Passionate about Geotechnical and Geo-Hazard Research with focus in foundations, liquefaction, rock mechanics, and landslides. Focused on creating safe, resilient, and environmentally responsible infrastructure.
I am a fresh Civil Engineer graduated from Pokhara University with a passion for Geotechnical and Geo-Hazard Research. With a keen interest in shallow and deep foundations, liquefaction, Rock mechanics, landslides, and geotechnical earthquake engineering, I am focused on creating safe, resilient, and environmentally responsible infrastructure. Eager to contribute innovative solutions to the challenges of modern engineering, I aim to design structures that stand the test of time and support sustainable growth.
Apart from my academic life, I watch football, play guitar, and hang out with family and friends. I love trekking, and going out for a short ride.
Bachelor in Civil Engineering from Pokhara University with a good academic record. Specialized coursework in structural engineering, Geotechnical engineering, and Hydropower.
Slope Stability, Foundations Engineering, Rock Mechanics, Empirical Modelling, and Soil Mechanics. Exploring Soil Erosion and Landslide susceptibility and environmental impact assessment in construction.
Proficient in advanced structural analysis techniques using modern software tools. Experience in designing reinforced concrete structures and steel frameworks.
A groundbreaking 72.5 km expressway connecting Kathmandu to Nijgadh, featuring 87 bridges and 6 tunnels. This mega-infrastructure project reduces travel time from 5 hours to just 1 hour, incorporating advanced engineering solutions for challenging Himalayan terrain. The project showcases cutting-edge 3D modeling techniques used for precise planning and visualization.
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A comprehensive two-week field training program where civil engineering students gain hands-on experience with surveying instruments and techniques. Students learn to conduct topographical surveys, road alignments, and bridge site investigations using modern equipment like Total Station, GPS, and traditional tools. The camp involves practical exercises in leveling, traversing, contouring, and preparing detailed site plans. This immersive experience bridges theoretical knowledge with real-world surveying applications.
Read MoreA sustainable residential complex designed to meet modern living standards while minimizing environmental impact. Features include solar-powered systems, rainwater harvesting, and green spaces for energy efficiency and environmental conservation.
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Rapid growth and domestic migration to capital city, Kathmandu Metropolitan City (KMC), Nepal have amplified land-use transition to unmanaged and unplanned urbanization have exacerbated problems of Urban Heat Island (UHI) Effect. This study focuses on forecasting Land Surface Temperature (LST) using both univariate and multivariate time series models. The primary goal is to predict the impact of climatic factors on the Urban Heat Island (UHI) effect, utilizing data from 1981 to 2019.
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Soil erosion is a critical environmental challenge in mountainous regions such as Nepal, where fragile landscapes and intensive land use exacerbate land degradation. This study assesses soil loss in the Bakraha Watershed by applying the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) within a GIS and remote sensing framework. The results revealed soil erosion rates ranging from 0 to 515 t ha⁻¹ yr⁻¹ (mean: 10.51 t ha⁻¹ yr⁻¹).
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The landslide inventory data were randomly divided, with 80% used for training the models and 20% reserved for validation. Both models performed robustly, as confirmed by ROC curve analysis, though the Weight of Evidence (WoE) model demonstrated superior predictive accuracy (AUC = 0.866) compared to the Frequency Ratio (FR) model (AUC = 0.854).
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Earthquake magnitude prediction for Nepal has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Machine learning methods, such as random forest, artificial neural network and long short-term memory (LSTM) neural networks, excel at identifying patterns in large-scale databases.
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