Investigation of Various Parameters Effect on Cerchar Abrasivity Index with PFC3D Modeling

The Cerchar test is one of the appropriate and routine tests for determining the rock abrasion; but as for the costs and pin wear measurement errors in laboratory procedures and lack of access to laboratory equipment, using of numerical modeling can lead to use of greater number of samples required during the course of mechanized excavation and reduce the costs and errors in the laboratory test. In this study, the Cerchar abrasivity test was modeled using PFC3D (Particle Flow Code in 3 Dimensions) software. In order to verify the simulation results, Cerchar laboratory test results obtained by Rostami (Rock Mech Rock Eng 47(5):1905–1919, 2014) were compared with the numerical modeling results. In modeling studies, the effects of some parameters such as apply load, test speed, pin hardness and scratching distance on pin wear were investigated. As a conclusion of the study, good agreement between modeling and experimental results was obtained for a given condition. As in the experiment with various loads in both laboratory tests and modeling, with increasing applied load the Cerchar abrasivity index also increased in the experiment with Rockwell hardness HRC (An abbreviation for Rockwell Hardness measured on the C scale. The Rockwell test determines the hardness by measuring the depth of penetration of an indenter under a large load compared to the penetration made by a preload, that on the C scale use from a indenter with 120° cone and 150 kgf load) 42.     

Leaching capabilities of iodine monochloride (ICl) and diethylenetriamine pentaacetic acid (DPTA) at different concentrations and pH for some major and trace metals from coal and coal fly ash

In the present study, coal from Chakwal (Pakistan) was leached with an aqueous solutions of iodine monochloride (ICl) and diethylenetriamine pentaacetic acid (DPTA) of different concentrations. The effect of stirring time, concentration and pH was studied on the leaching of different metals from coal. The physicochemical parameters indicated that the coal was of reasonably good quality. The results indicated that with increase in time duration, the extraction of metals increased. In most of the cases, metal concentration increased in the leachate with increasing the concentration of the leaching agents. DPTA was found to be the best leaching agent for most of the metals. Higher extraction of metals from coal fly ash indicated that coal organic matter has a pronounced effect on the leaching. Higher concentration of metals was extracted from virgin coal and coal fly ash at low pH (p?>?0.00) as compared to high pH. DPTA extracted metals in higher concentration from virgin coal and coal fly ash at low pH as compared to ICl. Based on the present study, the most leached metals were Fe, Cu, Mn and the least were Pb, Ni, Cd and Cr.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Uncertainties in Grout Penetrability Measurements; Evaluation and Comparison of Filter pump,Penetrability Meter and Short Slot

To measure grout penetrability in fractured hard rock, various measuring instruments have been developed over the years. Penetrability meter and Filter pump have been designed to use in both the lab and the field. Short slot has been applicable mainly in the lab due to its complexity. The fact, that these instruments have been built based on different assumptions, limitations, and test conditions, makes their results occasionally in contradict. Deficiency in design of the instruments as well as the methods of evaluating grout penetrability is additionally a basis for uncertainty in results. This study is an experimental effort to determine and thoroughly perceive the nature of the most governing uncertainties in grout penetrability measurements. The test apparatus, procedure, and method used to evaluate the grout penetrability in both Penetrability meter and Filter pump were thus modified. The aim was to control the corresponding uncertainties and make their limitations and test conditions as similar as possible with the ones in Short slot. The results suggested that to obtain a more realistic evaluation of the grout penetrability, measurement should be accomplished at both the high and the low pressures with sufficient grout volume using Short slot. Moreover, application of both Filter pump and Penetrability meter is no longer recommended due to the revealed uncertainties.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Geodynamic setting and emplacement of mylonitic granitoids within the North Golpayegan shear zone,Iran

The metamorphic complex of the North Golpayegan is part of the Sanandaj-Sirjan Zone. There are at least three distinct stages of deformation in this complex. Throughout the first stage, Paleozoic and Mesozoic sedimentary rocks have experienced regional metamorphism during Late Jurassic tectonic events related to the subduction of the Neo-Tethys oceanic lithosphere under the Iranian microcontinent. During the second deformation stage in the Late Cretaceous-Paleocene, the rocks have been mylonitized. The third stage of deformation in the region has led to folding and faulting superimposed on previous structures, and to exhumation of the metamorphic complex. This stage has determined the current morphology and N70E strike of the complex. The mylonitic zones of the second stage of deformation have been formed along the dextral transpressional faults. During the third stage of deformation and exhumation of the metamorphic complex, the mylonitic zones have been uplifted to the surface. The granitoids in the metamorphic complex have been injected along the extensional shear fractures related to the dextral transpressional displacements. The granitoids have been transformed into mylonites within the synthetic or antithetic shear zones. These granitoids are recognized as syncollision type (CCG) and have been formed at the end of orogenic events synchronous to the collision between the Arabian and the Iranian plates at the Late Cretaceous-Paleocene.     

Automatic Selection of Landmarks for Navigation Guidance

Although current navigation services provide significant benefits to people's mobility, the turn‐by‐turn instructions they provide are sometimes ineffective. These instructions require people to maintain a high level of attention and cognitive workload while performing distance or angle measurements on their own mental map. To overcome this problem, landmarks have been identified as playing a major role in turn‐by‐turn instructions. This requires the availability of landmarks in navigation databases. Landmarks are commonly selected manually, which involves time‐consuming and tedious tasks. Automatic selection of landmarks has recently gained the attention of researchers but currently there are only a few techniques that can select appropriate landmarks. In this article, we present a technique based on a neural network model, where both static and dynamic features are used for selecting landmarks automatically. To train and test this model, two labeling approaches, manual labeling and rule‐based labeling, are also discussed. Experiments on the developed technique were conducted and the results show that rule‐based labeling has a precision of approximately 90%, which makes the technique suitable and reliable for automatic selection of landmarks.     

Damage Estimation in Masonry Buildings Based on Excavation-Induced Ground Movements

Excavation-induced ground movements and the resulting damages to adjacent structures and facilities is a source of concern for excavation projects in urban areas. The concern will be even higher if the adjacent structure is old or has low strength parameters like masonry building. Frame distortion and crack generation are predictors of building damage resulted from excavation-induced ground movements, which pose challenges to projects involving excavations. This study is aimed to investigate the relation between excavation-induced ground movements and damage probability of buildings in excavation affected distance. The main focus of this paper is on masonry buildings and excavations stabilized using soil nail wall method. To achieve this purpose, 21 masonry buildings adjacent to 12 excavation projects were studied. Parametric studies were performed by developing 3D FE models of brick walls and excavations stabilized using soil nail wall. Finally, probability evaluations were conducted to analyze the outputs obtained from case studies. Based on the obtained results, simple charts were established to estimate the damage of masonry structures in excavation affected distance with two key parameters including “Displacement Ratio” and “Normalized Distance”. The results also highlight the effects of building distance from excavation wall on its damage probability.