Department of Civil Engineering
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Item Comprehensive Seismic Hazard Assessment of Odisha State with Emphasis on Bhubaneswar City and Port Areas(Department of Civil Engineering, NITS, 2025-11-10) Mishra, SatyaprakashItem ASSESSMENT OF TSUNAMI HAZARD AND TSUNAMI RESILIENT STRUCTURE PROPOSED FOR THE COASTAL REGION OF INDIA(Department of Civil, NITS, 2026) Biswas, SohamThis study introduces a comprehensive, multi-scale framework for tsunami hazard assessment that combines global probabilistic forecasting, geospatial vulnerability mapping, and structural resilience analysis to guide risk-informed planning and mitigation. On a global level, a stochastic model estimates future tsunami occurrences across 14 high-risk tsunamigenic zones, using Conditional Probability (CP) and the Total Probability Theorem (TPT). This dual methodology considers both mixed tsunami conditions (any wave height) and high-impact events (wave height > 3 meters). Inter-arrival times are modelled with the best-fitting distributions, such as Gamma, Lognormal, Weibull, and Log-logistic. The tsunami-to earthquake ratio (r) is included in TPT calculations to account for spatial seismic-tsunamigenic variability. Results indicate that, under mixed conditions, the highest tsunami probabilities are in Zones 1 (Colombia, Ecuador, Brazil), 7 (Japan, China, North Korea), and 11 (Solomon Islands, Vanuatu, New Caledonia). For high-impact tsunamis, Zones 3 (Cuba, Jamaica, Haiti), 9 (Malaysia, Brunei, Australia), and again Zone 1 show the greatest risk. This probabilistic framework offers a robust basis for early warning systems and transnational preparedness. A comprehensive tsunami vulnerability assessment for India’s eastern coast was performed at the regional level, utilizing GIS-based spatial analysis combined with a multicriteria decision making framework. Six key parameters, elevation, slope, coastal proximity, land use/land cover (LULC), flow accumulation, and population density, were incorporated through the Analytic Hierarchy Process (AHP) to generate detailed vulnerability maps. These maps highlighted Nagapattinam (166.46 km²), Cuddalore (245.64 km²), Kanyakumari (37.65 km²), Karaikal (35.71 km²), and Chennai (35.63 km²) as highly susceptible coastal zones. To enhance these insights, connectivity overlays depicting major roads, railways, and high-elevation safe locations were added, offering essential guidance for emergency response and sustainable coastal development. On the structural front, the study assessed the tsunami performance of Open Ground Storey (OGS) G+2 reinforced concrete (RC) buildings, with a specific focus on retrofitted stepped column designs. Using Artificial Neural Networks (ANNs) and Monte Carlo Simulations (MCS), fragility curves were developed for three damage states: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP). Compared to traditional RC frames, buildings with stepped columns showed substantial performance improvements, with about a 50% reduction in inter-storey drift and lower exceedance probabilities of 27.05% (IO), 28.57% (LS), and 32.4% (CP). Stepped-column buildings withstood tsunami wave heights of 9.4 m (IO), 11.4 m (LS), and 12.9 m (CP), while regular RC buildings failed at lower wave heights of 7.2 m (IO), 9.3 m (LS), and 10.6 m (CP). The study also compared square and circular stepped column geometries, highlighting the crucial role of column shape in enhancing tsunami resistant performance. At a 50% exceedance probability, circular columns withstood greater wave heights of 10.3 m (IO), 12.1 m (LS), and 13.6 m (CP), outperforming square columns by 9.6%, 6.1%, and 5.5%, respectively. They also exhibited reduced inter-storey drift ratios and delayed hinge formation, indicating superior ductility and energy dissipation. Although circular columns involve approximately 9% higher initial construction costs, they provide long-term economic benefits, including 5–10% reductions in overall lifecycle losses. These findings confirm that circular stepped-column retrofits offer a cost-effective and structurally resilient solution for tsunami-prone infrastructure. Overall, this study integrates global tsunami forecasting, regional hazard mapping, and performance-based structural design into a unified framework, providing practical insights for infrastructure planning, disaster preparedness, and coastal policy-making, particularly in highly vulnerable areas such as coastal India.Item REHABILITATION OF BEAM-COLUMN JOINT USING GEOPOLYMER ADOPTING REMOVAL AND REPLACEMENT TECHNIQUE(Department of Civil Engineering, NITS, 2022) Choudhury, Arshad HussainThe most critical component of reinforced concrete (RC) moment resisting frame is the beam-column joint. Beam-column joint plays an important role in load transfer mechanism between horizontal and vertical components of a RC framed structure. Integrity of beam column joint influences structural stability of RC frame during an earthquake. During lateral loading, beam-column joint goes through large inelastic