This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions.

Landslide susceptibility mapping is crucial for landslide risk management in mountainous areas like Nepal. However, the performance of a landslide susceptibility model is often compromised by multicollinearity among landslide causative factors. While feature selection techniques are recognized as essential preprocessing steps, most studies lack systematic comparisons of how different selection methods affect traditional models under identical conditions. This study addresses this gap by evaluating Weighted Overlay (WO), Multiple Linear Regression (MLR), and Logistic Regression (LR) using Correlation Analysis, Variance Inflation Factor (VIF), and Information Gain (IG) feature selection techniques. It is found that LR with Correlation Analysis results in 69.30% accuracy and 75.48% Area Under the Receiver Operating Characteristic Curve (AUC-ROC) while maintaining balanced precision (64.47%) and recall (85.96%). The WO model yields outstanding landslide recognition (90.18% recall) with VIF analysis despite a lower precision value (56.74%). MLR with IG analysis achieves reliable performance (62.11% accuracy, 64.76% AUC-ROC) for regional assessments. The study offers practical guidelines for method selection based on assessment goals, emphasizing the trade-off between statistical optimization and physical interpretability in susceptibility mapping.

Bearing capacity of shallow foundation is a challenging problem for geotechnical engineers. The difficulty comes from multiple sources of uncertainty. Only the uncertainties in Soil properties; unit weight (γ), cohesion(c), and angle of friction (φ) are taken in this study. The Reliability Based Design method is used for the assessment of bearing capacity of shallow foundation by First-and second-order approximation using Taylor’s series expansion and Mathcad Computer program which provide systematic analysis of uncertainties and allow the designer how reliable are their designs. The Terzaghi Bearing capacity equation is used for the analysis along with the certain range of variability (CV %) associated with the variables. For the reliability analysis, to become more realistic two real cases of shallow foundation on c-φ soil has been taken as a sample case.

Kathmandu Valley lies in an active tectonic zone, meaning that earthquakes are common in the region. The most recent was the Gorkha Nepal earthquake, measuring 7.8 M_w. Past earthquakes caused soil liquefaction in the valley with severe damages and destruction of existing critical infrastructures. As for such infrastructures, the road network, health facilities, schools and airports are considered. This paper presents a liquefaction susceptibility map. This map was obtained by computing the liquefaction potential index (LPI) for several boreholes with SPT measurements and clustering the areas with similar values of LPI. Moreover, the locations of existing critical infrastructures were reported on this risk map. Therefore, we noted that 42% of the road network and 16% of the airport area are in zones of very high liquefaction susceptibility, while 60%, 54%, and 64% of health facilities, schools and colleges are in very high liquefaction zones, respectively. This indicates that most of the critical facilities in the valley are at serious risk of liquefaction during a major earthquake and therefore should be retrofitted for their proper functioning during such disasters.

Understanding the particle crushing behavior of sand under high compressive stress is critical for accurate prediction of soil compressibility and strength characteristics. This study examines the particle breakage characteristics of Melamchi river sand under extreme vertical loads using a Compression Testing Machine (CTM). Dry sand specimens were placed in a CTM machine and loaded step-by-step upto 1000 kN (about 53 MPa). Particle size distribution analysis was done before and after each test to find D10, D20, D50, D60 and coefficient of uniformity (Cu) . Breakage indices were calculated to quantify the degree of particle breakage. Results show upto 59% reduction in mean particle size with increasing stress levels at 1000 kN. The findings confirm that grain crushing must be considered in geotechnical infrastructure works like deep pile foundations, rockfill dams, embankments and hydropower tunnels, where high stresses occur. The findings are useful for the design of gravel drains and soil filters, where stress-induced particle crushing can change gradation and influence filtration performance.