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.     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models

Past studies consistently indicate measurable local associations between alcohol distribution and the incidence of violence. These results, coupled with measurements of spatial correlation, reveal the importance of spatial analysis in the study of the interaction of alcohol and violence. While studies increasingly incorporate spatial correlation among model residuals to improve precision and reduce bias, to date, most analyses assume associations that are constant and independent of location, an assumption coming under increasing scrutiny in the quantitative geography literature. In this paper, we review and contrast two approaches for the estimation of and inference for spatially heterogeneous effects (i.e., associative factors whose impacts on the outcome of interest vary throughout geographic space). Specifically, we provide an in-depth comparison of “geographically weighted regression” models (allowing covariate effects to vary in space but only allowing relatively ad hoc inference) with “variable coefficient” models (allowing varying effects via spatial random fields and providing model-based estimation and inference, but requiring more advanced computational techniques). We compare the approaches with respect to underlying conceptual structures, computational implementation, and inferential output. We apply both approaches to violent crime, illegal drug arrest, and alcohol distribution data from Houston, Texas and compare results in light of the differing methodological structures of the two approaches.     

Jökulhlaup-related Ice Fracture and Supraglacial Water Release During the November 1996 Jökulhlaup, Skeiðarárjökull, Iceland

During the initial stages of the November 1996 jökulhlaup at Skeiðarárjökull, Iceland, floodwaters burst onto the glacier surface via a series of fractures. This supraglacial drainage led to the formation of a number of distinct ice surface depressions, one of which is investigated in detail. The morphology and structural characteristics of this feature are described, as well as the sedimentology of an associated assemblage of debris-filled fractures. This work suggests that debris-charged subglacial floodwaters travelled up to the glacier surface, where supraglacial flow occurred initially via an extensive network of fractures, orientated parallel to the glacier margin. Supraglacial discharge became progressively more focused into a series of discrete outlets, leading to the mechanical erosion of a number of depressions on the glacier surface. The associated transfer of subglacially derived floodwaters to high levels within the glacier resulted in the rapid entrainment of large volumes of sediment which may influence the patterns, processes and products of ice-marginal sedimentation in the future.     

A morphological, sedimentological and geophysical investigation of the Woore Moraine, Shropshire, England

The Bar Hill-Whitchurch-Wrexham Morainic Complex is a large-scale glacial landform thought to represent either the maximum extent or the re-advance of the British-Irish Ice Sheet during the Late Devensian. The origin of the moraine remains uncertain as its key characteristics have not been studied in detail due to a lack of exposures from which its large-scale structure can be determined. The development of new technologies has enabled detailed examination of the topography and internal structure of such large-scale landforms. This paper describes a multi-disciplinary approach involving digital geomorphological mapping using enhanced resolution NextMAP™ digital surface models, geophysical imaging (electrical resistivity tomography) and conventional sedimentological analyses. This combination of techniques is useful for elucidating the origin of a large glacial landform in a region of poor exposure. Digital elevation models such as NextMAP™ offer an efficient and accurate method for landform-mapping, whilst electrical resistivity tomography was able to map the major constituent sediments of the moraine, which had in turn been identified in the single exposure available. Additional geophysical techniques should however be applied to provide further structural data and thereby enable a more detailed interpretation of the moraine's internal structure. Preliminary findings indicate that the moraine is a glaciotectonic landform composed of diamicton and glaciofluvial sediments, an origin consistent with recent suggestions that the Cheshire Plain contained an active ice lobe during the last glacial maximum.     

Environmental isotope investigation for the identification of source of springs observed in the hillock on the left flank of Gollaleru Earthen Dam,Andhra Pradesh,India

A hydrometric, hydrochemical and environmental isotopic study was conducted to identify the source and origin of observed springs on the foot of the hillock abutting the left flank of the Gollaleru earthen dam, Nandyal, Andhra Pradesh, India. Water samples (springs, reservoir water and groundwater) in and around the dam area were collected and analyzed for environmental isotopes (\(\updelta ^{18}\!\hbox {O}\), \(\updelta ^{2}\hbox {H}\) and \(^{3}\hbox {H}\)) and hydrochemistry. Reservoir level, spring discharges and physico-chemical parameters (temperature, electrical conductivity, pH, etc.) were monitored in-situ. Isotopic results indicated that the source of springs is from the Owk reservoir and groundwater contribution to the springs is insignificant. Based on hydrometric observations, it is inferred that the springs might be originated from the reservoir level of 209 m amsl. It is found that the lower spring discharges were derived from diffuse sources (seepage) which could be a mixture of reservoir water and the groundwater, while the relatively higher spring discharges were resulted from concentrated sources (leakage) from the reservoir. Thus, the study portraits the usefulness of isotope techniques in understanding the dam seepage/leakage related problems.     

Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers

This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN (R ² = 0.958, RMSE = 0.0698), ANFIS (R ² = 0.648, RMSE = 6.654), and GEP (R ² = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.