Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R ² and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R ² and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.     

Volume,gravitational potential energy reduction,and regional centroid position change in the wake of landslides triggered by the 14 April 2010 Yushu earthquake of China

In recent years, earthquake-triggered landslides have attracted much attention in the scientific community as a main form of seismic ground response. However, little work has been performed concerning the volume and gravitational potential energy reduction of earthquake-triggered landslides and their severe effect on landscape change. This paper presents a quantitative study on the volume, gravitational potential energy reduction, and change in landscape related to landslides triggered by the 14 April 2010 Yushu earthquake. At least 2,036 landslides were triggered by the earthquake. A total landslide scar area of 1.194 km² was delineated from the visual interpretation of aerial photographs and satellite images and was supported by selected field checking. In this paper, we focus on possible answers to the following five questions: (1) What is the total volume of the 2,036 landslides triggered by the earthquake, and what is the average landslide erosion thickness in the earthquake-stricken area? (2) What are the elevations of all landslide materials in relation to pre- and post-landsliding? (3) How much was the gravitational potential energy reduced due to the sliding of these landslide materials? (4) What is the average elevation change caused by these landslides in the study area? (5) What is the vertical change of the regional centroid position above sea level, as induced by these landslides? It is concluded that the total volume of the 2,036 landslides is 2.9399?×?10⁶ m³. The landslide erosion thickness throughout the study area is 2.02 mm. The materials of these landslides moved from an elevation of 4,145.243 to 4,104.697 m, resulting in a decreased distance of 40.546 m. The gravitational potential energy reduction related to the landslides triggered by the earthquake was 2.9213?×?10¹² J. The average regional elevation of the study area is 4,427.160 m, a value consistent with the assumption that the accumulated materials were remained in situ. This value changes from 4,427.160 to 4,427.158 m with all landslide materials moved out of the study area, resulting in a reduction in elevation of 2 mm. Based on the assumption that all landslide materials moved out of the study area, the elevations of the centroid of the study area’s crust changed from 2,222.45967 to 2,222.45867 m, which means the centroid value decreased by 1 mm. This value is 0.001 mm when assuming that the materials were remained in situ, which is almost negligible, compared with the situation of “all landslide materials moved out of the study area.”     

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Effect of silica fume on compressive strength of oil-polluted concrete in different marine environments

In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.     

Full-scale experimental investigations on the response of a flooded passenger vehicle under subcritical conditions

Natural Hazards - Vehicles can be easily swept away by floodwaters once the flow velocity and depth reach certain critical limits, with probabilities toward fatality reported to be nearly 50%....