Hoek-Brown准则岩体力学参数非线性位移反分析

Hoek-Brown准则综合考虑岩体结构、岩块强度等因素,能很好地反映岩体的非线性破坏,更符合工程实际,但其参数选取的主观性及不确定性严重影响其应用效果。通过分析敏感性分析结果及Hoek-Brown准则的圆形硐室围岩位移表达式,借助FLAC~(3D)数值模拟和回归性分析建立了基于Hoek-Brown准则的非线性回归模型,结合差分进化算法提出了合理的位移反分析方法及步骤,并成功应用于青岛地铁工程。结果表明,所建立的非线性回归模型能较好地反映隧道位移与地质强度指标GSI、扰动系数D及泊松比μ的关系,所提出的位移反分析方法能较准确地反演获得GSI、D、μ值,由所得反演参数值计算的位移与现场监测位移的相对误差较小,具有良好的一致性,验证了该方法的合理性、准确性,为准确获取Hoek-Brown准则参数提供了新方法。     

矿井小断层延伸长度的多元线性回归预测模型

矿井小断层普遍存在且错综复杂,对煤层开采影响很大。根据黄河北煤田赵官井田7号煤层小断层资料,选取断层走向延展长度、断层落差和断层倾角3个因素,运用多元回归分析方法,建立了7号煤层NE、NEE和NW向小断层延伸长度的预测模型。实际应用表明,该模型的预测精度较高,比较符合实际情况。     

Sunspot cycle prediction using multivariate regression and binary mixture of Laplace distribution model

Prediction of sunspot cycle is a vital activity in space mission planning and various engineering decision making. In the present study, the sunspot cycle prediction has been carried out by a hybrid model which employs multivariate regression technique and the binary mixture of Laplace distribution (BMLD) function. The Expectation Maximization (EM) algorithm is being applied to the multivariate regression analysis to obtain a robust prediction of the sunspot cycle. Sunspot cycle 24 has been predicted using this technique. Multivariate regression model has been derived based on the available cycles 1 to 23. This model could predict cycle 24 as an average of previous cycles. Prediction from this model has been refined to capture the cycle characteristics such as bimodal peak at the high solar activity period by incorporating a predicted peak sunspot number from the BMLD model. This revised prediction has shown more accuracy in forecasting the major discrete features of sunspot cycle like maximum amplitude, the Gnevyshev gap, time duration from peak to peak amplitude, and the epoch of peak amplitude. This refined prediction shows that cycle 24 will be having a peak amplitude of 78 with an uncertainty of ±25. Moreover, the present forecast says that, cycle 24 will be having double peak with a strong second peak compared to the first peak. This hypothesis is found to be true with the realized data of cycle 24. Further, this techniques have been validated by predicting sunspot cycles 22 and 23. A preliminary level prediction of sunspot cycle 25 also been carried out using the technique presented here. Present study predicts that, cycle 25 also will be a modest cycle like the present cycle 24, and the peak amplitude may vary in a band of 75–95.     

Specification and prediction of nickel mobilization using artificial intelligence methods

Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO₄, HCO₃, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment.     

Seismic performance of a high-rise building by using linear and nonlinear methods

Natural Hazards - In this paper, the seismic behavior of the existing reinforced concrete tall building is investigated by using the linear and nonlinear dynamic methods. The selected existing...     

Numerical simulation of liquefaction in porous media using nonlinear fluid flow law

It is well known that for a sufficiently high seepage velocity, the governing flow law of porous media is nonlinear (J. Computers & Fluids 2010; 39 : 2069–2077). However, this fact has not been considered in the studies of soil‐pore fluid interaction and in conventional soil mechanics. In the present paper, a fully explicit dynamic finite element method is developed for nonlinear Darcy law. The governing equations are expressed for saturated porous media based on the extension of the Biot (J. Appl. Phys. 1941; 12 : 155–164) formulation. The elastoplastic behavior of soil under earthquake loading is simulated using a generalized plasticity theory that is composed of a yield surface along with non‐associated flow rule. Numerical simulations of porous media subjected to horizontal and vertical components of ground motion excitations with different permeability coefficients are carried out; while computed maximum pore water pressure is specially taken into consideration to make the difference between Darcy and non‐Darcy flow regimes tangible. Finally, the effect of non‐Darcy flow on the evaluated liquefaction potential of sand in comparison to conventional Darcy law is examined. Copyright © 2014 John Wiley & Sons, Ltd.     

