Prediction of surface crown pillar stability using artificial neural networks

A relatively novel technique, artificial neural networks (ANN), is used in predicting the stability of crown pillars left over large excavations. Data for the training and verification of the networks were obtained from the literature. Four artificial networks, based on two different architectures, were used. The networks used different numbers of input parameters to predict the stability or failure of crown pillars. Multi‐layer perceptron networks using mine type, dip of orebody, overburden thickness, pillar thickness, pillar length, stope height, backfill height, Rock Mass Rating (RMR) of the host rock and RMR of the orebody showed excellent performance in training and verification. Adding three more variables, namely pillar width, rock density and pillar thickness to width ratio, showed symptoms of over‐learning without degrading performance significantly. Radial basis function networks were capable of predicting crown pillar behaviour on the basis of few input functions. It was shown that mine type, dip and pillar thickness to width ratio can be used for a preliminary estimation of stability. Copyright © 2006 John Wiley & Sons, Ltd.     

A direct inversion scheme for deep resistivity sounding data using artificial neural networks

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need ofa priori information. We introduce a two-step network approach where the first network identifies the curve type, followed by the model parameter estimation using the second network. This approach provides the flexibility to accommodate all the characteristic sounding curve types with a wide range of resistivity and thickness. Here we realize a three layer feed-forward neural network with fast back propagation learning algorithms performing well. The basic data sets for training and testing were simulated on the basis of available deep resistivity sounding (DRS) data from the crystalline terrains of south India. The optimum network parameters and performance were decided as a function of the testing error convergence with respect to the network training error. On adequate training, the final weights simulate faithfully to recover resistivity and thickness on new data. The small discrepancies noticed, however, are well within the resolvability of resistivity sounding curve interpretations.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

基于深度学习的山东大尹格庄金矿床深部三维预测模型

在隐伏矿体三维预测中,预测模型的准确性在很大程度上取决于找矿指标对矿化富集部位的指示性。然而,找矿指标容易受到找矿概念模型可靠性和成矿信息提取有效性限制,从而影响预测的准确性。论文以山东大尹格庄金矿隐伏矿体三维预测为例,基于深度学习方法,构建矿床深部隐伏矿体三维预测模型,旨在利用深度网络模型,学习获得对矿化具有显著指示性的找矿指标,提升三维预测的准确性。该方法将三维地质模型及其形态特征转换为适合卷积网络二维图像,采用卷积神经网络实现找矿指标的自动提取,并构建三维地质模型到矿化富集地段的定量关联。利用该方法建立了大尹格庄金矿的三维预测模型,经与几种人工建立找矿指标预测模型的对比分析,表明基于深度学习的预测模型较大地提升了预测准确性。     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

Prediction of surface blast patterns in limestone quarries using artificial neural networks

This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry.     

BP和RBF在断层防水煤柱留设宽度预测中的应用

在总结全国各典型煤矿断层防水煤柱相关资料的基础上,以水头压力、煤层厚度、安全系数、煤的抗张强度为主要影响因子,选择有代表性的样本数据,通过Matlab软件构建了BP和RBF神经网络模型,对各煤矿断层防水煤柱的留设宽度进行了预测,并与规程经验公式计算的结果进行了对比。结果显示,在煤矿断层防水煤柱留设宽度预测中,RBF神经网络比BP神经网络的训练速度更快,效率更高,具有更加广阔的应用前景。     

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.     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.     

