Estimation of Hydraulic Property of an Unconfined Aquifer by GPR

Controlled water productions were performed at a water source area of Ulaanbaatar city, Mongolia to evaluate the effectiveness of ground penetrating radar (GPR) for detecting and monitoring dynamic groundwater movements in the subsurface and for estimating the hydraulic properties of the aquifer. Field experiments in Ulaanbaatar were carried out in 2001 and 2002. GPR data were acquired using 100 MHz antennas. This paper reports the results of GPR methods to monitor the groundwater migration caused by the pumping operation and GPR’s potential ability to estimate hydraulic properties of the aquifer. The GPR data sets were acquired very carefully by locating the antenna position accurately. The residual trace shows a feature that is a combination of the water level reflections acquired at two different times in the pumping test. It helped to determine travel time and its effective reflection point from the top of the water saturated zone. The residual wavelet varies versus offset from the pumping well for a given residual image. Common midpoint (CMP) data and velocity analysis indicated the depth of the water table and the water content in the unsaturated and saturated zone. Combining hydrogeologic data with quantitative information yielded by GPR data, hydraulic properties of the aquifer could be estimated by assuming a hydraulic model. It was concluded that GPR can be successfully employed to monitor groundwater migration and to estimate hydraulic properties of the aquifer.     

Use of time-lapse resistivity tomography to determine freshwater lens morphology

Time-lapse electrical resistivity tomography (TLERT) was used to determine the freshwater-lens morphology in local scale at Carey Island, Selangor, Malaysia. TLERT images with geological background formation history, borehole and hydraulic conductivity data were used to interpret the changes of morphology. Subsurface resistivity changes were measured by ABEM Terrameter SAS4000 and ES10-64 electrode selector. TLERT monitoring using resistivity images on tide increment shows the freshwater lens morphology exhibited a thin and thick layer side by side of 400 m length of resistivity survey line. The occurrences of heterogeneous alluvium sediments in local scale created the different variability of hydraulic conductivity. The asymmetry of the freshwater lens enabled the tide force increment to pass through the differences in hydraulic conductivity. This is a major factor determining the morphology of freshwater lens in local scale. The results can assist in planning a strategy for sustainable groundwater exploration of local scale at the limited recharge area.     

A study on the effective hydraulic conductivity of an anisotropie porous medium

Effective hydraulic conductivity of a statistically anisotropic heterogeneous medium is obtained for steady two-dimensional flows employing stochastic analysis. Flow equations are solved up to second order and the effective conductivity is obtained in a semi-analytic form depending only on the spatial correlation function and the anisotropy ratio of the hydraulic conductivity field, hence becoming a true intrinsic property independent of the flow field. Results are obtained using a statistically anisotropic Gaussian correlation function where the anisotropy is defined as the ratio of integral scales normal and parallel to the mean flow direction. Second order results indicate that the effective conductivity of an anisotropic medium is greater than that of an isotropic one when the anisotropy ratio is less than one and vice versa. It is also found that the effective conductivity has upper and lower bounds of the arithmetic and the harmonic mean conductivities.     

ANN model for predicting the impact of submerged aquatic weeds existence on the hydraulic performance of branched open channel system accompanied by water structures

The existence of hydraulic structures in a branched open channel system urges the need for considering the gradually varied flow criterion in evaluating the different hydraulic characteristics in this type of open channel system. Computations of hydraulic characteristics such as flow rates and water surface profiles in branched open channel system with hydraulic structures require tremendous numerical effort especially when the flow cannot be assumed uniform. In addition, the existence of submerged aquatic weeds in this branched open channel system adds to the complexity of the evaluation of the different hydraulic characteristics for this system. However, this existence of aquatic weeds can not be neglected since it is very common in Egyptian open channel systems. Artificial Neural Network (ANN) has been widely utilized in the past decade in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of submerged aquatic weeds existence on the hydraulic performance of branched open channel system. Specifically the current paper investigates a branched open channel system that consists of main channel supplies water to two branch channels that are infested by submerged aquatic weeds and have water structures such as clear over fall weirs and sluice gates. The results of this study showed that ANN technique was capable, with small computational effort and high accuracy, of predicting the impact of different infestation percentage for submerged aquatic weeds on the hydraulic performance of branched open channel system with two different hydraulic structures.     

