Papers by Manoj Khandelwal
Sustainability
According to chaos theory, some underlying patterns can disclose the order of disordered systems.... more According to chaos theory, some underlying patterns can disclose the order of disordered systems. Here, it has been discussed that intermittency of rough rock fractured surfaces is an orderable disorder at intermediate length scales. However, this kind of disorder is more complicated than simple fractal or even multi-scaling behaviours. It is planned to deal with some multifractal spectra that systematically change as a function of the analysed domain. Accordingly, some parameters are introduced that can perfectly take into account such systematic behaviour and quantify the intermittency of the studied surfaces. This framework can be used to quantify and model the roughness of fractured surfaces as a prerequisite factor for the analysis of fluid flow in rock media as well as the shear strength of rock joints. Ultimately, the presented framework can be used for analysing the intermittency of time series and developing new models for predicting, for instance, seismic or flood events i...
Water
Roughness of rock fractured surfaces is one of the most important factors controlling fluid flow ... more Roughness of rock fractured surfaces is one of the most important factors controlling fluid flow in rock masses. Roughness quantification is of prime importance for modelling the flow of ground waters as well as reservoir fluid mechanics. In this study, with the aid of high-resolution 3D X-ray CT scanning and image processing techniques, the roughness of four different rock types is reconstructed with a resolution of 16.5 microns. Moreover, the correlation and structure functions are used to analyse height fluctuations as well as statistical intermittency of the studied rock fractured surfaces. It is observed that at length scales smaller than a critical length scale, fractures surfaces are correlated and show multifractality. Monofractals are neither intermittent nor correlated; hence, a meaningful link between statistical intermittency and the correlation function of multifractals is expected. However, a model that considers this relationship and predicts multifractal spectra of d...
AIMS Geosciences
The blasting operation is an integral part of mines, and it is still being used as the most econo... more The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting pra...
All Days
ABSTRACT: It is of crucial significance to study the effect of loading rate on the behaviour of r... more ABSTRACT: It is of crucial significance to study the effect of loading rate on the behaviour of rock because engineering structures are subjected to multiple loading conditions in their entire life. Although rock behaviour under single loading rate has been widely studied but very limited research has been conducted to study the performance of rock strength subjected to multiple loading conditions. This paper presents an experimental study of the effects of single and multiple strain rates on Sandstone samples. The first set of samples was tested at constant strain rates until failure to determine the peak uniaxial compressive strength (UCS). For the second set of samples, the first strain rate was applied to the sample up to a predetermined load, and then the second strain was initiated to find out the influence of multiple loading rates on the UCS of rock samples. 1. INTRODUCTION Geo-mining engineering deals with the extraction of minerals from the earth’s crust with the applicati...
Energies
Ever since the introduction of lithium-ion batteries (LIBs) in the 1970s, their demand has increa... more Ever since the introduction of lithium-ion batteries (LIBs) in the 1970s, their demand has increased exponentially with their applications in electric vehicles, smartphones, and energy storage systems. To cope with the increase in demand and the ensuing environmental effects of excessive mining activities and waste production, it becomes crucial to explore ways of manufacturing LIBs from the resources that have already been extracted from nature. It is possible by promoting the re-usage, refurbishing, and recycling of the batteries and their constituent components, rethinking the fundamental design of devices using these batteries, and introducing the circular economy model in the battery industry. This paper through a literature review provides the current state of CE adoption in the lithium-ion battery industry. The review suggests that the focus is mostly on recycling at this moment in the battery industry, and a further understanding of the process is needed to better adapt to o...
Natural Resources Research
Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it ... more Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output param...
Lecture Notes in Civil Engineering
Advances in Civil Engineering
A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of bot... more A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle-to-moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android appl...
Neural Computing and Applications, 2022
IEEE Access, 2021
Rock-burst is a common failure in hard rock related projects in civil and mining construction and... more Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.
