http://localhost:8081/jspui/handle/123456789/25 2026-05-07T17:27:49Z 2026-05-07T17:27:49Z Chandel, Anoopsingh Jainarayansingh http://localhost:8081/jspui/handle/123456789/20527 2026-04-27T06:10:01Z 2024-06-01T00:00:00Z

Title: STABILITY OF RESERVOIR RIM SLOPES SUBJECTED TO FLUCTUATING WATER LEVEL Authors: Chandel, Anoopsingh Jainarayansingh Abstract: Existence of the reservoir-induced landslides has been acknowledged worldwide as a major barrier in the effective and successful functioning of hydro-power projects. Events of reservoir induced landslides have simultaneously risen in the past few decades along with the increase in number of hydro-power projects. Reservoir-induced landslides are more complex in nature than the landslides observed in mountainous regions as there can be several causes of failure. The triggering factors for the reservoir-induced landslides are mainly seepage, rainfall, geometric instabilities, and reservoir-water level fluctuations. Reservoir-induced landslides are often linked to fluctuations in reservoir water levels. For instance, more than 5000 landslides have been witnessed in Three Gorges Reservoir (TGR) since the commissioning of the TGR Dam, majority of which have been attributed to water level fluctuations. There exist various theories that explain the failure mechanisms of these landslides due to such fluctuations. Still, the failure mechanism of reservoir-induced landslides due to water level fluctuations is not clearly understood. Besides, limited studies are available on the displacement prediction models for rim slopes experiencing movements implicitly due to reservoir level fluctuations. The available prediction models are based on long-term in-situ field displacement monitoring. A detailed investigation is essential to understand the various aspects of the failure mechanism of these fluctuations-induced landslides. The contribution of reservoir water level fluctuations as an isolated triggering factor needs to be quantified. With the increasing number of hydro-power projects in India as well as throughout the globe, a simple quick assessment tool for predicting rim slope displacements based on easily available field slope data is the need of the hour. The present study aims at exploring the failure mechanism of rim slopes specifically due to reservoir water level fluctuations, and to come up with a simple generalized empirical solution to predict the displacements along the field rim slopes based on available field data. To achieve the objectives of the present study, an extensive experimental program was planned followed by seepage and stability analyses of the test results. A small-scale physical slope model test (PSMT) apparatus was developed indigenously to study the rim slopes subjected to fluctuating water levels. Eight PSMTs were performed in total. Two PSMTs were performed in steady-state condition to study the initiation and propagation of rim slope failures in steady-state seepage i condition with varying soil type (c-ϕ soil from Koteshwar, and sandy soil from Solani river) and slope inclination (30° and 45°). Six PSMTs were performed on a modelled soil (i.e. scale-reduced soil derived from a rim slope site in Koteshwar reservoir using parallel gradation technique) subjected to reservoir water level fluctuations. The six PSMTs were performed by varying cyclic fluctuation rate (i.e. 16 cm per 6 hours – slow, 16 cm per 1 hour – moderate, and 16 cm per 10 minutes - rapid), slope inclination (30° and 45°), and number of cycles (i.e. up to 30 cycles). During the experimentation, total head values with the help of piezometers installed on the setup, degradation of slope face, and deformed slope profiles with the help of deformation gauge, were observed at different test stages. The observations were made until the model slope failure occurred in steady-state condition, and up to the completion of 30 cycles when subjected to reservoir fluctuations. Lastly, seepage and stability analyses of the PSMTs results were performed to gain deeper understanding about the failure mechanism. The results and analyses of PSMTs in steady-state condition revealed that the cause for initial failures in the case of slopes that are reasonably stable under steady-state seepage condition is highly likely due to high exit hydraulic gradient. The initial slope failure will be induced within the region of high hydraulic gradients. The depth of failure surface will mostly be governed by the soil type. As the soil changes from cohesionless soil to a cohesive soil, the depth of failure slip surface tends to be increasing with cohesion being mobilized. There is a high probability of retrogressive failure phenomenon in the case of cohesionless granular soils. Using the experimental observations of PSMTs subjected to cyclic fluctuations, the analyses of PSMT observations were carried out in various ways. Firstly, spread (displacement at toe) was assessed. It was found that the amount of spread is high for slopes subjected to slow cyclic fluctuations in comparison to slopes subjected to rapid cyclic fluctuations. Change in pore water pressure just after the drawdown with successive cycles were studied for the performed PSMTs. The results suggest that sharp accumulation of pore pressure occurs during the initial cycles of water level fluctuation. The rate of increase in pore pressure decreases afterwards, and stabilizes for the remaining cycles of water level fluctuations. Seepage analysis was carried out to validate the trend of total head values observed during experimentation for reservoir water level fluctuations. The pore-pressure derived from the seepage analyses were then used for stability analysis. It was found that the stability of slopes subjected to cyclic fluctuation strongly follows the water level fluctuation scheme. Lastly, volumetric evolution, and static liquefaction ii were evaluated. Static liquefaction assessment concluded that the rim slopes are susceptible to static liquefaction under the influence of cyclic fluctuations. Based on the test observations, deformational behavior of rim slopes subjected to cyclic fluctuations was suggested. It was elicited that deformations and pore-pressure are inter-related to each other. Deformations occur mostly due to the high excess pore pressure generated after drawdown of each cycle. The rate of deformation reduces during the impounding process. Permanent deformations get accumulated with every passing cycle. Initial cycles of water level fluctuations cause large deformations at a high rate with the possibility of shallow toe failure during the initial cycle drawdowns. The rate of deformation reduces over passage of successive cycles. However, repeated cyclic fluctuations induce weakening effect in the slope mass (strain softening phenomenon). Spread observed from the PSMTs was then used to propose a novel approach to predict displacements along rim slopes due to reservoir water level fluctuations. A dimensionless term called Spread Index (Silab) was defined. The index was defined as the ratio of Spread (horizontal displacement at toe) and Hydro-Fluctuation Belt (HFB) of the cyclic fluctuation in the reservoir water level. Spread index was obtained for all the tests subjected to 30 cycles. Spread index data were used to perform multiple linear regression analysis. The spread index (Silab) was considered to be dependent on slope inclination (θsm), cyclic fluctuation rate (CFR) and number of cycles (Nc). The obtained regression equation was then statistically examined for goodness of fit, multicollinearity among the parameters, hypothesis testing by ANOVA. A correction factor Ci, field was defined to account for the influence of change in the ratio of hydro-fluctuation belt and slope height. Finally, an empirical solution to compute the spread in the field was suggested. The proposed empirical solution was validated by assessing the displacement for three landslide sites. The predicted displacements were compared with the already observed displacements in the field. An error of approximately 10% was observed. Lastly, the proposed empirical solution was used to predict displacements along a rim slope situated in Koteshwar reservoir, India.

