Multivariate statistical analysis approach for prediction of blast-induced ground vibration

Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits.     

An intelligent approach to prediction and control ground vibration in mines

Drilling and Blasting are still considered to be the most economical method for rock excavation either on surface or underground. The explosive energy, which breaks the rockmass, is not fully utilized for this purpose. Only 20–30% of explosive energy is utilized for fragmenting the rockmass and the rest wasted away in the form of ground vibration, air blast, noise, fly rock, back breaks, etc. Among them, ground vibration is considered to have the most damaging effect. A number of predictor equations have been proposed by various researchers to predict ground vibration prior to blasting. Still, it is difficult to recommend any one predictor for a particular ground condition because ground vibration is influenced by a number of parameters. These parameters are either controllable or non-controllable like blast geometry, explosive types, rock strength properties, joints patterns, etc. In the present paper, an attempt has been made to predict the ground vibration using an Artificial Neural Network incorporating large number of parameters, which affect the ground vibration. Results are also compared with the values obtained from regression analysis and observed field data sets. Finally, it is found that the neural network approach is more accurate and able to predict the value of blast vibration without increasing error with increasing number of inputs and non-linearity among these.     

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.     

Hyperbola-Logistic模型预测列车引起的地面振动

振动波在土介质中传播包含呈幂函数关系的几何衰减和呈指数函数关系的阻尼衰减两方面,针对振动波衰减的两种性质,提出Hyperbola-Logistic组合模型来描述振动波沿地表的传播。实例分析表明,Hyperbola-Logistic组合模型既可以用来预测地面位移、速度、加速度及振级的衰减,又可以预测高架桥段、路堤段及地下列车引起的地面振动。Hyperbola-Logistic组合模型具有较高的拟合精度,相关系数R均在0.95以上,决定系数R2均在0.90以上,它对振动衰减的拟合是可信的。通过对比,文中模型无论数值大小还是分布趋势,都能更好地预测列车引起的地面振动。     

Assessment of vulnerability zones for ground water pollution using GIS-DRASTIC-EC model: A field-based approach

Assessment of groundwater vulnerability to pollution is an essential pre-requisite for better planning of an area. We present the groundwater vulnerability assessment in parts of the Yamuna Nagar District, Haryana State, India in an area of about \(800 \hbox { km}^{2}\), considered to be a freshwater zone in the foothills of the Siwalik Hill Ranges. Such areas in the Lower Himalayas form good groundwater recharge zones, and should always be free from contamination. But, the administration has been trying to promote industrialization along these foothill zones without actually assessing the environmental consequences such activities may invite in the future. GIS-DRASTIC model has been used with field based data inputs for studying the vulnerability assessment. But, we find that inclusion electrical conductivity (EC) as a model parameter makes it more robust. Therefore, we rename it as GIS-DRASTIC-EC model. The model identifies three vulnerability zones such as low, moderate and high with an areal extent of 5%, 80% and 15%, respectively. On the basis of major chemical parameters alone, the groundwater in the foothill zones apparently looks safe, but analysis with the help of GIS-DRASTIC-EC model gives a better perspective of the groundwater quality in terms of identifying the vulnerable areas.     

南京地面轨道交通运行引起的大地环境振动预测研究

城市轨道交通造成环境的振动“污染”不容忽视。通过现场测试和理论分析,得到南京地面轨道交通运行引起的大地竖向振动加速度幅值、频率随距离轨道中心线不同位置的衰减规律,即随着与轨道水平距离的增加,大地竖向振动加速度幅值总的趋势表现为逐渐减弱,但在距离轨道中心线20～30 m之间振动幅值有所反弹;振动加速度的频率集中在0～ 100 Hz,最大值出现在30～80 Hz左右,随着与轨道水平距离的增加,各频率的振动信号分量总的趋势是减弱,且频率愈高衰减愈快。基于“北京交通大学”预测公式,建立包含受振点距离、地基土性质、列车速度3个参数的南京地面轨道交通运行引起大地振动的预测模型,与实测数据相比,吻合较好。     

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

抽水地面沉降预计的随机介质模型

本文视抽水引起的地面沉降为一随机过程,应用随机介质理论和土力学基本原理,建立了随机介质模型,对抽水地面沉降及变形分布进行了分析预测,计算实例表明,该模型是有铲的。     

隧道掘进爆破地震峰值神经网络预报研究

结合某抽水蓄能电站尾水隧道掘进爆破地面振动监测结果,建立了基于BP神经网络隧道掘进爆破振动速度峰值的预报模型。并对铁路运输安全进行预报,与现场实际较好地吻合。将神经网络模型预报的结果与传统方法(经验公式法)预报的结果相比,前者的预报结果有明显的改善。对于指导隧道掘进爆破设计,优化爆破参数,确保安全具有重要的意义。     

A new model for the prediction of earthquake ground-motion duration in Iran

The paper proposes a new empirical model to estimate earthquake ground-motion duration, which significantly influences the damage potential of an earthquake. The paper is concerned with significant duration parameters that are defined as the time intervals between which specified values of Arias intensity are reached. In the proposed model, significant duration parameters have been expressed as a function of moment magnitude, closest site-source distance, and site condition. The predictive model has been developed based on a database of earthquake ground-motion records in Iran, containing 286 records up to the year 2007, and a random-effect regression procedure. The result of the proposed model has been compared with that of other published models. It has been found that the proposed model can predict earthquake ground-motion duration in Iran with adequate accuracy.     

