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.     

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 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.     

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.     

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.     

A prediction method using grey model for cumulative plastic deformation under cyclic loads

Being able to predict cumulative plastic deformation of soil under cyclic loading has been quite a popular subject for researchers both in the past and at present. In this paper, a non-equal interval non-homogeneous exponential grey model NNGM (1, 1) for cumulative plastic deformation has been put forward by improving the traditional grey model GM (1, 1). The proposed model was evaluated by numerous experimental data of existing literatures and found to be valid for a wide range of soils. It is also valid for predicting long-term settlement of subgrade under subway loads and has a better performance than the traditional model GM (1, 1) as well as non-homogeneous exponential model NEM.     

Toward real-time three-dimensional mapping of surficial aquifers using a hybrid modeling approach

A hybrid modeling approach is proposed for near real-time three-dimensional (3D) mapping of surficial aquifers. First, airborne frequency-domain electromagnetic (FDEM) measurements are numerically inverted to obtain subsurface resistivities. Second, a machine-learning (ML) algorithm is trained using the FDEM measurements and inverted resistivity profiles, and borehole geophysical and hydrogeologic data. Third, the trained ML algorithm is used together with independent FDEM measurements to map the spatial distribution of the aquifer system. Efficacy of the hybrid approach is demonstrated for mapping a heterogeneous surficial aquifer and confining unit in northwestern Nebraska, USA. For this case, independent performance testing reveals that aquifer mapping is unbiased with a strong correlation (0.94) among numerically inverted and ML-estimated binary (clay-silt or sand-gravel) layer resistivities (5–20 ohm-m or 21–5,000 ohm-m), and an intermediate correlation (0.74) for heterogeneous (clay, silt, sand, gravel) layer resistivities (5–5,000 ohm-m). Reduced correlation for the heterogeneous model is attributed to over-estimating the under-sampled high-resistivity gravels (about 0.5 % of the training data), and when removed the correlation increases (0.87). Independent analysis of the numerically inverted and ML-estimated resistivities finds that the hybrid procedure preserves both univariate and spatial statistics for each layer. Following training, the algorithms can map 3D surficial aquifers as fast as leveled FDEM measurements are presented to the ML network.     

Drought characterization using a new copula-based trivariate approach

Meteorological drought is a natural climatic phenomenon that occurs over various time scales and may cause significant economic, environmental and social damages. Three drought characteristics, namely duration, average severity and peak intensity, are important variables in water resources planning and decision making. This study presents a new method for construction of three-dimensional copulas to describe the joint distribution function of meteorological drought characteristics. Using the inference function for margins, the parameters for six types of copulas were tested to select the best-fitted copulas. According to the values of the log-likelihood function, Galambos, Frank and Clayton were the selected copula models to describe the dependence structure for pairs of duration–severity, severity–peak and duration–peak, respectively. Trivariate cumulative probability, conditional probability and drought return period were also investigated based on the derived copula-based joint distributions. The proposed model was evaluated over the observed data of a Qazvin synoptic station, and the results were compared with the empirical probabilities. For measuring the model accuracy, R ², root mean square error (RMSE) and the Nash–Sutcliffe efficiency (NSE) criteria were used. Results indicated that R ², RMSE and NSE were equal to 0.91, 0.098 and 0.668, respectively, which demonstrate sufficient accuracy of the proposed model. Drought probabilistic characteristics can provide useful information for water resource planning and management.     

Life-time prediction for rocks under static compressive and tensile loads: a new simulation approach

The life time or time to failure of rocks under load is governed by microstructural defects, like microcracks, voids etc. The life time can be predicted either by empirical exponential laws or physical laws based on damage and fracture mechanics. The proposed numerical model is based on subcritical crack growth using the linear elastic fracture mechanical approach and is implemented as a numerical cellular automate. The algorithm considers both tensile and shear fracturing. Each cell contains a microcrack of random length according to a given probability function. Fracture growth is controlled by the Charles equation. Macroscopic cracks are the results of the coalescence of growing microcracks. Within the numerical approach elasto-plastic stress redistributions take place. If the stress intensity factors have reached the critical values or the microcrack has reached the zone dimension, the zone is considered as fractured and residual strength values are assigned. The proposed approach was applied to rock samples under uniaxial compressive and tensile loads (creep tests). Successful results were obtained in respect to the predicted life time, damage evolution and the fracture pattern. Conclusions for further improvements and extensions of this methodology were drawn.     