2024-06-01T00:00:00Z Singh, Chandra Has http://localhost:8081/jspui/handle/123456789/20485 2026-04-21T10:54:12Z 2024-03-01T00:00:00Z

Title: UAV FOR HILLY AREA MAPPING AND OBJECT DETECTION Authors: Singh, Chandra Has Abstract: In recent years, Unmanned Aerial Vehicles (UAVs), colloquially referred to as drones, have instigated a paradigm shift across diverse industries by delivering unparalleled proficiency in data acquisition, analysis, and surveillance. Among the myriad applications, their role in hilly area mapping and object detection stands out as particularly promising and impactful. The intricate topography and varied landscapes of hilly regions present unique obstacles for conventional mapping methodologies, positioning UAV technology as an indispensable instrument for efficient and precise data procurement. This exposition endeavors to comprehensively elucidate UAV utilization for mapping and object detection in hilly areas, delineating the significance of this technology, elucidating the challenges endemic to traditional methods, and illustrating the transformative influence of UAVs in overcoming these obstacles. Hilly terrains engender a plethora of challenges for conventional mapping techniques. Traditional surveying methods, such as ground-based or manned aerial surveys, are frequently impracticable or economically prohibitive in such rugged environments. Protracted durations, and labor intensive procedures characterize these methodologies and often fail to furnish the requisite level of granularity necessary for precise mapping and object detection in hilly regions. Traditional mapping methodologies confront several inherent limitations when applied to hilly terrain. Primarily, the rugged topography and precipitous inclines render ground-based surveys arduous and perilous for surveyors. Accessing remote or inaccessible regions poses an immense challenge, often leading to incomplete or inaccurate data collection. Though fiscal and logistical constraints frequently beset less encumbered by terrain, manned aerial surveyss. Helicopters or fixed-wing aircraft employed for aerial surveys necessitate specialized equipment and skilled pilots, thereby incurring elevated operational costs. Furthermore, inclement weather conditions such as strong winds or fog can curtail flight operations, further complicating data acquisition endeavors.

