http://dspace.dtu.ac.in:8080/jspui/handle/123456789/45 2026-07-25T11:41:25Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/23019 Title: COMPARATIVE LANDSLIDE SUSCEPTIBILITY MAPPING IN PITHORAGARH DISTRICT, UTTARAKHAND: A MULTI-MODEL APPROACH USING FREQUENCY RATIO, SHANNON ENTROPY AND ANALYTICAL HIERARCHY PROCESS Authors: YADAV, KRISH; SARKAR, RAJU (SUPERVISOR) Abstract: Pithoragarh District, located in the Kumaon division of the Uttarakhand Himalaya, remains highly vulnerable to heavy and dangerous landslides resulting from fractured bedrock, steep slopes, long-standing tectonic activity, and heavy seasonal rainfall. As a first step toward facilitating hazard targeting and management, this paper primarily carries out spatially comparative susceptibility assessments of three frequency-ratio based methods, Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy Process (AHP), in this complex geological mountainous setting. Location data for 366 past slope failures were gathered and integrated into an inventory database and linked to ten topographic and environmental factors: slope, aspect, hillshade, curvature, rock type, distance to roads, distance to drainage, distance to structural lineaments, Topographic wetness index, and Terrain ruggedness index. The sample was split 70:30 for model fitting and independent testing, respectively. Among the three methods that were tested, AHP gave the best result with the AUC value of 0.839, whereas SE and FR produced AUC values of 0.836 and 0.835, correspondingly, all three having the very good accuracy range comfortably. Expert assignments of weights in the AHP schema were corroborated by a Consistency Ratio of 0.009, which indicates a high level of internal consistency of the judgment matrix. The two major factors that influence the occurrence of slope movements and kept revealing their dominant roles were the proximity to roads and the lithology of the bedrock. Final hazard maps provide spatially differentiated hazard zones for the district, which can be used by planners and disaster management offices as decision-making tools in at-risk mountainous areas of Uttarakhand. 2026-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/23018 Title: SWELLING CHARACTERISATION AND RISK CLASSIFICATION OF BLACK COTTON SOIL USING EMPIRICAL CORRELATIONS AND MACHINE LEARNING Authors: PANDEY, ANURAG DAYAL; SAHU, ANIL KUMAR (SUPERVISOR) Abstract: Black cotton soils (BCS) of Deccan Trap region of India are characterized by the presence of high proportion of montmorillonite clay mineralogy and exhibit high swell-shrink characteristics leading to distress in roads, foundations, embankment and lightly loaded structures. In Indian geotechnical practice, the Free Swell Index (FSI) determined by comparing the sedimented volume of oven dried soil in distilled water with kerosene is the standard index of expansiveness of the soil. The FSI test is relatively simple to perform in a well-equipped lab, but the Atterberg limit data (liquid limit, plastic limit, plasticity index and shrinkage limit) are readily available from the earliest stages of site investigation. Therefore, it would be beneficial to establish reliable relationships between these index properties and FSI and provide a complementary assessment tool for geotechnical engineers that would not replace the actual FSI test. This study aims to fill that need with a two-part investigation. During the first phase, the black cotton soil was taken from Shajapur district, Madhya Pradesh and mixed with sodium bentonite at 0%, 2%, 4%, 6%, 8% and 10% of the dry weight of soil to prepare six soil mixtures (M1 – M6). Each mixture was tested for Atterberg limits (LL, PL, PI, SL) and FSI as per IS:2720 and IS:9451 respectively. The Pearson correlation analysis showed that all four Atterberg limit parameters were highly correlated with FSI: LL (r = +0.9998), PI (r = +0.9997) and SL (r = −0.9981). Five empirical regression equations were developed, ranging from single-variable Atterberg limit models — FSI = 2.299·PI − 22.315 (R² = 0.9995) and FSI = 2.037·LL − 61.289 (R² = 0.9996) — to dual-Atterberg-limit models incorporating PI and SL (R² = 0.9996) and LL and SL (R² = 0.9997). The single-variable PI and LL models were found to be the most robust models in this small dataset by leave-one-out cross validation (LOOCV), with RMSE values of 0.53% and 0.47%, respectively. The second phase involved training and testing three machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (ANN)) on a compiled database of 186 Indian BCS samples from ten states to III predict the IS:1498 swelling risk class (Low, Medium, High, Very High) from LL, PI, SL, optimum moisture content (OMC), and maximum dry density (MDD). The class imbalance issue was addressed using stratified 5-fold cross validation. Random Forest was the best model in terms of balanced accuracy (0.860, CV accuracy = 0.880 and Cohen's κ = 0.792), indicating its appropriateness for categorical swelling risk assessment. The derived empirical equations can be used to estimate FSI from Atterberg limit index properties for Shajapur-type Malwa Plateau BCS and the Random Forest classifier can be used to map the swelling potential of BCS in a geographically wider Indian context. Both are designed to be complementary rapid