displacement. Substandard beam-column joint which were designed, detailed and constructed prior to the development of any seismic code of practice lacks in adequate confinement reinforcement and sufficient anchorage length of the beam reinforcement. Such substandard beam-column joint when subjected to large lateral loads during an earthquake, may lead to formation of diagonal cracks and concrete crushing in joint core resulting in sudden brittle shear failure. With advent of seismic code of practice, beam-column joints are being constructed with special confining hoop reinforcement and adequate anchorage. Such seismically detailed beam column joints exhibit better stiffness, ductility and higher energy dissipation capacity. Recently some studies revealed that reinforced concrete (RC) structures designed as per seismic code of practice exhibited damage in joint region when maximum force acting on the joint exceeded the code expectation level. If the damaged joint is not repaired and strengthened, it may cause instability in structure. In order to mitigate structural instability, rehabilitation of damaged beam-column joint is carried out. Rehabilitation is a simple and effective process of repairing and strengthening the damaged structures to the service level so as to provide safety to the occupant. Rehabilitation by removal and replacement technique is generally used for severely damaged beam-column joint. In removal and replacement technique, the fragmented concrete is removed from the damaged joint region and replaced with suitable repair material. From reported literature it was observed that the repair materials that were used to rehabilitated beam-column joint adopting removal and replacement technique were mostly cement based (concrete, cement paste, cement mortar and commercial micro concrete). Due to utilisation of natural resources and huge environment cost associated with production of cement, scientist and researchers were looking for a sustainable binding material. Geopolymer has immerged out as an environmentally friendly cementitious material alternative to ordinary Portland cement (OPC). Use of industrial by-products like ground granulated blast furnace slag (GGBS), fly ash as source material contributed to lower CO2 emission and energy consumption during production stage makes geopolymer an eco friendly green material. In last decade, geopolymer due to better physical and mechanical properties, has shown immense potential as repair material alternative to cement-based materials. But the repair works reported in literatures was associated with structures subjected to monotonic loading only. Till date, no study was carried out to investigate suitability of geopolymer based material on rehabilitation of structures subjected to cyclic loading. In the present investigation, an attempt has been made to rehabilitate substandard as well as seismically detailed beam-column joints following removal and replacement technique using geopolymer mortar (GM). Two one-third scaled substandard beam-column joints having different percentages of beam and column longitudinal reinforcements designed as per pre 1993s design code (substandard) of Indian Standard were used as control specimens. Both control and rehabilitated specimens were tested under quasi-static cyclic loading. Test results indicated that geopolymer mortar were successful in restoring the cyclic performance of damaged substandard beam-column joint in terms of peak load, stiffness degradation, ductility, energy dissipation and damage index up to the level of control specimens but exhibited undesirable brittle joint failure, similar to that of substandard control specimens. For seismically detailed specimens, two one-third scaled beam-column joints of two different groups categorized on flexural strength ratio (1.2 and 1.4), designed as per seismic code of practice were considered in the study. To compare performance of geopolymer mortar with that of cement mortar, another set of seismically detailed beam-column joint (one from each group) were rehabilitated with cement mortar following same rehabilitation technique. Seismically detailed specimens rehabilitated with geopolymer mortar exhibited 20 – 21% higher initial stiffness, 19 – 22% higher displacement ductility and 23 – 24% higher cumulative energy dissipation compared to specimens rehabilitated with cement mortar. But irrespective of repair material, removal and replacement technique was only able to partially restore the cyclic performance of rehabilitated specimens. This was attributed to presence of yielded hoop reinforcement and cold joints in seismically detailed specimens. When removal and replacement technique was used to rehabilitate seismically detailed beam-column joint, only fragmented concrete was removed and yielded hoops from the damaged joint core are not removed and it remained inside the joint core after rehabilitation. Therefore, investigation was carried out to study the effect of yielding of hoops on cyclic performance of seismically detailed beam-column joint. For this