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Sunil Khuntia

Bio: Sunil Khuntia is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Embedment & Multivariate adaptive regression splines. The author has an hindex of 6, co-authored 10 publications receiving 82 citations. Previous affiliations of Sunil Khuntia include National Institute of Technology, Rourkela.

Papers
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Journal ArticleDOI
TL;DR: In this article, different models are developed to estimate the compaction parameters of sandy soil using artificial neural network (ANN), least square support vector machine (LS-SVM), and multivariate adaptive regression splines (MARS).
Abstract: In this paper, different models are developed to estimate the compaction parameters of sandy soil using artificial neural network (ANN), least square support vector machine (LS-SVM), and multivariate adaptive regression splines (MARS). The experimental database of Mujtaba et al. (2013) is used for the analysis. The above techniques have been used to improve the regression results. The model equations are established and compared with the regression equation. The MARS model results found to be more accurate and it improved the coefficient of determination to more acceptable levels of 0·88 and 0·81 for the prediction of compaction parameters maximum dry density (γdmax) and optimum moisture content (ωopt), respectively. The results showed that variation between experimental and predicted values of γdmax is within ±4% and that of the ωopt is within ±13% at 95% confidence level. Sensitivity analysis is carried out to evaluate the parameters affecting the output.

34 citations

Journal ArticleDOI
TL;DR: In this article, numerical solutions have been obtained for the vertical uplift capacity of strip plate anchors embedded adjacent to sloping ground in fully cohesive soil under undrained condition, and the analysis was performed using finite element lower bound limit analysis with second order conic optimization technique.
Abstract: Numerical solutions have been obtained for the vertical uplift capacity of strip plate anchors embedded adjacent to sloping ground in fully cohesive soil under undrained condition. The analysis was performed using finite element lower bound limit analysis with second-order conic optimization technique. The effect of anchor edge distance from the crest of slope, angle and height of slope, normalized overburden pressure due to soil self-weight, and embedded depth of anchor on the uplift capacity has been examined. A nondimensional uplift factor defined as Fcγ owing to the combined contribution of soil cohesion (cu), and soil unit weight (γ) is used for expressing the uplift capacity. For an anchor buried near to a sloping ground, the ultimate uplift capacity is dependent on either pullout failure of anchor or overall slope failure. The magnitude of Fcγ has been found to increase with an increase in the normalized overburden pressure up to a certain maximum value, beyond which either the behavior of ...

17 citations

Journal ArticleDOI
TL;DR: In this article, an empirical non-dimensional reduction factor is proposed for determining the ultimate bearing capacity of shallow strip foundations embedded in sand, based on a series of laboratory model test results available in the literature.
Abstract: An empirical nondimensional reduction factor is proposed in this paper, which can be used for determining the ultimate bearing capacity of eccentrically and/or obliquely loaded shallow strip foundations embedded in sand. This was developed after analyzing a series of laboratory model test results available in the literature on strip foundations. The depth of foundation (D), eccentricity of load (e), and inclination of load (α)varied from 0 to 1B, 0 to 0.5B, and 0 to 20°, respectively, where B refers to the width of foundation. The proposed reduction factor was found to provide reasonably well results for calculating the ultimate bearing capacity of shallow strip foundation under eccentric and/or inclined loads. The results obtained by using the proposed method have been found to be in good agreement with widely used traditional methods available in the literature.

17 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the application of artificial neural networks (ANN) and least square support vector machine (LS-SVM) for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE) modified bituminous mixtures.
Abstract: This study presents the application of artificial neural networks (ANN) and least square support vector machine (LS-SVM) for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE) modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN) model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

14 citations

Journal ArticleDOI
TL;DR: In this paper, a multivariate adaptive regression spline (MARS) model-based approach for the determination of horizontal pullout capacity of vertical plate anchors buried in cohesionless soil by utilizing experimental results reported by different researchers was developed.
Abstract: Vertical plate anchors provide an economical solution to safely resist the large horizontal forces experienced by the foundation of different structures such as bulkheads, sheet piles, retaining walls and so forth. This paper develops a multivariate adaptive regression spline (MARS) model-based approach for the determination of horizontal pullout capacity (P u ) of vertical plate anchors buried in cohesionless soil by utilizing experimental results reported by different researchers. Based on the collection of forty different pullout experimental test results reported in the literature for anchors buried in loose to dense cohesionless soil with an embedment ratio ranges from 1 to 5, a predictive approach for P u of vertical plate anchors has been developed in terms of non-dimensional pullout coefficient (M γq ). The capability of the proposed MARS model for estimating the values of M γq is examined by comparing the results obtained in the present study with those methods available in the literature. Using different statistical error measure criteria, this study indicates that the present approach is efficient in estimating the horizontal pullout capacity of vertical plate anchors as compared to other methods. The sensitivity analysis indicates that the embedment ratio (H/h, where H = embedment depth of anchor, and h = height of anchor) and internal friction angle (ϕ) of soil mass are the two most important parameters for the evaluation of non-dimensional pullout coefficient (M γq ) using the proposed MARS model.

10 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the influence of synthesis parameters on mechanical properties of geopolymer synthesized under ambient environmental conditions is discussed, and the relevance of development in compressive strength with the amount of leached alumino-silicate ions is discussed.

106 citations

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TL;DR: In this paper, the authors investigated the stability of unlined square tunnels in anisotropic and non-homogeneous clays by the lower bound finite element limit analysis using second-order cone programming.
Abstract: In general, the undrained strength anisotropy of clays is found in nature. Its effect on a stability problem during undrained loading should be considered in order to get a more accurate and realistic safety assessment. In this paper, the undrained stability of unlined square tunnels in anisotropic and non-homogeneous clays is investigated by the lower bound finite element limit analysis using second-order cone programming. The anisotropic undrained strength of clays is modelled by using an elliptical strength envelope under plane strain conditions. The stability analyses of the problem are performed by the comprehensive investigations of the effects of the cover-depth ratio, the normalized overburden pressure, the normalized strength gradient, and the anisotropic strength ratio on the stability load factor and associated failure mechanisms. The computed lower bound solutions are validated with the existing results of square tunnels in isotropic clays. The new approximate equations of the stability load factor and factor of safety for square tunnels in anisotropic and non-homogeneous clays are first time presented by using a nonlinear regression, hence providing a reliable, accurate and convenient tool for stability analyses of the problem in practice. The numerical results reveal that the strength anisotropy has a significant impact on the stability load factor, especially when anisotropic clays have much difference in undrained strengths between compression and extension.

60 citations

Journal ArticleDOI
TL;DR: The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researched.
Abstract: It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.

56 citations

Journal ArticleDOI
TL;DR: In this paper, a new prediction model for the soil compaction parameters (i.e., optimum water content and maximum dry density) using multi expression programming (MEP) is presented.

56 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling.

49 citations