIS software package, represents the combined development of 3D slope stability analysis and GIS-based component object model (COM) skills. Since all related data are supplied in the GIS format, this new database approach will be convenient for the repeated renewal and consulting of data. Certain widely addressed examples are evaluated in this paper and the results show the correction and potential of this GIS-based tool as a means of assessing the 3D stability of a slope. Two practical slope problems have been evaluated using the 3DSlopeGIS system. The results illustrate the convenience of data management as well as the effective range selection of Monte-Carlo random variables and the critical slip surface location in some parts of a lava dome.     

Some applications of a backpropagation neural network in geo-engineering

A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables that have direct physical significance. A new approach is adopted by introducing an infiltration coefficient in the network architecture apart from antecedent rainfall, slope profile, groundwater level and strength parameters to predict the slope movement. The input variables for settlement around underground excavations are taken from literature. The neural network models demonstrate a promising result predicting fairly successfully the ground behavior in both cases. If input variables influencing output goals are clearly identified and if a decent number of quality data are available, backpropagation neural network can be successfully applied as mapping and prediction tools in geotechnical investigations.     

基于参数反演的边坡地下水渗流数值模拟

渗流是影响岩土工程边坡稳定性的重要因素,对不同工况下岩土体边坡地下水渗流规律进行数值模拟,弄清其动态变化规律,对于边坡稳定性分析、支护设计、工程防排水措施的制定等都有重要的意义。以龙滩水电站左岸进水口边坡为研究对象,通过免疫进化规划算法,得到了边坡岩体渗流场参数。在此基础上,对洪水期、枯水期情况下地下水渗流状态进行了数值模拟,通过对比分析,研究了不同河床水位情况下地下水渗流变化规律,从而为边坡稳定性预测奠定了理论基础。     

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.     

基于地质模型快速构建的边坡三维稳定性分析

目前边坡稳定性分析多以二维剖面为主,随着各种数值分析软件的应用,边坡的三维稳定性分析技术越来越成熟,但是对于三维边坡的地质模型快速构建的方法不多,且建模精度难以得到保证。本文借助Geobim软件建立工程区的三维地质模型,提出借助surfer、Ansys等软件快速建立flac3d能够识别的类型文件的方法,此方法能够大大简化建模过程,提高建模精度。根据工程区的主要工程地质问题,对工程区的三维地质模型进行概化,结合相关实例对其进行三维地质建模,并借助flac3d软件计算不同工况下边坡的稳定性情况。根据计算结果可知,在暴雨工况下,边坡的稳定性降低,填方工况下,边坡的侧向位移变大,但是其稳定性相对较好,边坡的主要破坏模式为沿着填筑体及边坡的浅表层全、强风化层发生剪切破坏。     

Test on application of distributed fiber optic sensing technique into soil slope monitoring

Brillouin optical time-domain reflectometer (BOTDR), a newly developed distributed fiber optic sensing technique, has been proved to be a very suitable and useful technique for monitoring and early warning of structural engineering by laboratory tests and practical projects due to its unique functions, such as distributing, long distance, anti-electromagnetic interference, waterproof, etc. However, its application to geotechnical engineering, especially soil-slope engineering, has been less carried out due to the complexity of the characteristics of geotechnical materials in the field. In this paper, BOTDR technique is applied to monitor the deformation of a laboratory soil-slope model in small scale in order to test the feasibility and early-warning characteristics of this technique with monitoring the deformation of soil slope. Different types of optical fibers are planted directly in the soil-slope model or bonded to geotextiles and geogrids that are planted in the fillings of the test model. Strain measurements of the model slope under various loads are obtained by BOTDR. By data processing and analysis, the abnormal strains can be obtained distributively, and the position of the abnormal strains can be located as well. The results show much valuable information for applications of BOTDR technique into soil-slope engineering. The test proves that the BOTDR technique can be used to ensure the stability of artificial soil slope and is useful for monitoring and early warning of the artificial soil-slope engineering.     

Use of evolutionary computing for modelling some complex problems in geotechnical engineering

In this paper, the feasibility of using evolutionary computing for solving some complex problems in geotechnical engineering is investigated. The paper presents a relatively new technique, i.e. evolutionary polynomial regression (EPR), for modelling three practical applications in geotechnical engineering including the settlement of shallow foundations on cohesionless soils, pullout capacity of small ground anchors and ultimate bearing capacity of pile foundations. The prediction results from the proposed EPR models are compared with those obtained from artificial neural network (ANN) models previously developed by the author, as well as some of the most commonly available methods. The results indicate that the proposed EPR models agree well with (or better than) the ANN models and significantly outperform the other existing methods. The advantage of EPR technique over ANNs is that EPR generates transparent and well-structured models in the form of simple and easy-to-use hand calculation formulae that can be readily used by practising engineers.     

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.     

考虑孔隙水压力作用的岩土体弹塑性模型及其有限元实现

岩土工程中孔隙水的作用一直是工程安全稳定分析中不容忽视的问题。基于Viesca提出的考虑饱和孔隙水压力作用的孔隙弹塑性模型,推导其在满足D-P准则的理想弹塑性情况下的具体形式。对于不排水条件,饱水状态下材料强度参数被等效化,使得屈服面的形状发生改变,同时根据最近点投影法确定其在弹塑性计算中的本构积分策略。不排水孔隙水压力对岩土体的塑性响应的影响得以表达,而对结构的稳定性的影响通过基于过应力概念的不平衡力反映出来。基于该模型开发非线性有限元程序PoreTfine,选取非均质边坡降雨入渗下的稳定问题作为算例验证其实用性,边坡随降雨作用的非平衡演化过程得到研究。Skempton系数的增大对连续降雨条件下边坡有弱化作用,对其稳定性不利。