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The paper pertains to the development of a numerical approach of auto-searching the critical slip surface of general shape and the associated factor of safety using limit equilibrium method but adopting a nonlinear strength envelope of soil in the analysis. The critical slip surface and the corresponding factor of safety are determined by formulating the same as a constrained nonlinear optimization problem. The problem is solved by using the extended interior penalty function method. The method as proposed in contrast to some of the used indirect procedures to identify the critical slip surface eliminates the additional computational efforts involved in carrying out the analysis using nonlinear strength envelope in comparison to the linear one. The obtained results reveal that nonlinear strength envelope gives a shallower critical slip surface with a lower factor of safety compared to those obtained from analysis considering linear strength envelope. This is of great significance in design of slopes in such soils as linear idealization of nonlinear strength envelope might lead to unsafe prediction of slope safety.

Stability analysis of multi-layered slopes with complex geometry and pore pressure conditions has always been a topic of immense interest to both the researchers and the practitioners in the field of geotechnical engineering. Over the decades, within the framework of limit equilibrium methods of slices, a number of rigorous methods have been proposed which are valid for general non-circular slip surfaces, satisfy all conditions of equilibrium, and take full account of the interslice forces. Among these, the Sarma method is the only method in which the slices are not necessarily vertical, and the critical inclinations of the slices are found as part of the solution and use the internal strength of material for the solution of the problem. Because of the number of iterations involved in finding the critical set of slice inclinations, Sarma method is not suitable for finding the critical slip surface. In order to compare the results obtained by using the other rigorous methods with those using Sarma’s method, the latter can be used to re-analyze the critical slip surfaces determined for the other methods. In this study involving three complex slope stability problems, the critical slip surfaces determined using the Morgenstern and Price method and the Spencer method are re-evaluated using the Sarma method. The GEO5 software is utilized for the purpose. The comparison of results has brought out that compared to the Sarma method, both the Morgenstern and Price and the Spencer method consistently yields conservative values of factor of safety for each of the three example problems.

The paper pertains to the application of a simple procedure for reliability analysis to natural slopes of unsaturated soils after rainfall infiltration considering the uncertain system parameters as random variables. Analysis has been done within the framework of the well known ‘infinite slope’ limit equilibrium model suitably modified to take into account the shear strength of an unsaturated soil. Analysis of the time dependent process of infiltration and the progression of the wetting front is based on a modified form of the widely known ‘Green-Ampt model’. Reliability analysis has been carried out based on the simple Taylor series (MVFOSM) method. The results obtained for an illustrative example predict a much earlier occurrence and a much shallower location of the potential slide than that predicted by the conventional deterministic analysis. A reliability-based sensitivity analysis reveals that the angle of internal friction has the most dominating influence on the probability of failure of the slope.

Estimating seismic response of reinforced concrete (RC) structures under bidirectional excitation covering admissible incidence angles is computationally challenging and time-consuming for ...

Within the framework of the limit equilibrium method of slices, a slope reliability analysis accounting for two-dimensional (2D) spatial variation requires a model to discretize a random fi...

Several private players have expressed their desire to mine resources in space. This posits ethical and legal concerns. Several scholars argue that space mining activities flout the national non-ap...

Extending conventional Park-Ang damage index, a simple yet rational damage measure applicable for bi-directional seismic shaking has been evolved. Comparing predicted damage states of a bridge pier in terms of the evolved damage index to the component strains, efficacy of the proposed index has been verified. Quantitative assessment of damage of a bridge pier demonstrates substantial magnification in damage due to bi-directional shaking relative to uni-directional companion. This amplification in damage may further vary with incidence angles. Unfortunately, conducting true bi-directional analysis is challenging and implications of incidence angles are elusive yet. Collectively investigating these two crucial issues, present study identifies suitable orientations/ incidence angles in terms of pure ground motion parameters. Maximum credible damage of bridge pier under bi-directional shaking may be estimated by combining damage (30% rule) due to less-rigorous uni-directional analysis conducted in the most preferred orientation defined by the ratio of energetic length of components. The present investigation thus appears to evolve a rational damage index and a novel strategy to quantify the same for bridge pier under bi-directional seismic excitation.

In the past, residual factor R in strain-softening soil slopes has been included, either directly or indirectly, as a deterministic variable in both deterministic and probabilistic studies. This paper discusses the uncertainties associated with R and outlines a systematic approach for the reliability analysis of a natural slope in which shear strength parameters and pore pressure ratio are random variables, each assumed with a lognormal probability distribution. For the residual factor R, seven probability distribution options under the generalized beta-distribution system are considered. Slope reliability is computed based on the first order reliability method (FORM) and validated against Monte-Carlo simulation (MCS). Results obtained from two illustrative examples indicate that the probability of failure, with R as one of six random variables, can be orders of magnitude higher than that based on five random variables with R considered as a deterministic parameter. The magnitude of influence of R as a random variable is, however, highly dependent on its probability distribution, the left-skewed triangular distribution having the most significant influence in both the examples. Results of sensitivity analyses reveal that, for almost all of its assumed probability distributions, R is the most dominant among the six random variables. Effects of variation of some of the statistical and correlation properties of the other random variables, viz. the shear strength parameters and the pore pressure ratio, on the results of reliability analyses are also studied.

The kNN algorithm remains a popular choice for pattern classification till date due to its non-parametric nature, easy implementation and the fact that its classification error is bounded by twice the Bayes error. In this paper, we show that the performance of the kNN classifier improves significantly from the use of (training) class-wise group-statistics based two criteria during pairwise comparison of features in a given dataset. Granger causality is employed to assign preferences to each criteria. Analytic Hierarchy Process (AHP) is applied to obtain weights for different features from the two criteria and their preferences. Finally, these weights are used to build a weighted distance function for the kNN classification. Comprehensive experimentation on fifteen benchmark datasets of the UCI Machine Learning Repository clearly reveals the supremacy of the proposed Granger causality driven AHP induced kNN algorithm over the kNN method with many different distance metrics, and, with various feature selection strategies. In addition, the proposed method is also shown to perform well on high-dimensional face and hand-writing recognition datasets. HighlightsFeature weighting for kNN by a multi-criteria based decision analysis tool called AHP.Automated weight assignment in criteria matrix of AHP using group-statistics.Criteria preference selection in AHP with Granger Causality.Superior classification performance over kNN with many other feature weighting/selection methods.

In this work, we tackle the problem of end-to-end language identification from speech. To this end, we propose the use of a residual convolutional neural network aiming at exploiting the ability of such architectures to take into account large contextual segments of input data. Moreover, in order for variable input lengths to be supported by the proposed setting, a self-attention mechanism is employed on top of the final convolutional layer. This results in a learnable temporal feature pooling scheme that allows for embedding varying duration utterances into a fixed dimension space. Evaluation is performed on data containing ten oriental languages under different test conditions, namely: short-duration recordings, confusing languages trials, as well as a set of trials in which non-target unseen languages are included. End-to-end evaluation of the proposed framework is thus shown to significantly outperform well-known benchmark methods under considered evaluation conditions.