Journal of Rock Mechanics and Geotechnical Engineering, 2021
Engineering with Computers, 2021
In recent years, block caving has drawn the attention of many mine enterprises due to the admired... more In recent years, block caving has drawn the attention of many mine enterprises due to the admired extraction rate and lower cost, which can exploit the materials via gravity inflow. At the same time, the limitation of this advanced method cannot be underestimated easily, such as surface subsidence and boulder, usually, the latter leads to the frequent secondary blast and damage of bottom structure. Thus, it is significant and crucial to evaluate the fragmentation before the implement of this method. But, traditional fragmentation assessment model suffers from the complex process of modeling and simulation. In this study, a hybrid model consists of unascertained measurement theory and information entropy was constructed to meet the requirements of this prospective mining method. Considering the influence of various parameters on rock fragmentation at the same time, twenty-three factors (i.e., uniaxial compressive strength, modulus ratio, fracture frequency, aperture, persistence, joint orientation, roughness, infilling, weathering, in situ stresses, stress orientation, stress ratio, underground water, fine ratio, hydraulic radius, undercut height, draw column height, draw points geometry, draw rate, multiple draw interaction, air gap height, broken ore density and undercut direction) were chosen to extract the main characteristics of rock mass samples from the two different mines, namely Reserve North (Chile), Diablo Regimiento (Chile) and Kemess mine (Canada). A new membership function (logarithm curve) was added to eliminate uncertainty results from the low level of knowledge about rock mass properties. Then, information entropy was performed to quantify the impacts of individual index. A credible degree identification criterion (Rη) was also applied to review the sample attributes qualitatively. Ultimately, degree of fragmentation of the three samples was judged easily on the basis of a composite measurement vectors and Rη. The evaluation results showed that the fragmentation grades of Reserve North, Diablo Regimiento and Kemess mine, separately, were “Good”, “Medium” and “Good”. With regard to the excellent performance of this hybrid model, it can be seen as a reliable approach to describe the fragmentation potential during the ore extraction using block caving mining method.
Underground Space, 2020
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parame... more The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R 2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R 2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
Natural Resources Research, 2019
Ground vibration (PPV) is one of the hazard effects induced by blasting operations in openpit min... more Ground vibration (PPV) is one of the hazard effects induced by blasting operations in openpit mines, which can affect the surrounding structures, particularly the stability of benches and slopes in open-pit mines, and impact underground water, railway, highway, and puzzling for neighboring communities. Therefore, controlling, accurate prediction, and mitigating blast-induced PPV are necessary. This study contributed a new computational model in predicting blast-induced PPV for the science community and practical engineering with high accuracy level. In this study, a novel hybrid artificial intelligence model based on the hierarchical k-means clustering algorithm (HKM) and artificial neural network (ANN), namely a HKM-ANN model, was considered and proposed for predicting blast-caused PPV in openpit mines. Accordingly, input data were first clustered by the HKM algorithm, and then, the ANN models were developed based on the obtained clusters. For this aim, 185 blasting events were collected and analyzed. A hybrid model based on fuzzy c-means clustering (FCM) and support vector regression (SVR), i.e., FCM-SVR model, which was proposed by previous authors was also applied for comparison of results with our proposed HKM-ANN model. In addition, an empirical method, several ANN and SVR models (without clustering), FCM-ANN, and HKM-SVR were developed for comparison purposes. For measuring the performance of the improved models, coefficient determination (R 2), root-mean-square error, and variance account for were used as the performance indicators. The results show that the HKM algorithm played a significant role in improving the accuracy of the ANN models. The proposed HKM-ANN model was the most superior model in estimating PPV caused by blasting operations in this study.
Rock Mechanics and Rock Engineering, 2016
Flyrock is considered as one of the main causes of human injury, fatalities, and structural damag... more Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.
Geotechnical and Geological Engineering, 2015
The purpose of this paper is to provide a proper, practical and convenient drilling rate index (D... more The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were conducted in the laboratory. In addition, the relevant strength properties i.e. uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) were determined and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R 2), root mean square error (RMSE) and variance account for (VAF) were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
International Journal of Earth Sciences, 2010
The main objective of this study was to establish statistical relationship between Schmidt hammer... more The main objective of this study was to establish statistical relationship between Schmidt hammer rebound numbers with impact strength index (ISI), slake durability index (SDI) and P-wave velocity. These are important properties to characterize a rock mass and are being widely used in geological and geotechnical engineering. Due to its importance, Schmidt hammer rebound number is considered as one of
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Papers by Manoj Khandelwal