Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Mishra, P.

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    Seismic Slope Stability Analysis Using Pseudo-static Approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Mishra, P.; Venkataramana, K.
    Ensuring the stability of slopes under the action of an earthquake is always a challenging problem for geotechnical engineers. As earthquake is one of the major factors responsible for the failure of slopes, it becomes necessary to carry out comprehensive research on the stability analysis of slopes subjected to earthquake-induced loads. Many researchers have developed several methods to analyse the stability behaviour of slope, but till now the failure behaviour has not been understood properly because of the complexity of earthquake loading. With the above background, this study presents a numerical analysis, performed in PLAXIS 3D, to investigate the stability of slopes subjected to earthquake-induced loadings using pseudo-static approach. Also, parametric studies have been carried out to better understand the effects of different parameters (soil properties, slope dimensions, earthquake loadings, etc.) on the Factor of Safety (FOS) and displacement of the slope. The stability of a slope is best assessed in terms of its FOS, which is computed by the strength reduction technique. Analyses’ results show that the slope can sustain a maximum displacement of 442.80 mm, while slope height is varied till the failure point keeping all other parameters constant. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments
    (Springer Science and Business Media Deutschland GmbH, 2025) Bhowmik, B.; Girish, K.K.; Mishra, P.; Mishra, R.
    In high-performance computing (HPC), multi-threaded applications using OpenMP face complex challenges in identifying hidden performance issues, often due to resource conflicts, software inefficiencies, and hardware anomalies. These subtle issues can significantly degrade performance and reduce system reliability. This paper introduces an innovative approach designed to address these concealed issues in OpenMP multi-threaded applications. The proposed method integrates a Random Forest classifier with anthropomorphic diagnosis to effectively identify and diagnose performance-affecting problems. The approach has demonstrated a remarkable ability to detect 90% of performance-affecting issues that are often obscured within complex HPC environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.