Particle shape effect on macro‐ and micro‐behaviors of monodisperse ellipsoids

The microscopic and macroscopic behaviors of assemblages of monodisperse ellipsoids with different particle shapes were studied using the discrete element method. Four samples were created with 1170 identical prolate ellipsoids. The samples were compressed isotropically to 100 kPa. Then triaxial compression tests were carried out to very large strains until the ultimate state was reached. This paper presents typical macroscopic result including stress–strain relationship and volumetric behavior. In addition, the fabric of the samples was examined at the initial state, at the peak shear strength state, and at the ultimate state. We studied the evolution of three vector‐typed micromechanical arguments with strain including the particle orientation, branch vector, and normal contact force. The normal contact force (micromechanical argument) was found to have a direct relationship with the principal stress ratio (macroscopic parameter). The angles between these vectors were also investigated. The maximum angle between vectors is related to particle shape. The results indicate that the distributions and the maximum values of these angles do not change with loading. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

粒状介质三维复杂应力加载离散元数值试验方法

实际荷载条件下（如交通、地震荷载）,粒状岩土材料常受到三维复杂应力路径作用。目前,多数粒状岩土材料的本构理论和模型都基于简单应力路径加载条件下的物理试验提出,在更加复杂应力路径下的适用性则需要进一步验证。但受机械控制的限制,物理试验中无法实现很多客观存在的三维复杂应力路径加载。为了能够再现并分析三维复杂应力路径下粒状介质的力学响应,提出了一种离散元数值试验方法,该方法采用球形数值试样,通过直接控制试样边界应力达到对3个主应力大小和方向的任意控制,从而可以实现诸多物理试验中无法实现的复杂应力路径。通过与目前常见的一些物理试验进行定性对比,论证了该数值试验方法通过高精度的加载控制和测量能够再现已有物理试验现象。在此基础上,进一步开展了应力主轴的三维旋转,分析了在这种实际存在却无法通过物理试验再现的加载条件下粒状介质的变形规律,初步显示了提出的数值试验方法在深入研究三维复杂应力路径下粒状介质力学响应方面所拥有的能力和优势。     

Stochastic simulations of fault networks in 3D structural modeling

3D structural modeling is a major instrument in geosciences, e.g. for the assessment of groundwater and energy resources or nuclear waste underground storage. Fault network modeling is a particularly crucial step during this task, for faults compartmentalize rock units and plays a key role in subsurface flow, whether faults are sealing barriers or drains. Whereas most structural uncertainty modeling techniques only allow for geometrical changes and keep the topology fixed, we propose a new method for creating realistic stochastic fault networks with different topologies. The idea is to combine an implicit representation of geological surfaces which provides new perspectives for handling topological changes with a stochastic binary tree to represent the spatial regions. Each node of the tree is a fault, separating the space in two fault blocks. Changes in this binary tree modify the fault relations and therefore the topology of the model.     

大型复杂地质体三维数值模型构建方法比较研究

真实地质体三维数值模型构建是进行岩体工程数值分析面临的难题,开展大型复杂地质体三维数值模型构建方法比较研究具有重要意义。以3DMine数字化模型为基础,提出了3DMine-FLAC3D耦合建模方法和3DMine-Surfer-Rhino- ANSYS-FLAC3D多软件耦合建模方法,详细阐述了各建模方法具体步骤,深入分析了各建模方法优缺点及适用性,通过对比各建模方法的优势与短板,取长补短,改进了3DMine-FLAC3D耦合建模方法存在的缺陷,解决了复杂地质体三维数值模型构建难题。以广西铜坑矿锌多金属矿体开采为背景,利用大型复杂地质体三维数值建模方法,构建了锌多金属矿三维数值模型,分析了矿体上行开采地表沉陷规律。研究成果对准确构建大型复杂地质体三维数值模型具有重要指导作用。     

Generation of a realistic 3D sand assembly using X‐ray micro‐computed tomography and spherical harmonic‐based principal component analysis

