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Slope failures are catastrophic in nature, resulting in widespread loss of lives and infrastructure. Consequently, stability analysis of slopes has gathered considerable attention by the research community across the globe. In practice, limit equilibrium approach forms the underlying principle for majority of the conventional slope stability analysis methods. One of the major limitations of the limit equilibrium methods is that they fail to account for inherent variability of in situ soil and its influence on the assessment of slope stability. As a contribution to this important aspect, the current study presents a reliability-based slope stability analysis of a natural slope of an infinite extent. The slope is analyzed in the following four states of the natural slope, namely, dry state, fully submerged state, with steady seepage, and under seismic loading. The reliability analysis is performed using Hasofer–Lind first-order reliability method. The in situ soil properties considered as variables include in situ soil cohesion, angle of internal friction, and unit weight. The analyses results are presented with respect to different levels of in situ soil variability, slope angles, depths of potential failure plane, and magnitude (and its variability) of seismic loading. Evidently, the study provided a better insight and different perspective into the stability analysis of infinite slopes.

The electric operated road vehicles are frequently powered by lithium ion batteries due to its low cost and ease of manufacturing. However, unforeseen impacts in road conditions can lead to fire hazard due to short circuiting of the battery pack. The impact strength of the battery pack can hence provide a key design input for manufacturing next generation batteries with a durable safe limit. In this work, a finite element based neural search approach is proposed for determining the effects of various uncertain phenomena on the strength of the battery. The approach combines the actual impact mechanics of battery as determined by the finite element model along with the high accuracy and robustness provided by neural search algorithm. The derived model is able to satisfactorily predict the variances in the mechanical strength even with slightest uncertainties in the phenomena which can affect the strength of the battery pack. It is anticipated that the proposed model will be of utmost importance in design of next generation safe and durable lithium ion battery packs.

In practice, numerical simulations of soil nail walls are often carried out to assess the performance and stability. In the present study, implications of the use of advanced soil models, such as hardening soil model and hardening soil with small-strain stiffness model to simulate the behavior of in situ soil on the overall response of simulated soil nail wall have been studied, and compared with respect to the analysis using conventional and most prevalently used Mohr-Coulomb soil model. Further, influence of the consideration of bending stiffness of soil nails on the simulation results has been examined. Results of the simulations indicated that the use of advanced models is desirable for cases of soil nail walls constructed in soft soils and when lateral wall displacements are critical to the adjoining structures. Incorporation of bending stiffness of nails is found important from the consideration of facing failure modes of soil nail walls.