Journal of Scientific Research and Technology

Role of Advanced Geotechnical Sensors and Analytical in Real-Time Slope Stability Monitoring

Yagnesh Salvi — 2024-12-01

Slope stability monitoring in real-time is essential for reducing the danger of landslides and other slope collapses. Improving real-time slope stability monitoring via the integration of sophisticated geotechnical sensors and analytical algorithms is the focus of this research. Inclinometers, piezometers, and strain gauges are some of the newer sensor technologies that can measure structure deformations, groundwater variations, and soil movement with great precision. These sensors can take readings in real time, which is crucial for finding any problems as they happen.

Deep Prediction Of Chronic Kidney Disease

Prof. Amreen Anjum — 2024-12-14

Chronic Kidney Disease (CKD) affects millions globally and early detection is crucial for preventing progression and complications. The use of Deep Learning algorithms to analyze massive volumes of medical data in search of patterns and correlations has great potential for the prediction of CKD using demographic, clinical, and laboratory outcomes. A Deep Learning model for forecasting CKD according to these characteristics is suggested for development in this work. This model will provide a rapid and accurate tool for early identification and effective treatment of the illness. With its reliable predictions, the suggested approach shows great promise as a tool to enhance CKD identification and treatment. Around $12 billion will be needed to treat all existing and future cases of renal failure in Australia until 2020. An effective method for early-stage CKD prediction is provided by machine learning techniques.

Ultimate Pit Limit Optimization Of Opencast Mine Using Surpac Software

D. Gawariya — 2024-12-19

This study focuses on optimizing the Ultimate Pit Limit (UPL) for opencast limestone mining using SURPAC software. The research aims to demonstrate the advantages of integrating advanced software in the mine planning process, specifically for optimizing pit design and improving resource recovery. By utilizing geological data, including borehole surveys, assay results, and lithological information, a 3D block model was developed, and various optimization algorithms were applied to determine the most economically viable pit boundaries. The study compares traditional manual methods with modern software-based approaches, showcasing the significant reduction in time and error. SURPAC’s integration of Lerchs-Grossmann and other algorithms allowed for more accurate mineable reserve estimation, enhanced safety measures, and improved environmental sustainability by minimizing waste removal and optimizing resource extraction. This research highlights the potential for modern mine planning software to transform the efficiency, safety, and profitability of opencast mining operations.

Mapping & Analysis Of Roof Stress Distribution In An Underground Coal Mine Using FLAC 3D Software

V. Thakur — 2024-12-19

This study focuses on mapping and analyzing roof stress distribution in an underground coal mine using FLAC3D, aiming to enhance mine safety and efficiency. A systematic methodology was employed, including data collection from 63 boreholes, solid modeling using SURPAC, and numerical stress analysis through FLAC3D. Key objectives included evaluating distributed loads, analyzing the impact of geological disturbances, and comparing pre- and post-development stress behaviors. Results reveal that geological disturbances significantly influence stress distribution, identifying high-risk zones requiring enhanced support systems. Pre- and post-development scenarios showcased distinct stress patterns, emphasizing the dynamic behavior of roof strata. Additionally, differences in weak and strong geological sections were mapped, guiding optimized support designs. This research contributes to improved mine planning, risk mitigation, and the advancement of numerical modeling techniques for underground mining operations.