Kunwar Mrityunjai Sharma

Bio: Kunwar Mrityunjai Sharma is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Flow (mathematics) & Fluid dynamics. The author has an hindex of 4, co-authored 5 publications receiving 140 citations.

Evaluating the modulus of elasticity of soil using soft computing system

Abstract: The elastic modulus of soil is a key parameter for geotechnical projects, transportation engineering, engineering geology and geotechnics, but its estimation in laboratory or field is complex and difficult task due to instrument handling problems, high cost, and it being a time consuming process. For this reason, the predictive models are useful tool for indirect estimation of elastic modulus. In this study, to determine the modulus of elasticity of soil, a rapid, less expensive, and reliable predictive model was proposed using artificial neural network (ANN). For this purpose, a series of laboratory tests were conducted to estimate the index properties (i.e., particle size fractions, plastic limit, liquid limit, unit weight, and specific gravity) and the modulus of elasticity of soils collected from Mahabaleshwar (Maharashtra), Malshej Ghat (Maharashtra), and Lucknow (Uttar Pradesh), in India. The input parameters in the developed ANN model are gravel, sand, fines, plastic limit, liquid limit, unit weight, and specific gravity, and the output is modulus of elasticity. The accuracy of the obtained ANN model was also compared with the multiple regression model based on coefficient of determination (R 2), the mean absolute error (MAE), and the variance account for (VAF). The ANN predictive model had the R 2, MAE, and VAF equal to 0.98, 5.07, and 97.64 %, respectively, superseding the performance of the multiple regression model. The performance comparison revealed that ANN model has more reliable predictive performance than multiple regression and it can be applied for predicting the modulus of elasticity of soil with more confidence. Thus, the result of the present study indicates that the modulus of elasticity of soil can reliably be estimated from the indirect method using ANN analysis with greater confidence.

Empirical Slope Stability Assessment Along the Road Corridor NH-7, in the Lesser Himalayan

Abstract: Stability assessment in active Himalayan region is very crucial, as the area has many phase of deformation and complex geological condition. Further the development activities and road settlement enhanced the slopes instabilities issues. The present article emphasizes empirical slope stability assessment to characterize the rock mass, failure types and stability analysis. For this eighteen different vulnerable slopes have been recognized between Devprayag and Srinagar in Uttarakhand (Lesser Himalayan), along the NH-7, which goes to Holy shrines Shri Kedarnath temple. Geometrical and kinematic investigation for each slopes have been assessed for structurally controlled rock mass which shows mostly wedge type of failure with planar and toppling failure. The empirical analysis between rock mass rating (RMR) and Geological Strength Index (GSI) characteristic show a linear relationship for particular rock type. The road corridor has generally fair to good variety of rock mass characteristic as from RMR description. The new system as Qslope and slope mass rating (SMR) system have been associated to distinguish the stability problem, which described comparatively same type of stability. The designed slope angle β for the Qslope value has also been evaluated for different probability of failures for the stability of each cut slope. This new Qslope empirical method compared with SMR method for fractured rock slopes will give the better perception to care the stability issue with simple, reasonable and prompt approach to design and develop the road corridor in hilly region.

Parametric study of factors affecting fluid flow through a fracture

Abstract: Understanding the flow behavior through fractures is critically important in a wide variety of applications. In many situations, the fluid flow can be highly irregular and non-linear in nature. Numerical simulation can be employed to simulate such conditions which are difficult to replicate in laboratory experiments. Therefore, a parametric study has been conducted on the fluid flow through micro-fracture over a large range of inlet pressure, fluid density, fluid viscosity, temperature, joint roughness coefficient (JRC), and fracture using finite element analysis. Irregular fracture profiles were created using Barton’s joint roughness coefficient. The Navier-Stokes (NS) equation was used to simulate the flow of water in those micro-fractures. The result showed that the fracture, fluid, and ambient conditions have a wide and varied effect on the fluid flow behavior. The interrelationship between these parameters was also studied. The model simulation provided result in the form of velocity and pressure drop profile, which can be used to determine the behavior of flow under different condition. The volumetric flow was calculated for each condition and has been plotted against the corresponding parameter to study the interrelationship.

Apparent flow-dimension approach to the study of heterogeneous fracture network systems

Abstract: The generalized radial flow (GRF) model in well-test analysis employs noninteger flow dimensions to represent the variation in flow area with respect to radial distance from a borehole. However, the flow dimension is influenced not only by changes in flow area, but also by permeability variations in the flow medium. In this report, the flow dimension from the combined effect of flow dimensionality and permeability/conductance variation is interpreted and referred to as apparent flow dimension (AFD). AFD is determined using the second derivative of the drawdown-time plot from pressure transient testing, which may have varied noninteger values with time. A systematic set of investigations is presented, starting from idealized channel networks in one, two and three dimensions (1D, 2D and 3D, respectively), and proceeding to a case study with a complex fracture network based on actual field data. Interestingly, a general relation between the AFD upsurge/dip and the conductance contrast between adjacent flow channels is established. The relation is derived from calculations for 1D networks but is shown to be useful even for data interpretation for more complex 2D and 3D cases. In an application to fracture network data at a real site, the presence of flow channel clusters is identified using the AFD plot. Overall, the AFD analysis is shown to be a useful tool in detecting the conductance/dimensionality changes in the flow system, and may serve as one of the different data types that can be jointly analysed for characterizing a heterogeneous flow system.

VALIDITY OF CUBIC LAW FOR FLUID FLOW IN A DEFORMABLE ROCK FRACTURE - eScholarship

Abstract: The validity of the cubic law for laminar flow of fluids through open fractures consisting of parallel planar plates has been established by others over a wide range of conditions with apertures ranging down to a minimum of 0.2 µm. The law may be given in simplified form by Q/Δh = C(2b)3, where Q is the flow rate, Δh is the difference in hydraulic head, C is a constant that depends on the flow geometry and fluid properties, and 2b is the fracture aperture. The validity of this law for flow in a closed fracture where the surfaces are in contact and the aperture is being decreased under stress has been investigated at room temperature by using homogeneous samples of granite, basalt, and marble. Tension fractures were artificially induced, and the laboratory setup used radial as well as straight flow geometries. Apertures ranged from 250 down to 4µm, which was the minimum size that could be attained under a normal stress of 20 MPa. The cubic law was found to be valid whether the fracture surfaces were held open or were being closed under stress, and the results are not dependent on rock type. Permeability was uniquely defined by fracture aperture and was independent of the stress history used in these investigations. The effects of deviations from the ideal parallel plate concept only cause an apparent reduction in flow and may be incorporated into the cubic law by replacing C by C/ƒ. The factor ƒ varied from 1.04 to 1.65 in these investigations. The model of a fracture that is being closed under normal stress is visualized as being controlled by the strength of the asperities that are in contact. These contact areas are able to withstand significant stresses while maintaining space for fluids to continue to flow as the fracture aperture decreases. The controlling factor is the magnitude of the aperture, and since flow depends on (2b)3, a slight change in aperture evidently can easily dominate any other change in the geometry of the flow field. Thus one does not see any noticeable shift in the correlations of our experimental results in passing from a condition where the fracture surfaces were held open to one where the surfaces were being closed under stress.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Abstract: Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Abstract: Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

Abstract: Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various geotechnical engineering aspects such as slope stability analysis, pile and foundation engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in geotechnical engineering compared with other civil engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in geotechnical engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of geotechnical engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for geotechnical engineers.