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

A remote sensing and Geographic Information System-based study has been carried out for landslide susceptibility zonation in the Chamoli region, part of Garhwal Himalayas. Logistic regression has been applied to correlate the presence of landslides with independent physical factors including slope, aspect, relative relief, land use/cover, lithology, lineament, and drainage density. Coefficients of the categories of each factor have been obtained and used to assess the landslide probability value to ultimately categorize the area into various landslide susceptibility zones; very low, low, moderate, high, and very high. The results show that 71.13% of observed landslides fall in 21.96% of predicted very high and high susceptibility zone, which in fact should be the case. Furthermore, lineament first buffer category (0–500 m) and the east and south aspects are the most influential in causing landslides in the region.     

叠前同步反演在M地区储层预测中的应用

经前人研究结果表明,珠江口盆地M地区常规叠后波阻抗较难识别储层和反映储层物性。使用叠前同步反演方法,即利用叠前CRP道集和测井信息,采用Fatti方程的改进形式进行反演,可同时获得纵波阻抗、横波阻抗、密度等弹性参数。结果表明,密度属性能很好地区分储层,反映储层物性,目的层灰岩和砂岩储层的沿层密度属性很好地反映了储层横向变化规律。实钻结果表明,预测结果与井实测结果吻合度较高,表明利用叠前同步反演技术进行珠江口盆地M地区储层预测是可行的。     

Simulation of anisotropic heterogeneous near-well flow using MPFA methods on flexible grids

Control-volume multipoint flux approximations (MPFA) are discussed for the simulation of complex near-well flow using geometrically flexible grids. Due to the strong non-linearity of the near-well flow, a linear model will, in general, be inefficient. Instead, a model accounting for the logarithmic pressure behavior in the well vicinity is advocated. This involves a non-uniform refinement of the grid in the radial direction. The model accounts for both near-well anisotropies and heterogeneities. For a full simulation involving multiple wells, this single-well approach can easily be coupled with the reservoir model. Numerical simulations demonstrate the convergence behavior of this model using various MPFA schemes under different near-well conditions for single-phase flow regimes. Two-phase simulations support the results of the single-phase simulations.     

Characterization of a landslide-triggered debris flow at a rainforest-covered mountain region in Brazil

Natural Hazards - Debris flows represent great hazard to humans due to their high destructive power. Understanding their hydrogeomorphic dynamics is fundamental in hazard assessment studies,...     

Scale prediction in liquid flow through porous media: A geochemical model for the simulation of CaCO3 deposition at the near-well region

Currently available models of CaCO₃ scale formation during flow in porous media, provide simply a scaling tendency. We present a new tool that proceeds further to predict the distribution of scale precipitation in a one-dimensional porous medium and the consequent pore blocking. The innovative feature of this model is the combination of thermodynamics, kinetics and hydrodynamics for the prediction of scale deposition along and around the production wells. The development of the geochemical model was based on scale reaction calculations attempting to simulate/interpret dynamic tube-scale laboratory experiments. In this work, we present the equation scheme with the basic assumptions employed and some results with relevance to the experimental work. The paper also considers some of the issues associated with the generation and reliability of the laboratory data used in the construction of the geochemical model. The new laboratory scale data have been acquired from a series of sandpack blocking experiments using for the first time a radiotracer technique.     

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).     

Screening of extraction methods for Cd and As bioavailability prediction in rhizospheric soil using multivariate analyses

Heavy metals in tailings and mining wastes from abandoned mines can be released into adjacent agricultural field and bioaccumulated in crops or vegetables. Therefore, prediction of metal bioavailability has become an important issue to prevent adverse effect of bioaccumulated metals on human health. In this study, single and sequential extraction methods were compared using multivariate analysis to predict the bioavailability of Cd and As in contaminated rhizosphere soils. Single extraction using 0.1 M HCl for Cd and 1.0 M HCl for As had an extraction efficiency of 8–12% for soil Cd and 14–17% for soil As compared to total concentration extracted with aqua regia. Using sequential extraction, Fe–Mn-bound Cd (FR3) and residual Cd (FR5) were the dominant fractions representing 43 and 41% of total Cd concentration. For As, the strongly absorbed form (FR2) was the most abundant chemical fraction showing 45–54% of the total As concentration in soil. Multivariate analyses showed that single extraction with HCl and total concentration of Cd and As in soil were significantly correlated to potato and green onion plant tissue metal concentration. Although little information was obtained with multiple regression analysis because of multicollinearity of variables, the result of principle component analysis (PCA) revealed that the highest positive loading was obtained using total concentration of Cd and As in soil in the first principle component (PC1). In addition, total concentration of Cd and As in soil was independently grouped with other chemical fractions by cluster analysis. Therefore, the overall result of this research indicated that total concentrations of Cd and As in rhizosphere soils were the best predictors of bioavailability of heavy metals in these contaminated soils.     