Three‐dimensional particle morphology is a significant problem in the discrete element modeling of granular sand. The major technical challenge is generating a realistic 3D sand assembly that is composed of a large number of random‐shaped particles containing essential morphological features of natural sands. Based on X‐ray micro‐computed tomography data collected from a series of image processing techniques, we used the spherical harmonics (SH) analysis to represent and reconstruct the multi‐scale features of real 3D particle morphologies. The SH analysis was extended to some highly complex particles with sharp corners and surface cavities. We then proposed a statistical approach for the generation of realistic particle assembly of a given type of sand based on the principle component analysis (PCA). The PCA aims to identify the major pattern of the coefficient matrix, which is made up of the SH coefficients of all the particles involved in the analysis. This approach takes into account the particle size effect on the variation of particle morphology, which is observed from the available results of micro‐computed tomography and QICPIC analyses of sand particle morphology. Using the aforementioned approach, two virtual sand samples were generated, whose statistics of morphological parameters were compared with those measured from real sand particles. The comparison shows that the proposed approach is capable of generating a realistic sand assembly that retains the major morphological features of the mother sand. Copyright © 2016 John Wiley & Sons, Ltd.     

基于PFC2D数值试验的异性结构面剪切强度特性研究

天然岩体中广泛发育两侧岩性不同的异性结构面,开展异性结构面变形和强度特性研究旨在为岩体稳定性评价和利用提供依据。选取三峡库区侏罗系典型的砂岩-泥岩异性岩层,首先运用分形几何理论,定量计算了平直和4种不同不规则起伏形态结构面的粗糙度系数JRC值,然后基于PFC2D颗粒流程序,分别开展了以上5种形态异性结构面的数值剪切试验,获得了各形态结构面在不同正应力下的剪切应力-位移曲线。根据数值试验结果,采用巴顿的JRC-JCS模型分析了异性结构面强度特性,并与同性结构面强度性质进行对比研究。最后,在考虑异性结构面剪切破坏机制的基础上,引入强度因子的概念,提出了新的适用于异性结构面强度评价的两类改进巴顿准则。研究结果表明：异性岩体结构面抗剪强度介于相同粗糙度的两种同性结构面强度之间,在较低正应力下接近软岩同性结构面强度,符合Ⅰ型改进巴顿准则;在较高正应力下偏向硬岩同性结构面强度,符合Ⅱ型改进巴顿准则。实际工程中可利用改进准则并根据异性结构面应力状态对岩体稳定性进行评价,弥补了以往研究的不足。     

水电三维地质模型中各地质要素的建模方法

决定水电工程地质分析成败的主要环节是地质实体的模拟。用三维数值地质实体模型来模拟实际工程施工过程,可以协助解决各种工程地质问题。介绍了水电工程中地质模型的各个要素（地形面、基岩面、岩层界面、构造界面、透镜体、地层单元、勘探工程以及地质实体）的创建方法、实现思路以及可视化显示,基本包含了水电工程地质分析所涉及到的所有方面。     

3-D Geological Modeling–Concept, Methods and Key Techniques

3-D geological modeling plays an increasingly important role in Petroleum Geology, Mining Geology and Engineering Geology. The complexity of geological conditions requires different modeling methods in different situations. This paper summarizes the general concept of geological modeling; compares the characteristics of borehole-based modeling, cross-section based modeling and multi-source interactive modeling; analyses key techniques in 3-D geological modeling; and highlights the main difficulties and directions of future studies.     

Estimation of compaction parameters of fine‐grained soils in terms of compaction energy using artificial neural networks

The determination of the compaction parameters such as optimum water content (w_opt) and maximum dry unit weight (γ_dmax) requires great efforts by applying the compaction testing procedure which is also time consuming and needs significant amount of work. Therefore, it seems more reasonable to use the indirect methods for estimating the compaction parameters. In recent years, the artificial neural network (ANN) modelling has gained an increasing interest and is also acquiring more popularity in geotechnical engineering applications. This study deals with the estimation of the compaction parameters for fine‐grained soils based on compaction energy using ANN with the feed‐forward back‐propagation algorithm. In this study, the data including the results of the consistency tests, standard and modified Proctor tests, are collected from the literature and used in the analyses. The optimum structure of a network is determined for each ANN models. The analyses showed that the ANN models give quite reliable estimations in comparison with regression methods, thus they can be used as a reliable tool for the prediction of w_opt and γ_dmax. Copyright © 2010 John Wiley & Sons, Ltd.