http://dspace.dtu.ac.in:8080/jspui/handle/123456789/46 2026-07-02T03:50:49Z 2026-07-02T03:50:49Z SINGH, ISTUTI SAHU, A. K. (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22943 2026-06-25T05:08:34Z 2025-12-01T00:00:00Z

Title: LOAD DEFORMATION BEHAVIOUR OF STONE COLUMN IN EXPANSIVE SOIL REINFORCED WITH GEOSYNTHETICS Authors: SINGH, ISTUTI; SAHU, A. K. (SUPERVISOR) Abstract: The present study investigates the improvement of weak soils using granular columns, with emphasis on the role of geosynthetic encasement and iron dust inclusion in enhancing load-bearing capacity and reducing settlement. Soft soils often exhibit excessive compressibility and inadequate strength, making them unsuitable for direct foundation support. To address these challenges, granular columns are widely employed; however, their efficiency depends on factors such as column arrangement, diameter, and confinement. This research systematically explores the behavior of both single and group columns under different conditions to identify optimum configurations for practical applications. The primary objectives of the study were to evaluate the load–deformation behaviour of ordinary and geosynthetic-encased stone columns in expansive soil, to investigate the influence of column configuration, encasement material, and iron dust stabilization on bearing capacity and settlement characteristics, and to identify the most efficient ground improvement system for expansive soils. The study employed both experimental and numerical methods. Laboratory model tests were conducted on single columns of diameters 50 mm and 70 mm, as well as group columns arranged in triangular, square, and hexagonal patterns with varying spacing-to-diameter (s/d) ratios. Both ordinary and encased stone columns were examined, using geotextile and geogrid as encasement materials. The stone column mix was prepared using stone dust, fly ash, cement, and iron dust to enhance column strength. Tests were performed under monotonic vertical loading, and settlements were measured up to 50 mm. The experimental results were further validated using numerical modeling in PLAXIS 3D. The findings revealed that untreated clay beds had very low load capacity (5.9 kN at 50 mm settlement). Single ordinary stone columns improved the load resistance, with 70 mm end-bearing columns carrying 7.2 kN and floating columns 6.5 kN. Encased single columns showed further gains, with geotextile-encased end-bearing viii columns sustaining up to 8.15 kN, about 38% higher than untreated clay. In group configurations, the triangular pattern consistently gave the best performance, followed by square and then hexagonal arrangements. Geosynthetic encasement enhanced group behavior significantly, with geotextile generally outperforming geogrid due to better fines retention and hoop stress mobilization. The inclusion of iron dust in the column mix further reduced settlement and improved strength, particularly in group columns. Overall, the study concludes that the triangular arrangement of group stone columns, encased with geotextile or geogrid and stabilized with iron dust, provides the most efficient configuration. This system achieves the highest load-bearing capacity and the least settlement, demonstrating its practical applicability for soft soil improvement. The research contributes valuable insights into the design and optimization of granular column foundations and establishes the benefits of combining geosynthetic encasement with stabilizing additives. Additionally, the experimental program was conducted using single columns of diameters 20 mm, 30 mm, and 50 mm, along with group columns arranged as double columns along the mould diameter and triangular columns with a spacing to-diameter ratio of 1. Tests were performed with and without encasement using geotextile and geogrid to assess their relative performance. Additionally, iron dust was introduced as a stabilizing additive to study its effect on settlement and strength. Load–settlement responses were recorded and analyzed to evaluate the comparative performance of each configuration. The results revealed that single columns performed best when encased with geotextile, with the 30 mm diameter column providing maximum load capacity. Group column behavior was influenced more by arrangement, with triangular patterns offering superior settlement resistance compared to double columns. Among the encasement materials, geogrid provided better confinement in group columns, whereas geotextile was more effective for single columns. The inclusion ix of iron dust consistently enhanced overall performance, lowering settlements and increasing load capacity, particularly in group column arrangements. It is concluded that the most efficient configuration for ground improvement in weak soils is the triangular group arrangement of granular columns encased with geogrid and stabilized with iron dust. This combination delivers the highest load bearing efficiency and the least settlement, offering a practical and effective solution for foundation support in soft soils. The findings provide valuable insights into the design and application of granular columns, contributing to the advancement of ground improvement techniques in geotechnical engineering.