assessment tools to support preliminary decision making in the field by the geotechnical engineer, rather than replacing direct measurement of FSI where site conditions require it. 2026-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/23017 Title: LANDSLIDE SUSCEPTIBILITY MAPPING OF SHIMLA DISTRICT USING INFORMATION VALUE AND WEIGHT OF EVIDENCE MODELS Authors: UJJAWAL; SARKAR, RAJU (SUPERVISOR) Abstract: Himalayan region is a major natural hazard prone area where landslides cause severe damage to infrastructure, property and lives. The steep topography, complex geological environment, high anthropogenic activities and fragile mountain ecosystem make Shimla district very prone to landslides. In the present study, the landslide susceptibility over Shimla district has been assessed by employing two statistical and machine-learning models namely Weight of Evidence (WoE) model and Information Value (IV) algorithm in a Geographic Information System (GIS) environment. A thorough inventory of landslides was compiled using historical records and interpretation of satellite images. To minimize sampling bias, an equal number of non landslide points were also generated from areas of stability. To account for the processes of landslides in a mountainous area, 12 landslide conditioning factors were selected: slope, aspect, curvature, elevation, hillshade, roughness, lithology, topographic wetness index, drainage density, distance to stream, normalized difference vegetation index, and contour derived information. The Weight of Evidence model was used to assess the statistical association of the occurrence of landslides with each of the conditioning factors, and the Information Value model was used to determine the complex non-linear relationships among the factors. Two kinds of landslide susceptibility maps were produced with both methods and then divided into low, moderate, and high susceptibility zones. The results reveal that the most influential factors controlling the occurrence of landslides in Shimla are slope, roughness, lithology and elevation. The results of this study are useful for landslide risk reduction, land use planning and disaster management in Shimla district and also showcases the effectiveness of combining the statistical and machine learning methods for landslide susceptibility mapping in high relief Himalayan areas. 2026-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/23013 Title: MECHANICAL PERFORMANCE OF GUAR GUM-STABILISED SOIL FROM THE YAMUNA BASIN Authors: SIDDHANT; Gupta, Ashok Kumar (supervisor) Abstract: Weak alluvial subgrades in the Yamuna floodplain, Delhi, represent a persistent geotechnical challenge for infrastructure development in one of India’s most rapidly urbanising regions. Conventional stabilisation approaches, while technically effective, rely heavily on cement and lime and carry substantial environmental penalties arising from energy-intensive production processes and significant carbon dioxide emissions. The present investigation evaluates guar gum, as an eco-friendly alternative for stabilising alluvial soil retrieved from the Yamuna Basin. Specimens were treated at dosage levels of 1.0% and 1.5% guar gum by dry soil weight and subjected to Standard Proctor compaction, Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) testing following curing periods of 3, 7, and 21 days. Microstructural characterisation was carried out using Scanning Electron Microscopy (SEM) to elucidate the physical bonding mechanisms underlying observed macroscale performance improvements. Additionally, a theoretical carbon emission analysis was performed to quantify the environmental advantage of biopolymer stabilisation relative to conventional cement based ground improvement. Compaction behaviour proved resilient to biopolymer addition, with maximum dry density and optimum moisture content remaining essentially unchanged across both dosage levels, confirming that guar gum-treated subgrades can be constructed to the same field specification as untreated fill. UCS results revealed a consistent, curing-dependent strength trajectory: the 1.0% dosage achieved the highest peak strength of approximately 4.80 kPa at 21 days, representing a 136% improvement over the untreated baseline of 2.03 kPa. CBR testing demonstrated a complementary dosage-response, with the 1.5% treatment achieving a 90% improvement in load resistance at 21 days under confined loading conditions. SEM analysis confirmed a hierarchical bonding structure operating simultaneously at sub-micron, micron, and aggregate length scales, providing the microstructural basis for observed macroscale performance gains. The theoretical carbon emission analysis indicates that guar gum stabilisation at the 1.0% dosage produces approximately 3 to 6 kg CO₂ per cubic metre of treated soil, representing a reduction of 90 to 95% relative to conventional cement stabilisation, which generates an estimated 72 to 115 kg CO₂ per cubic metre. These findings collectively establish guar gum as a technically sound and environmentally responsible approach to ground improvement for collapsible Yamuna Basin soil, with direct applicability to pavement subgrade and low-to-moderate load-bearing foundation contexts in ecologically sensitive floodplain environments. 2026-05-01T00:00:00Z