Analytical model for the prediction of building deflections induced by ground movements

The objective of this study is to develop an analytical model that can predict the building‐relevant deflections induced by tunnelling or mining subsidence. The model takes into account soil–structure interactions due to differences in stiffness between the ground and the building. The ground is modelled by the Winkler model with an initial ground curvature equivalent to the free‐field ground movements. The building is modelled by a horizontal beam with uniform loading. The static and cinematic equilibrium of both the ground and the building are then calculated to assess the final building and ground shape, and the building deflection is derived. The resulting analytical model is used to investigate the influence of the ground and the building's mechanical properties, the building load and the initial value of the free‐field ground curvature (hogging or sagging). The model appears to be more comprehensive than those reported elsewhere that address the problem with numerical models. In particular, the analytical model makes it possible to distinguish two different final situations—with continuous or discontinuous contact between the ground and the building. The model is compared with numerical results and used to analyse a case study. Copyright © 2010 John Wiley & Sons, Ltd.     

Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data

In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating.     

A new approach for crude oil price prediction based on stream learning

Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.     

地下开采地表下沉预测的三维理论模型

根据弹性力学理论,建立了地表下沉预测分析的三维层状理论模型,并用于小官庄铁矿地下开采引起地表下沉的预测分析。在分析过程中,采用Matlab软件进行数值积分计算和图形绘制。通过工程实例计算分析,表明理论计算结果与矿山现场实测资料二者吻合较好。     

A new model for the vibration isolation via pile rows consisting of infinite number of piles

In this study, on the basis of the Floquet transform method, a numerical model for the simulation of the vibration isolation via multiple periodic pile rows with infinite number of piles is established. By means of the fictitious pile method due to Muki and Sternberg, the second kind of Fredholm integral equations for the pile rows are developed by using the fundamental solutions for the half‐space and the compatibility conditions between the piles and half‐space. Employing the Floquet transform method, integral equations for the pile rows in the wavenumber domain are then derived. Solution of the integral equations yields the wavenumber domain solution for the pile rows. The space domain solution can then be retrieved by inversion of the Floquet transform. Numerical results show that the proposed model with the Floquet transform method is in a good agreement with those of the conventional direct superposition method. On the basis of the new model, influences of the spacing between neighboring piles, the Young's modulus of the piles, and the pile length on the vibration isolation effect of the pile rows are investigated. Numerical simulations conducted in this study show that compared with the direct superposition method, the efficiency of the proposed model for simulation of the vibration isolation via pile rows is very high. Copyright © 2012 John Wiley & Sons, Ltd.     

A hybrid discrete-continuum approach to model hydro-mechanical behaviour of jointed rocks

A hybrid discrete-continuum approach has been presented in this paper to simulate water flow in the near and far fields of deformable fractured rocks. In the near field, the discrete model is used; while in the far field, the equivalent continuum model is employed. The discrete element method (with the static relaxation algorithm) is used in the near field and the boundary integral equation method in the far field. Along the interface of these two domains, both mechanical and hydraulic compatibility conditions are satisfied. Fully coupled hydro-mechanical analysis can be conducted in the combined near and far fields. Application to a dam foundation problem has demonstrated the capability of the developed approach.     

Flow slides run-out prediction using a sliding-consolidation model

The estimation of maximum travel distance of flow slides is an important topic to assess the consequence of natural disasters caused by landslides. During debris transportation, dissipation rules of pore-water pressure determine movement properties of flow slides. Based on 1-D Terzaghi consolidation theory, expressions of excess pore-water pressure with three cases of initial conditions are deduced and are programmed using Mathematica^® language. Furthermore, the factors affecting the distribution of pore-water pressure are studied using nondimensional method interactively, such as z/h, u _b /u _a , and T _v , which are fairly significant to investigate soil consolidation during the movement of flow slides. On the basis of the sliding-consolidation model first provided as reported by Hutchinson (Can. Geotech. J. 23(2):115-126, 1986), equations of pore-water pressure, velocity, and travel distance of flow slides are obtained and the physical quantities are coded as mathematical functions using Mathematica^® language characterized by its user-friendly interfaces to study run-out properties of flow slides very easily. The program can be used to compute velocity of flow slide, time, and pore-water pressure at a certain position, and thus judge automatically when and where flow slide will stop on slopes with different slope angles, solving the computing difficulties encountered during the Hutchinson's model application, especially in the last decades when computing technique with computers did not develop so rapidly as at present. At last, back analysis for properties of the 1966 flow slide at Aberfan, South Wales is done to test the model and the program, whose results are compared with those as reported by Hutchinson (Can. Geotech. J. 23(2):115-126, 1986). The results show that the program developed by the authors makes the application of Hutchinson's model more correct and easier.