2025-12-01T00:00:00Z KHAN, ZOHAIB AHMED Jhamnani, Bharat (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22774 2026-06-08T05:47:32Z 2026-04-01T00:00:00Z

Title: STUDY ON ASSESSMENT OF FLOODS & GROUNDWATER SUSCEPTIBLE ZONES IN IDUKKI DISTRICT, KERALA USING GIS BASED APPROACH Authors: KHAN, ZOHAIB AHMED; Jhamnani, Bharat (SUPERVISOR) Abstract: Floods constitute a recurrent and intensifying hydro-meteorological hazard in the Idukki district of Kerala, driven by the coupled influence of steep topography, high monsoonal rainfall variability, land use transitions, and complex catchment scale hydrological responses. This thesis undertakes a comprehensive assessment of flood susceptibility and its consequent impacts using an integrated geospatial and hydrological modelling framework, providing a scientific basis for understanding their broader environmental significance. The study begins with the development of a flood susceptibility map for the Idukki district using twelve hydrological and geomorphological parameters such as geology, distance from river, land use, Topographic Wetness Index (TWI), elevation, slope, Topographic Roughness Index (TRI), soil, aspect, rainfall, Stream Power Index (SPI), and Sediment Transport Index (STI) within a GIS-based Analytical Hierarchy Process framework. The developed flood susceptibility map was categorized into five susceptibility categories namely very low, low, moderate, high, and very high and these classes occupied the areas of 609.0417 km2, 1222.83 km2, 1180.45 km2, 950.48 km2 and 395.9487 km2 respectively. The analysis also identifies over 30% of the district as highly susceptible, with prominent hotspots in Thodupuzha and central Idukki where low terrain gradients, dense drainage networks, and proximity to major rivers enhance flood generation potential. To characterise subsurface conditions, groundwater potential zones were modelled using a GIS-enabled machine learning approach incorporating AdaBoost, Gradient Boosting, and Random Forest algorithms, capturing the influence of litho-structural features, slope, land use, and other recharge- controlling factors. These groundwater potential outputs were subsequently integrated with the flood susceptibility map to derive a groundwater susceptibility assessment, enabling evaluation of how flood affected areas respond in terms of recharge capacity. The coupled analysis reveals that regions repeatedly subjected to inundation experience elevated runoff coefficients, increased sediment detachment, and reduced infiltration, collectively constraining groundwater replenishment even in zones with otherwise favourable structural characteristics. To evaluate how successive flood events alter the district’s surface conditions, Land Use and Land Cover (LULC) changes associated with the major floods of August 2018 vi and October 2021 were analysed using multi-temporal satellite imagery classified with a Random Forest algorithm on the Google Earth Engine platform. The 2018 floods affected approximately 20.86 km², influencing built-up (0.48 km2), forest (10.60 km2), agricultural (5.11 km2), and barren (4.67 km2) areas, whereas the 2021 event impacted about 19.24 km² across built-up (0.24 km2), agricultural (6.23 km2), barren (2.82 km2), and forest (9.95 km²) classes. These spatial patterns illustrate the substantial modifications to vegetation cover, agricultural land, and terrain stability driven by repeated high-intensity rainfall and flooding in the region. The impact of flooding on surface runoff was further quantified using the Soil and Water Assessment Tool (SWAT) applied to the Periyar River Basin, the largest basin within the Idukki district. Model simulations indicate substantial amplification of runoff during extreme rainfall events, with several outlets exhibiting nearly a 128% rise and others showing increases exceeding nearly 126% compared to non flood conditions. These elevated discharge responses spatially coincide with the high and very high flood susceptibility zones, reinforcing the reliability of the susceptibility modelling and highlighting the presence of hydrologically sensitive regions within Idukki. Collectively, the findings demonstrate strong interactions between surface flooding, groundwater recharge dynamics, landscape transformations, and basin scale runoff behaviour. The integration of multi-criteria analysis, machine-learning modelling, remote sensing, and hydrological simulation provides a comprehensive framework for characterising flood impacts in a complex mountainous environment. The outcomes offer valuable insights for flood mitigation planning, groundwater management, and sustainable land use decision making, while establishing a robust methodological foundation for future hydrological assessments in similar data-scarce regions.

