Exploration
Ali Aalianvari; shirin Jahanmiri; malihehe Abbaszadeh
Abstract
Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and ...
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Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and subsequent operational phases. Groundwater inflows, often perceived as rare geological hazards, can induce instability in the surrounding rock formations, leading to severe consequences such as injuries, fatalities, and substantial financial expenditures. The primary objective of this research is to explore the application of machine learning techniques to identify the most accurate method of forecasting tunnel water seepage. The prediction of water loss into the tunnel during the forecasting phase employed a tree equation based on gene expression programming (GEP). These results were compared with those obtained from a hybrid model comprising particle swarm optimization (PSO) and artificial neural networks (ANN). The Whale Optimization Algorithm (WOA) was selected and developed during the optimization phase. Upon contrasting the aforementioned methods, the Whale Optimization Algorithm demonstrated superior performance, precisely forecasting the volume of water lost into the tunnel with a correlation coefficient of 0.99. This underscores the effectiveness of advanced optimization techniques in enhancing the accuracy of groundwater inflow predictions and mitigating potential risks associated with tunneling activities.
V. Mwango Bowa; W. Samiselo; E. Manda; Y. Lei; W. Zhou; A. Shane; S. Chinyanta
Abstract
The influence of variable groundwater has been overlooked in the available literature. Yet, wedge failure induced by variable groundwater is still commonly experienced in sedimentary rock formation in many commercial dams, highways, and surface mine slopes around the world. In this article, a robust ...
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The influence of variable groundwater has been overlooked in the available literature. Yet, wedge failure induced by variable groundwater is still commonly experienced in sedimentary rock formation in many commercial dams, highways, and surface mine slopes around the world. In this article, a robust analytical model for stability analysis of the rock slopes subjected to wedge slope failure induced by variable groundwater is presented. This involves modifying the existing analytical model for estimating the safety factor of the rock slope subjected to wedge failure by incorporating the effects of variable groundwater. The proposed analytical model is validated using a numerical simulation model using the Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D) software. Furthermore, a real wedge slope instability at the Chingola Open-Pit Mine (COP F&D) induced by the presence of variable groundwater case history is studied in order to illustrate the effectiveness of the presented analytical model. The investigation results indicate that the presence of variable groundwater has a direct impact on the computed factor of safety of the rock slope subjected to wedge failure. The results obtained entail that the presented analytical model can provide a robust analytical model for the stability analyses of the rock slope subjected to wedge failure considering the presence of variable groundwater.
Z. Bayatzadeh Fard; F. Ghadimi; H. Fattahi
Abstract
Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization ...
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Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization (ANN-BBO), and multi-output adaptive neural fuzzy inference system (MANFIS) to estimate the distribution of heavy metals in groundwater of Lakan lead-zinc mine is demonstrated.For this purpose, the contamination groundwater resources were determined using the existing groundwater quality monitoring data, and several models were trained and tested using the collected data to determine the optimum model that used three inputs and four outputs. A comparison between the predicted and measured data indicated that the MANFIS model had the mostpotential to estimate the distribution of heavy metals in groundwater with a high degree of accuracy and robustness.
M. Sakizadeh; R. Mirzaei
Abstract
The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total ...
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The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total hardness, Ca, Mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, Fe, Mn, Cu, and Cr(VI) from 41 wells and springs were used during an eight-year time period (2006 to 2013). The cluster analysis was used, leading to a dendrogram that differentiated two distinct groups. The factor analysis extracted eight factors accumulatively, accounting for 90.97% of the total variance. Thus the variations in 17 variables could be covered by just eight factors. K-NN and SVMs were applied for the classification of the aquifer under study. The results of SVMs indicated that the best performed model was related to an exponent of degree one with an accuracy of 94% for the test data set, in which the sensitivity and specificity were 1.00 and 0.87, respectively. In addition, there was no significant difference among the results of different kernels, indicating that an acceptable result can be achieved by selecting the optimum parameters for a kernel. The results of K-NN showed roughly a lower efficiency compared with those of SVMs, where the sensitivity and specificity was reduced to 0.90 and 0.88, respectively, although the accuracy of the model was 93%. A sensitivity analysis was performed on the groundwater quality variables, suggesting that calcium next to nitrate were the most influential parameters in the classification of this aquifer.
C. Bempah; H-J. Voigt; A. Ewusi
Abstract
The focus of this research work is on the determination of the impact of mining on the groundwater quality in the historical mining region of SW Ashanti region in Ghana. This work describes the characteristics of the groundwater chemistry and pollution of the aquifer in the gold-ore bearing formation, ...
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The focus of this research work is on the determination of the impact of mining on the groundwater quality in the historical mining region of SW Ashanti region in Ghana. This work describes the characteristics of the groundwater chemistry and pollution of the aquifer in the gold-ore bearing formation, which is highly weathered and fractured. The fractures control the permeability and depth of the groundwater within the studied area. The concentrations of the major ions and trace elements (As, Fe, Cu, Mn, and Zn) present are determined in 63 groundwater wells at dry and wet seasons. The results obtained showed that the concentrations of these ions and elements were below the World Health Organization (WHO) guideline values for drinking water. However, concentrations of the As and Fe ions were very high above the guideline values. The wells with high As and Fe concentration levels might be located at an apparent rock fractured zone that extends to a nearby mine. Such fractured zones allow groundwater to move more rapidly away from a mine, creating more severe mine-drainage pollution in their paths. The results obtained from this study suggested a possible risk to the population of the studied area, given the toxicities of the As and Fe ions, and the fact that for many people living in the studied area, groundwater is a main source of their water supply.
