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.

The management of discarded woven polypropylene cement sacks (W-PPF) after cement use at construction sites has become a critical issue, as it degrades the site environment and significantly increases landfill volume. This study investigates the use of W-PPF as discrete fibers in concrete to reduce construction waste while evaluating the resulting properties of the concrete. M20 grade concrete was adopted as the control mix, with a water–cement ratio of 0.50 and a mix proportion of 1:1.84:3.08 (cement: fine aggregate: coarse aggregate). W-PPF was incorporated at contents of 0.25%, 0.50%, 0.75%, and 1.00% by weight of cement. Tests were conducted to assess workability, density, mechanical strengths, and water absorption. The optimum W-PPF content was found to be 0.50%, which enhanced compressive strength, splitting tensile strength, flexural strength, and elastic modulus by 18.1%, 37.2%, 41.7%, and 3.81%, respectively, compared to the control mix. However, workability decreased from 120 mm to 90 mm, and water absorption increased from 2.12% to 2.87%. A comparison of strength performance with carbon footprint indicated that 0.50% W-PPF provides the most favorable balance. The practical implementation of these findings can help address the challenge of managing waste cement sacks while improving the mechanical performance of concrete, thereby promoting more sustainable construction practices and reducing the environmental impact at construction sites. Nevertheless, the long-term durability of W-PPF-reinforced concrete requires further investigation.

Frequent rainfall-induced landslides disrupt the Mugling-Narayanghat highway corridor in central Nepal, a major trade route. This study presents a site-specific geotechnical investigation aimed at characterizing soil properties and assessing the stability of unstable sections along the corridor. Representative soil samples were collected from five active landslide locations (chainages 16 + 600, 20 + 400, 32 + 300, 34 + 800, and 35 + 600) to determine particle size distribution, Atterberg limits, compaction characteristics, and shear strength parameters. Laboratory results classify the slope materials as non-plastic (NP) soils, ranging from well-graded gravels (GW) to poorly graded sands (SP) and well-graded sands (SW). These characteristics render the soil matrix highly permeable and susceptible to rapid shear strength loss upon saturation due to the absence of cohesive clay minerals. Shear strength parameters determined using direct shear tests yielded friction angles ranging from 26.1° to 29.1° and apparent cohesion values between 38.2 kPa and 102.5 kPa. An analytical factor of safety (FOS) calculation was performed to evaluate slope vulnerability under dry and saturated conditions. The assessment indicates that saturation reduces the FOS by approximately 30% to 36%, with values dropping below unity (FOS ranges from 0.89 to 0.96) in critical sections. Because the apparent cohesion of these non-plastic soils is largely lost on saturation, a conservative analysis neglecting cohesion (c′ = 0) was also performed, under which all sections fall well below unity; a pseudo-static assessment confirms that the combination of monsoon saturation and seismic loading is the governing condition for the corridor. These results quantitatively align with field observations of recurrent failures driven by monsoon rainfall and hydraulic forcing. Consequently, site-specific mitigation measures are proposed. This study demonstrates that index-property testing combined with analytical stability assessment provides a cost-effective framework for prioritizing maintenance in highway corridors with similar geotechnical conditions.