Calculation of periodic roof weighting interval in longwall mining using finite element method

The state of periodic loading and the interval of periodic roof weighting have an important role in geomechanical stability and, hence, in the continuity of longwall mining operations. In this paper, the mechanism of roof caving in longwall mining—together with the effect of engineering and geomechanical properties of surrounding rock masses on the magnitude and timing of periodic loading—is studied. For this purpose, a longwall mine is first modeled using Phase2 software, and then, by simulating the roof caving process, the periodic roof weighting intervals is calculated. Based on the numerical modeling, the first roof weighting interval and the periodic roof weighting interval are calculated as 27.2 and 12.1 m, respectively. Sensitivity analysis is then applied to determine the effect of changes in the mechanical properties of the rock mass, especially in the main roof and immediate roof. The results of the analysis show that as GSI and quality of the immediate roof increases, the periodic roof weighting interval also increases. Hence, the applied algorithm in this research study can effectively be utilized to calculate the periodic roof weighting interval in the longwall mining method.     

From A to B,randomly: a point-to-point random trajectory generator for animal movement

For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines the concepts of random walks, space-time prisms, and the Brownian bridge movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-)continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

Airside velocity measurements over the wind-sheared water surface using particle image velocimetry

An experimental investigation of the airflow structure in the near surface region over the wind-sheared air–water interface is reported. The two-dimensional velocity fields in a plane perpendicular to the water surface were measured using particle image velocimetry (PIV) technique over a wind speed range from 1.5 to 4.4 m s⁻¹. The results show a reduction in the mean velocity magnitudes and the tangential stresses when gravity waves appear on the surface. An enhanced vorticity layer was observed immediately above the water surface that extended to a height of approximately 2 cm. The vorticity was enhanced by an order of magnitude, and the energy dissipation rate was enhanced by a factor of 7 in this layer at all wind speeds. The vertical profiles of Reynolds stress, energy production, and dissipation indicate the contribution of surface waves in the enhanced transfer of momentum and energy between the two fluids. The results in this study show that the flow dynamics in a layer immediately adjacent to the water surface whose thickness is of the order of the significant wave height is significantly different from that at greater heights. Thus, it is concluded that the quantitative investigation of the flow in the immediate vicinity of the interface is vital for an improved understanding of the heat, mass, and momentum exchange between air and water. The present study demonstrates that PIV is an effective technique to accurately measure the velocity fields in this region.     

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.     

Wetter or colder during the Last Glacial Maximum? Revisiting the pluvial lake question in southwestern North America

Well-preserved shorelines in Estancia basin and a relatively simple hydrologic setting have prompted several inquiries into the basin's hydrologic balance for the purpose of estimating regional precipitation during the late Pleistocene. Estimates have ranged from 86% to 150% of modern, the disparity largely the result of assumptions about past temperatures. In this study, we use an array of models for surface-water runoff, groundwater flow, and lake energy balance to examine previously proposed scenarios for late Pleistocene climate. Constraints imposed by geologic evidence of past lake levels indicate that precipitation for the Last Glacial Maximum (LGM) may have doubled relative to modern values during brief episodes of colder and wetter climate and that annual runoff was as much as 15% of annual precipitation during these episodes.