Non Destructive Geophysical Monitoring of Water Content and Fluid Conductivity Anomalies in the Near Surface at the Border of an Agricultural

We use in this paper advanced geophysical techniques for the characterization and monitoring of subsurface properties such as porosity, water content and electrical conductivity of water. Ground Penetrating Radar (GPR) and electrical conductivity measurements were recorded monthly during one year at the border of a corn field. Velocity analyses of multioffset GPR data were conducted to determine total porosity and to monitor vertical transport of water from the soil surface to the water table. The use of novel and original techniques for GPR processing (GPR velocity estimation by the Common Reflection Surface (CRS) method, kriging applied to GPR velocity) improved the estimate and the resolution of GPR velocity maps compared with the classical Normal MoveOut (NMO) and the bi-linear interpolation. Electrical resistivities were used to determine the effective porosity. The combination of GPR and electrical data permitted to estimate the electrical conductivity of water and to highlight high conductivity zones, possibly due to contamination by agricultural fertilizers. Independent determinations (grain size fractions, electrical conductivity, major ion content of water samples and porosity) were obtained, that validate our geophysical investigation. This study demonstrates the efficiency of non destructive geophysical approaches for providing accurate models of water content, porosity and electrical conductivity of water down to a depth of several meters in a poorly conductive soil.     

基于ARIMA的盾构机液压推进系统数据预测方法研究

液压推进系统是盾构机的关键构成,承担着盾构机姿态控制、纠偏和同步前进等重要功能,以推进系统的运行数据为基础,精准预测数据的变化是分析、预测和避免盾构机产生安全问题的重要手段。基于随机时序分析法(Autoregressive Integrated Moving Average model, ARIMA)对盾构机液压推进系统数据进行预测研究。首先利用相关性分析方法,获得了与盾构机液压推进系统推进过程相关性较高的数据类别为掘进速度,基于该数据进行了自相关性的分析;之后,基于ARIMA方法,建立了盾构机液压推进系统ARIMA模型,并利用该模型进行了平稳性分析与贝叶斯信息准则;最后,基于优化模型分析比较了基于K-means的循环神经网络(Recurrent Neural Network, RNN)预测方法以及线性回归预测方法对数据预测的效果。研究表明,ARIMA模型下的线性回归方法能很好的预测盾构机液压推进系统数据变化趋势及异常数据预测,对盾构机的故障诊断及预测有重要的意义。     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

A novel approach based on artificial neural network for calibration of multi-hole pressure probes

Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.     

Classification-driven model selection approach of genetic programming in modelling of turning process

Turning is a widely used machining process, but the process complexity and uncertainty lead to empirical modelling techniques being preferred over physics-based models for predicting the process performance. The literature reveals that empirical methods such as artificial neural networks (ANN), support vector regression (SVR), regression analysis and fuzzy logic have been extensively applied in the modelling of turning process. The present work introduces genetic programming (GP) for the modelling of turning, but it is observed that the optimal models selected from the GP population based on training and validation errors do not perform well on testing data (unseen samples). Selecting the best GP model from the population of models is therefore a vital step. In view of this, the classification-driven model selection approach of GP (C-GP) is proposed in this paper. In this methodology, potential classification techniques such as Bayes multinomial, partitioning and regression trees, classification and regression trees and decision trees are integrated with GP to predict the class (best or bad) of the GP models. The model that is classified as the “best” by the most number of classification techniques is selected, and its performance is compared to those from ANN and SVR. It is found that the C-GP model has accuracy on par with ANN and gives satisfactory performance on testing data.     

Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network

It is difficult to predict when, where, and how long algal blooms will occur in a water body. The objectives of this study were to determine the factors affecting algal bloom and predict chlorophyll-a (Chl-a) levels in the reservoir formed by damming a river using an artificial neural network (ANN). The automatic water quality monitoring data [water temperature, pH, dissolved oxygen (DO), electric conductivity, total organic carbon (TOC), Chl-a, total nitrogen (T-N), and total phosphorus (T-P)], weather data (precipitation, temperature, insolation, and duration of sunshine) and hydrologic data (water level, discharges, and inflows) in the man-made Lake Juam during 2008–2010 were used to develop a model to predict Chl-a as an indirect measure of the abundance of algae. The ANN was trained using the collected data during 2008–2010 and the accuracy of the model was verified using the data collected in 2011. It was found that Chl-a concentration, TOC, pH and atmospheric and water temperatures were the most important parameters in predicting Chl-a concentrations. The Chl-a prediction was most influenced by the parameters showing the algal activities such as Chl-a, TOC and pH. Due to the relatively long hydraulic retention time of ∼131 days, the inflow and outflow did not affect the prediction much. Likewise, atmospheric and water temperatures did not respond to the change of the Chl-a concentration due to the lake’s relatively slow response to the temperature. Most importantly, T-N and T-P were not the major factors in predicting Chl-a levels at Lake Juam. The ANN trained with the time series data successfully predicted the Chl-a concentration and provided information regarding the principal factors affecting algal bloom at Lake Juam.     