2026-04-01T00:00:00Z SRIVASTAVA, ANUPAM TIWARI, K. C. (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22762 2026-06-08T05:45:45Z 2024-05-01T00:00:00Z

Title: INTEGRATED ANALYSIS OF DISASTER MANAGEMENT IN WESTERN HIMALAYAN REGION Authors: SRIVASTAVA, ANUPAM; TIWARI, K. C. (SUPERVISOR) Abstract: The study presented herein delves into an exhaustive examination of Glacial Lake Outburst Floods (GLOFs) within the Chenab Basin, aiming to assess vulnerability, develop mitigation strategies, and enhance preparedness through the implementation of an Early Warning System (EWS). Leveraging remote sensing data and sophisticated algorithms, this research endeavors to classify glacial lakes, analyze temporal changes in glaciers and lakes over the period 1990-2018, and model potential GLOF impacts downstream. Two specific lakes, namely Lake 1 and Lake 2, have been identified as particularly vulnerable, prompting the application of hydrodynamic modeling to predict potential flood scenarios and assess their repercussions on downstream communities. The methodology employed in this study involves the utilization of remote sensing techniques coupled with a decision tree algorithm for the classification of glacial lakes based on specific parameters and spectral characteristics. By systematically analyzing temporal changes in glacier dynamics and lake expansion, researchers can effectively identify evolving patterns and assess potential risks associated with GLOFs. Through this process, Lake 1 and Lake 2 emerged as focal points for vulnerability assessment and subsequent mitigation measures. Hydrodynamic modeling constitutes a pivotal component of the research methodology, enabling the simulation of GLOF scenarios and the estimation of response times for downstream communities. The findings underscore the heightened vulnerability of villages SHANSHA and THOLONG to GLOFs originating from Lake 1 and Lake 2, respectively, with projected response times of 60 minutes and 4 hours 15 minutes. These insights provide valuable input for the development and implementation of an effective Early Warning System tailored to the specific needs and dynamics of the Chenab Basin. The study advocates for the integration of advanced geophysical monitoring systems, remote sensing technologies, and machine learning algorithms within the framework of the proposed EWS. By harnessing real-time data and predictive analytics, authorities can enhance early detection capabilities, facilitate timely communication, and mitigate the potential impact of GLOFs on vulnerable communities. Furthermore, the study emphasizes the importance of community engagement and capacity building initiatives to foster resilience and empower local stakeholders in disaster preparedness and response efforts.

2024-05-01T00:00:00Z KUMAR, YAKSHANSH TRIVEDI, A. (SUPERVISOR) SHUKLA, SANJAY KUMAR (CO-SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22757 2026-06-08T05:44:57Z 2026-03-01T00:00:00Z

Title: DYNAMIC ANALYSIS OF PAVEMENT-SOIL SYSTEM WITH PIEZO-SENSORS Authors: KUMAR, YAKSHANSH; TRIVEDI, A. (SUPERVISOR); SHUKLA, SANJAY KUMAR (CO-SUPERVISOR) Abstract: The dynamic performance of pavement–soil systems under moving vehicular loads is a complex interaction of load-induced stresses, material damping, and subgrade behavior. Despite significant theoretical progress, practical implementations remain limited due to the lack of integrated modeling frameworks and sensing mechanisms that can capture in situ dynamic responses. This thesis presents a comprehensive investigation that combines finite element-based numerical simulation, experimental validation, and piezoelectric sensing to develop a generalised dynamic framework for pavement–soil systems. A finite element model incorporating viscoelastic pavement layers over an elastoplastic subgrade was developed using Lagrangian mechanics and multi-degree-of-freedom discretization. The model introduces a Generalised Dissipation Mechanism (GDM) defined by dissipation parameters (α, β, γ) and empirical coefficients (η, ϑ) to quantify damping and amplification characteristics under moving loads. Simulations revealed a 70% increase in load and a 46% increase in displacement compared to static conditions, establishing realistic velocity-dependent amplification zones. The Vibrational Compounded Stress Transfer Mechanism (V-CSTM) was formulated to explain the nonlinear amplification and post-elastic flow behavior of geomaterials, bridging cyclic strength and fatigue responses under high-velocity traffic loads. Experimental investigations employed flexible PVDF and PVDF–MoS2 piezoelectric films embedded within confined geomaterials to capture electromechanical responses under vibration. The inclusion of MoS2 nanoflakes enhanced the electroactive β-phase from 54% to 76%, producing a four-fold increase in voltage output (up to 16.2 V) and viii confirming superior energy-harvesting efficiency. Correlations between frequency, stress, deflection, and voltage established the films’ suitability for vibration sensing and renewable energy generation. The integrated numerical–experimental framework enables quantification of damping, stress transfer, and energy dissipation across multi-layered pavement systems. The developed piezo-sensors demonstrate strong potential for self-powered monitoring, while the coupled model offers predictive capability for deformation and damage evolution. Overall, this research advances the understanding of dynamic pavement–soil interaction and contributes to the development of smart, sustainable, and energyefficient infrastructure systems. The work supports Sustainable Development Goals 9 and 11 by promoting intelligent transport networks capable of real-time monitoring, reduced maintenance, and green energy harvesting.

2026-03-01T00:00:00Z