2024-03-01T00:00:00Z Meenu krishnan, U. http://localhost:8081/jspui/handle/123456789/20473 2026-04-21T10:50:11Z 2024-08-01T00:00:00Z

Title: ENHANCING COMPUTATIONAL EFFICIENCY IN MULTI-PHYSICS FRACTURE ANALYSIS AND TOPOLOGY OPTIMIZATION USING ADAPTIVE PHASE-FIELD METHOD Authors: Meenu krishnan, U. Abstract: In civil engineering, aging structures often develop cracks over time, posing significant risks of failure. Accurately predicting these failures and understanding the underlying fracture processes are crucial for maintaining structural safety. The phase-field fracture (PFF) method is effective in predicting complex crack patterns such as crack initiation, branching, and merging. However, its high computational cost limits its practical application. This thesis addresses the challenge of computational expense in fracture analysis by uti lizing the phase-field method with efficient algorithms like auto sub-stepping and multi level adaptive mesh refinement (ML-AMR). The study focuses on three different PFF mod els—AT1, AT2, and PF-CZM—to explore the behavior of various materials and geometries under mechanical, thermo-mechanical, and dynamic loading conditions. The integration of ML-AMR into the phase-field method results in a significant reduction in computation time, ranging from 50% to 99%, while preserving accuracy in capturing crack paths, peak loads, and total strain energy in multi-physics scenarios. Additionally, the algorithm is extended to in corporate sparse polynomial chaos expansion (PCE) to predict fractures using the phase-field method. Beyond fracture analysis, this research also focuses on topology optimization, driven by advancements in 3D printing technology. Both density-based and phase-field methods are uti lized to address 3D topology optimization problems. State-of-the-art algorithms, developed in-house, are applied to solve various industrial challenges, including plate and shell topology optimization as well as large-scale optimization problems. These algorithms are further ex tended to design auxetic metamaterials using functionally graded materials, demonstrating their efficiency in enhancing fracture resistance. In the context of industrial applications, the developed tools are used to address large scale engineering problems. This research provides valuable strategies for engineers, offering practical insights to prevent crack-induced failures and improve the durability of engineering structures.

2024-08-01T00:00:00Z Verma, Ravi http://localhost:8081/jspui/handle/123456789/20472 2026-04-20T13:27:28Z 2024-06-01T00:00:00Z