On intermediate-term prediction of strong earthquakes in the Himalayan arc region using pattern recognition algorithm M8

Seismicity of the Himalayan arc lying within the limits shown in figure 1 and covering the period 1964 to 1987 was scanned using M8 algorithm with a view to identifying the times of increased probabilities (TIPs) of the occurrence of earthquakes of magnitude greater than or equal to 7·0, during the period 1970 to 1987. In this period, TIPs occupy 18% of the space time considered. One of these precedes the only earthquake in this magnitude range which occurred during the period. Two numerical parameters used in the algorithm, namely the magnitude thresholds, had to be altered for the present study owing to incomplete data. Further monitoring of TIPs is however warranted, both for testing the predictive capability of this algorithm in the Himalayan region and for creating a base for the search of short-term precursors.     

利用毛管压力曲线分形分维方法研究流动单元

利用取心井铸体薄片获得的图像资料和毛管压力曲线,通过图像分形几何学方法以分维数的形式定量地表征出了复杂的微观孔隙喉道结构特征,发现能够很好地划分和评价孔隙岩石中油、气、水的渗流差异,可以用于储层微观流动单元表征。文中阐述了岩石微观孔隙喉道结构分形的理论基础、计算方法和应用于表征流动单元的依据。建立了中国西部砾岩低渗透油藏微观孔隙喉道分维数与孔隙度、渗透率之间计算图版,据此在油藏中利用常规测井资料获得的孔隙度、渗透率参数计算微观孔隙喉道分维数,开展全油藏流动单元划分与评价,取得了良好的效果。研究结果表明,利用毛管压力曲线分形分维方法研究储层微观流动单元是一种很有效的途径。     

Simulation of groundwater flow and contaminant transport at a landfill site using models

Numerical and analytical modelling studies were conducted for the analysis of groundwater flow and contaminant transport at the Innisfil landfill site in the Town of Innisfil, County of Simcoe, in Ontario, Canada. Previously conducted field studies categorized the upper stratigraphy at the site into three units: upper sand unit, upper silt/clay unit and Intermediate Sand unit. Essentially horizontal groundwater movement in the two sand units and vertical downward flow in the silt/clay unit were reported by the field hydrogeologists. In the following, application of three computer models (FLOWPATH, USGS MOC and POLLUTE) for the simulation of the groundwater flow and contaminant transport processes at the Innisfil landfill site is described. The paper focuses on the calibration of groundwater flow and contaminant transport systems, and demonstrates how the insight gained during the contaminant transport calibration was used to improve the initial groundwater flow characterization of the hydrogeological system.     

Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.     

Estimation of water flow from upper reservoir of Azad pumped storage power plant,using geostatistical methods

The objective of this paper is to estimate water seepage from the upper reservoir of Azad pumped storage power plant (PSPP), based on combined geotechnical investigations and geostatistical methods. Azad PSPP has been located in Sanandaj Province in Iran. In order to select the optimum water tightening alternative, such as clay blanket, concrete cover (or concrete lining), geomembrane, asphalt cover etc, estimation of water seepage from the reservoir is essential. Six exploratory boreholes were drilled at the pumped storage reservoir area and permeability tests (Lugeon tests) were conducted in all of them. Records at the boreholes have been considered as the main source for seepage calculations. Due to expansion of upper reservoir and a few boreholes, distribution of permeability and permeability changes in the reservoir area is not indicator of reservoir. In this research using geostatistical method (Kriging), lugeon values have been estimated for walls of reservoir. According to correspondence between estimated permeability distribution and geological conditions, the estimated values are acceptable. In addition results show that in about 60% of tests, permeability is very high and potential of water seepage is very high. Seepage was estimated for reservoir by using both analytical (vedernikov method) and numerical method. Results from both methods are very close and the average of seepage is around 280000 m³/day. From the results and general geological considerations, seepage is concentrated at fault zones. Results show that by using appropriate permeability distribution, the estimated values of water seepage are reliable. Due to the high amount of water seepage and economic importance of water in this region, water tightening is necessary.     

国内外泥石流活动关键指标估算方法之比较

泥石流流体容重、流速、冲击力是表征泥石流活动特征众多指标中的三个关键指标.本文在分析国内外对这些指标评估现状的基础上,总结了应用较普遍的估算公式,对比了不同公式的特点及其适用性.比较结果认为:由于泥石流组成和运动状态的复杂性以及影响因素的多样性,对于这三个指标,目前的估算公式都不具有普适性.在当前的认知水平下,建立基于各个地区泥石流特点的指标估算经验公式是最为实用的途径.在各个指标经验公式的建立方法上,考虑各个粒组的多变量容重统计分析方法相对较为合理;泥石流流速估算公式的建立途径在我国、前苏联和欧美国家之间有显著差异,前二者基于曼宁公式,后者基于强迫涡流公式、以弯道超高为主要参数;泥石流冲击力的估算方法国内外都以动量理论为基础,区别主要体现在经验系数取值上,巨石冲击力的计算则都考虑了拦挡建筑物的特点.