A RBFNN-based method for the prediction of the developed height of a water-conductive fractured zone for fully mechanized mining with sublevel caving

The movement and failure of overlying strata induced by underground coal mining cause “three zones,” including the caving zone, the water-conductive fractured zone, and the sagging zone from the bottom up. For knowledge about the height of the water-conductive fractured zone, there has been no empirical or theoretical formulae for thick coal seam using fully mechanized longwall mining with sublevel caving. This paper presents a methodology of determining the height of the water-conductive fractured zone based on the radial basis function neural networks (RBFNN) model in MATLAB software. Before modeling, the relationship between the height of the water-conductive fractured zone and mining thickness, the lithologic character of the overburden and its composite structures, and workface parameters was studied. After that, 32 and 7 measured data were used as training and testing samples, respectively. It has been found that the average relative error is 6% and the maximum relative error is 10% for 7 test samples by comparing actual results with predicted results. The model was applied to the no. 31503 workface in the Gaozhuang coal mine for safety evaluation. The predicted value is 59.6 m, and the measured value is 55.9 m. The RBF-based model shows much better performance than empirical formulae in the Regulations for Coal Mining and Coal Pillar Design under Buildings, Water-bodies, Railways and Main Shafts for the prediction of the height of the water-conductive fractured zone for fully mechanized mining with sublevel caving.     

A new combined framework for sustainable development using the DPSIR approach and numerical modeling

Relying on the conceptual DPSIR framework and MODFLOW analysis,this study used a mixed approach to produce groundwater resource management solutions for the Najafabad area in central Iran.According to DPSIR results,agricultural activities put the highest pressure on groundwater resources in this region.The results showed the effectiveness of reducing water withdrawal over 30 years in maintaining the aquifer in a state of equilibrium.The best scenario consisted of cutting down extraction by 10% over the said period.Output maps of the water table rise at the Najafabad aquifer clearly showed that the groundwater management scenario involving a 10% reduction of water withdrawal was the most effective solution,as it would raise the water level by 6.7 m.Regarding other scenarios,reducing cultivated area by 20% was found to raise the water table by 5.03 m on average,while cutting down water withdrawal by 5% increased the water table by 3.6 m,and a 10% reduction of the cultivated area resulted in a 1.85 m rise.The combined model proposed here can be used for similar aquifers and can aid decision-makers and managers.     

A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey

This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R²). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions.     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.     

Conceptual hydrosalinity model for prediction of salt load from wastewater flows into soil and ground water

Dynamic hydrosalinity models are available, but are not used extensively on a large scale soil which receives wastewater from industrial areas, partly because adequate database are expensive to be obtained. Thus, for this reason, there is an urgent need to assess the salt and other pollutant loads collected in wastewater flows into the soil and/ or ground water systems. A conceptual hydrosalinity model was used on two major underlying principals of mass balance and steady state. This model was initially tested on the 4,117 km² plains west of the Yazd-Ardakan district in the central part of Iran. This model was used at a time when the soil and ground water salinity problem was serious due to the high shortage of water. It was possible to calibrate the model with ± 2 % of the flow volume and total dissolved solids of the industrial wastewater discharge from over 2,000 factories. The verification results were 98 % of the measured values. Moreover, this model was tested for the verification of the model data from the analysis of 36 wells’ water in the area where industrial wastewater discharge was used. The results showed that most of the indices of total dissolved solids, total suspended solids, biological oxygen demand and chemical oxygen demand are above standard levels. The results of the model can be used for the management practice of the reduction of salt pollutant load in the area to achieve sustainable development for location of industries in the study area.