Title: MODELLING OF URBAN DYNAMICS USING URBAN GREEN SPACE AND MULTI-SENSOR DATA Authors: Verma, Ravi Abstract: The process of urbanisation, particularly in emerging nations, like India, is a serious problem because of its profound impact on resources, environment, and human health. Rapid population increase, industrialization, and socio-economic considerations, all contribute to the expansion of urban areas, resulting in challenges, like urban sprawl and the establishment of Urban Heat Island (UHI). Addressing these difficulties demands a thorough understanding of the urban growth dynamics, the importance of Urban Green Spaces (UGS), and UHI mitigation. This study investigates the complex interaction between the urbanisation, UGS, and UHI using modern geospatial technologies such as Geographic Information Systems (GIS) and remote sensing. The study investigates the cooling intensity of UGS and its spatial attributes, focusing on Indian cities, using land use (LU) classes, land cover (LC) indices, Land Surface Temperature (LST) data, and Landscape Metrics (LSM). By analysing existing literature and research approaches, the study emphasises the importance of UGS in promoting healthy urban living, minimising the negative effects of urbanisation, and advancing sustainable development. Furthermore, it emphasises the importance of multidisciplinary approaches that combine ecological, social, and technical perspectives in order to successfully manage UGS and improve urban resilience. This comprehensive study sheds light on the current state of UGS research, its methodology, and its implications for urban planning and sustainability in Indian cities. Objectives of the study are to incorporate UGS into the analysis of urban dynamics in an Indian city utilising remote sensing data. The objectives include characterising urban dynamics and undertaking multi-scale analysis with UGS integration. Sub-objectives include analysing built up features, spatio-temporal changes in urban dynamics, and the urban thermal environment, as well as optimising urban parks. The study looks at spatial attributes of built-up LU class, population correlations, and LST trends in Indian city. It also assesses the impact of UGS on urbanisation and local thermal environment, with a special focus on Lucknow city, India. Chapter 2 presents a spatio-temporal analysis of urban dynamics in Indian cities, with a focus on land use/land cover (LULC) components. Open-source data, including Decadal Land Use Data by ORNL DAAC for India from year 1985 to 2005 and Copernicus Global Land Service Dynamic Land Cover (CGLS-LC100) for year 2015, were used. GIS tools and the RStudio® landscapemetrics library were used to compute LSM for built-up and shrub LU classes in 694 districts. In addition, MODIS LST maps were utilised to analyse temperature patterns, and population data from the year 2001 and 2011 Indian censuses were incorporated to correlate with urban metrics. Results of the chapter focuses on the thorough spatio-temporal analysis i undertaken across India's heterogeneous environment, specifically on LULC changes and their impact on LST in 694 districts. The study used open-source data and GIS technologies to identify substantial correlations between spatial attributes of urbanisation and LST patterns, emphasising the importance of LSM such as Perimeter-Area Ratio (PARA), Fractal Dimension Index (FRAC), Contiguity Index (CONITG), and Core Area Index (CAI). The findings highlight the importance of prioritising UGS in optimising urban sprawl and achieving sustainability. Furthermore, the study clarifies the significance of population dynamics in creating urbanisation patterns, providing important insights for future city planning efforts. Chapter 3 explores urban sprawl in study area of Lucknow Development Authority (LDA), India, using magnitude and direction techniques. It examines the qualitative and quantitative features of urban expansion patterns, classifying them into three types: infilling, edge-expansion, and outlying growth. The study uses LSM over Landsat series data from 1990 to 2020 to investigate the spatio-temporal changes in LDA's urban landscape structure. Concentric buffer analysis is used to comprehend urbanisation patterns across four directional zones. Furthermore, the study employs approaches such as urban growth type classification and LSM calculation to provide a thorough analysis of LDA's changing urban dynamics. Results indicate significant growth of built-up LU in LDA, particularly in the North-East (NE) and South-East (SEE) direction, that too at the expense of vegetation and agricultural loss. Urban sprawl is found to be majorly of edge-expansion type. LSM show dispersion away from the city centre towards the outskirts, with increasing complexity, depicted by Landscape Shape Index (LSI). Shannon's entropy (Hn) suggests dispersed growth, with NE direction having the most dispersion. These findings highlight the importance of comprehensive urban planning in managing urban expansion efficiently in rapidly urbanising places such as LDA. Chapter 4 investigates the impact of UHI on LDA, with a focus on directional buffers from city centre. The study uses same data from chapter 3 to connect LST with spatial attributes of UGS. LC indices such as NDVI, NDBI, NDBaI, and NDWI are used to highlight their correlation with LST trends. Normalisation of LST (NLST) is done to account for seasonal differences. LSM are used to assess how spatial attributes of UGS affect built-up and LST at built-up in neighbourhood. Categories for UGS size, distance from built-up regions, and LST levels have been defined for analysis. Results show that NLST exhibits consistent negative correlation with NDVI and NDWI but positive correlation with the NDBI and NDBaI over time. Spatial analysis unveils urbanization's influence on local climate, with higher temperatures and UHI effects concentrated at the city center. Surface Urban Heat Intensity (SUHI) has escalated due to urban expansion, albeit partially mitigated by UGS initiatives. Dynamic LSM variations from 1990 to 2020 ii indicate shifts possibly due to LU, urbanization, or environmental changes. Notably, UGS impacts built-up areas at close distance only, influenced by UGS size and LSM like Aggregation Index (AI) and LSI. These findings offer valuable insights for policymakers and urban planners to address UHI impacts and safeguard natural landscapes amidst ongoing urbanization trends. Chapter 5 examines the cooling capacity of urban parks in city of Lucknow, India, in the context of rising urbanisation and climate change problems. The study uses satellite images such as Landsat- 8 (30 m) and PlanetScope (3 m) and urban park inventories to better understand how the spatial attributes of these parks affect their cooling impact on neighbouring built-up patches. The study evaluates the cooling distance and intensity of parks using advanced methodologies such as downscaling Landsat-8-derived LST up to the resolution of Planet data (3 m) and analysing LSM, which is critical for analysing impact of urban cooling by urban parks. The process entails digitising parks, categorising LU, downscaling LST, and urban parks’ spatial attributes which culminates in a full analysis of urban parks' contribution to thermal comfort and sustainable urbanisation. In results, urban parks were found to considerably reduce LST within a distance of 18 m from their boundaries, with an average cooling of 2.55 °C, at built-up in neighbourhood. CONTIG, CAI, PARA, and FRAC are spatial attributes associated with urban park cooling. Smaller in size and less complex parks produced larger cooling benefits. The study countered earlier findings by emphasising the small effect of urban park size on cooling the neighbourhood. The findings imply that ideal park designs provide cooling benefits, with implications for urban planning to reduce the UHI effect. The study also emphasises the necessity of taking into account ecological and cultural benefits, as well as cooling impacts, when designing urban parks. The research uses modern geospatial techniques to analyse the complex interaction between the urbanisation, UGS, and UHI in Indian cities. It analyses spatio-temporal remote sensing data and finds that urban sprawl has a major impact on LULC classes, LST trends, and the effectiveness of UGS in mitigating UHI impacts. LSM such as PARA, FRAC, CONITG, and CAI are found to be relevant for analysis of urban dynamic by remote sensing data of various resolutions. The findings highlight the crucial role of UGS in supporting sustainable development and improving urban resilience. Insights on the cooling capability of urban parks (up to 18 m) with specific spatial attributes, highlight the necessity of strategic planning of UGS in combating the negative effects of growing urbanisation and UHI.

2024-06-01T00:00:00Z