Characterization and reconstruction of 3D stochastic microstructures via supervised learning

The need for computational characterization and reconstruction of volumetric maps of stochastic microstructures for understanding the role of material structure in the processing–structure–property chain has been highlighted in the literature. Recently, a promising characterization and reconstruction approach has been developed where the essential idea is to convert the digitized microstructure image into an appropriate training dataset to learn the stochastic nature of the morphology by fitting a supervised learning model to the dataset. This compact model can subsequently be used to efficiently reconstruct as many statistically equivalent microstructure samples as desired. The goal of this paper is to build upon the developed approach in three major directions by: (1) extending the approach to characterize 3D stochastic microstructures and efficiently reconstruct 3D samples, (2) improving the performance of the approach by incorporating user‐defined predictors into the supervised learning model, and (3) addressing potential computational issues by introducing a reduced model which can perform as effectively as the full model. We test the extended approach on three examples and show that the spatial dependencies, as evaluated via various measures, are well preserved in the reconstructed samples.     

Recent developments in modeling of heat transfer during TIG welding—a review

This paper is an attempt to list the recent developments in the area of arc welding heat transfer simulation. Fusion welding modeling is a broad area where a number of research groups were spending their efforts to get solutions for both research and industrial problems. Starting from fundamentals of arc physics, heat transfer, microstructure models, thermal stress, and modern techniques like pattern recognition comes into picture while considering the complete solution of welding-related problems. These areas are developing almost independently and there are only few efforts to couple them together as computational welding mechanics, which includes the computational fluid mechanics, magneto hydrodynamics, thermo mechanical problems, and computational material science. Here, an effort is made to list down major developments in this area and to plot the present state of research on the TIG welding heat transfer modeling by giving priority to last few years of research.     

An improved artificial neural network for laser welding parameter selection and prediction

In the laser welding production, the selection and prediction of welding parameters is essentially important to guarantee weld quality. Artificial neural networks (ANN), which perform a nonlinear mapping between inputs and outputs, are an alternative approach for developing welding parameter forecasting model. In this paper, in order to speed up the convergence and avoid local minimum of the conditional ANN, genetic algorithm simulated annealing (GASA) based on the random global optimization is inducted into the network training. By means of GASA method, weights and threshold of neural networks can be globally optimized with short training time. Meanwhile, the gray correlation model (GCM) is used as a pre-processing tool to simplify the original networks based on obtaining the main influence elements of network inputs. The GCM–GASA–ANN method combines the complementary features of three computational intelligence techniques and owns very good applicability. Through the simulation and analysis of an orthogonal experiment, the proposed method can be proved to have higher accuracy and to perform better than the traditional ANN to forecast the laser welding parameters.     

Application of ANN in the prediction of the pore concentration of aluminum metal foams manufactured by powder metallurgy methods

In this work, the effect of fabrication parameters on the pore concentration of aluminum metal foam, manufactured by the powder metallurgy process, has been studied. The artificial neural network (ANN) technique has been used to predict pore concentration as a function of some key fabrication parameters. Aluminum metal foam specimens were fabricated from a mixture of aluminum powders (mean particle size 60 μm) and NaCl at 10, 20, 30, 40(wt)% content under a pressure of 200, 250, and 300 MPa. All specimens were then sintered at 630°C for 2.5 h in argon atmosphere. For pore formation (foaming), sintered specimens were immersed into 70°C hot running water. Finally, the pore concentration of specimens was recorded to analyze the effect of fabrication parameters (namely, NaCl ratio, NaCl particle size, and compacting pressure) on the foaming behavior of compacted specimens. It has been recorded that the above-mentioned fabrication parameters are effective on pore concentration profile while pore diameters remain unchanged. In the ANN training module, NaCl content (wt)%, NaCl particle size (μm), and compacting pressure (MPA) were employed as inputs, while pore concentration % (volume) of compacts related to fabrication parameters was employed as output. The ANN program was successfully used to predict the pore concentration % (volume) of compacts related to fabrication parameters.     

Predicting the impact of vegetations in open channels with different distributaries’ operations on water surface profile using artificial neural networks

Most of the open water irrigation channels in Egypt suffer from the infestation of aquatic weeds, especially the submerged ones that cause numerous hydraulic problems for the open channels themselves and their water distributaries such as increasing water losses, obstructing water flow, and reducing channels’ water distribution efficiencies. Accurate simulation and prediction of flow behavior in such channels is very essential for water distribution decision makers. Artificial neural networks (ANN) have proven to be very successful in the simulation of several physical phenomena, in general, and in the water research field in particular. Therefore, the current study aims towards introducing the utilization of ANN in simulating the impact of vegetation in main open channel, which supplies water to different distributaries, on the water surface profile in this main channel. Specifically, the study, presented in the current paper utilizes ANN technique for the development of various models to simulate the impact of different submerged weeds’densities, different flow discharges, and different distributaries operation scheduling on the water surface profile in an experimental main open channel that supplies water to different distributaries. In the investigated experiment, the submerged weeds were simulated as branched flexible elements. The investigated experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results showed that the ANN technique is very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds’cases that were considered in the ANN development process     

Prediction of calcium concentration in human blood serum using an artificial neural network

A predictive method, based on artificial neural network (ANN) has been developed to study absorbance and pH effects on the equilibrium of blood serum. This strategy has been used to analyze serum samples and to predict the calcium concentration in blood serum. A dedicated data acquisition system is designed and fabricated using a LPC2106 microcontroller with light emitting diode (LED) as source and photodiode as sensor to measure absorbance and to calculate the calcium concentration. A multilayer neural network with back propagation (BP) training algorithm is used to simulate different concentration of calcium (Ca²⁺) as a function of absorbance and pH, to correlate and predict calcium concentration. The computed calcium concentration by neural network is quite satisfactory with correlations R² = 0.998 and 0.995, standard errors of 0.0127 and 0.0122 in validation and testing stages respectively. Statistical analysis are carried out to check the accuracy and precision of the proposed ANN model and validation of results produce a relative error of about 3%. These results suggest that ANN can be efficiently applied and is in good agreement with values obtained with the current clinical spectrophotometric methods. Hence, ANN can be used as a complementary tool for studying metal ion complexion, with special attention to the blood serum analysis.     

Notes on contributors

Various metallic pairs were tested under conditions of unlubricated solid contact. Experiments were conducted for repetitive impulsive and continuous sliding contact. Wide ranges of materials and conditions (nominal contact stress and relative transverse sliding velocity) and a variety of loading modes (pure normal impact at various frequencies, compound impact at various sliding velocities, and pure sliding under various stress levels) were explored.Particular attention was focused on the establishment of subsurface material zones developed in the tests, in situ. These zones exhibit dependences on velocity, stress, material, test duration and loading mode. The experimental findings, based on several analysis techniques, serve to characterize subsurface zone composition and morphology. Both surface and subsurface features were examined by optical and electron microscopy and analyzed by energy-dispersive X-ray techniques to allow interpretations concerning the role of external parameters, material transport and debris formation, as well as insight into operative mechanisms which act on specific materials under prescribed conditions to cause wear.     

The subsurface zone in magnesium alloy studied by positron annihilation techniques

This paper presents the positron lifetime and Doppler broadening of annihilation line studies of the subsurface region in a magnesium-based alloy which was exposed to dry sliding. The total range of the subsurface zone below the worn surface detected using these techniques was lower than 100 μm and was hardly affected by the applied load and sliding distance. The obtained results are significantly different from the results achieved for pure magnesium, thus alloying has considerable effect on the subsurface zone formation. The positron lifetime profile of the subsurface region was well correlated with the microhardness profile. The significant result was that the weak long-lived component indicated ortho-positronium formation has been found at the depth lower than 30 μm. This indicates the formation of voids below the worn surface.