purpose, four numbers of one-third scaled beam-column subassemblies with flexural strength ratio 1.4 were prepared. All four specimens were designed and detailed following relevant provisions of Indian Standard Code of Practice. Out of the four specimens, one was used as control specimen and the rest three were cast with different magnitude of pre-yielded hoop reinforcement in the joint core. It was observed that specimens with yielded hoop reinforcement showed lower peak load, lower ductility and lower energy dissipation along with higher stiffness degradation and damage index. On the other hand, due to removal and replacement, there exists cold joint between old concrete and new repair material. Moreover, yielded hoops remain inside the repaired joint core after rehabilitation adopting removal and replacement technique. Till date, no attempt has been made to investigate combined effect of cold joint and yielded hoop reinforcement on cyclic behaviour of seismically detailed beam-column joint. When tested under cyclic loading, specimens with both cold joint and yielded hoop reinforcement shown the highest reduction in cyclic performance in terms of initial stiffness, stiffness degradation, ductility and cumulative energy dissipation compared to individual effect of either cold joints or yielded hoops. Between individual parameters – cold joint and yielded hoop reinforcement, cold joint played a more dominant role. This was established by reduced cyclic performance of specimens with cold joint in terms of initial stiffness (15 -17% lower), displacement ductility (7 – 9% lower) and energy dissipation (18 – 19% lower) in comparison to specimens with yielded hoops. It was observed that though GM was able to enhance performance of rehabilitated specimens but was not able to alter mode of failure brittle joint failure to ductile beam failure in substandard specimens. Furthermore, when seismically detailed specimens were rehabilitated with geopolymer mortar, rehabilitated specimens displayed beam failure accompanied by joint failure as well as specimens exhibited lower performance compared to control under cyclic loading. Such failure is undesirable and unacceptable as per current seismic design philosophy. To mitigate underperformances in beam-column joints, steel fiber reinforced geopolymer mortar (SFRGM) was used for rehabilitation of both substandard and seismically detailed specimens adopting same repair technique. For substandard specimens, SFRGM not only increased performance of rehabilitated specimens substantially above the level of controls but also alter mode of failure to ductile beam failure. In case of seismically detailed beam-column joints, specimens rehabilitated with SFRGM exhibited beam failure along with 28 – 34% increased peak load, 20 – 26% increased initial stiffness, 109 – 113% increased ductility, 177 – 212% higher cumulative energy dissipation, substantial lower stiffness degradation and lower damage index.Item STUDY ON THE RIVER BANK EROSION FOR THE ALLUVIAL PLAINS OF THE BARAK RIVER, NORTHEAST INDIA(Department of Civil, NITS, 2025) Das, TinkleRiverbank erosion is a significant and distinct problem in the floodplains of alluvial rivers in North East India. The Barak River has experienced an alarming increase in bank erosion rate over the last few decades, resulting in embankment breaches and biodiversity loss, but there is a dearth of field studies to evaluate riverbank fluvial erosion. This study aims to assess the river bank erosion and Vulnerability Assessment of the Barak River in India using an in-situ submerged Jet Erosion Test (JET). The riverbank erosion was estimated for a span of the riverbank on one side of the stream using the excess shear stress equation and impinging jet theory. A total of Forty in-situ submerged JET tests were performed at ten locations to determine the soil erodibility parameters (i.e., critical shear stress and erodibility coefficient) of the composite riverbanks along the river. The upper reach of the Barak River up to Badarpur Ghat in Assam was considered for the study area. The results show that the spatial variation of the erodibility parameters in the river banks varies significantly, with the magnitude of critical shear stress 𝜏𝑐 and erodibility coefficient 𝑘𝑑 of the composite river bank soils ranging from 0.1 to 55 Pa and 0.1 to 10 cm3/N-s, respectively. The analysis of erosion-vulnerable sites based on the JET test showed that the soil could be classified as very erodible to moderately erodible, which is highly vulnerable to erosion. Annual bank erosion was computed using measured erodibility parameters and stage record data, which shows the bank erosion of the Barak River will occur in many places, particularly at the critical area. The measured bank erosion was compared with the observed satellite imagery map for the 2000-2020. The results of the in-situ jet tests using the excess shear stress approach are more reliable in predicting erosion rates than the empirical method and are also helpful in identifying river bank locations that are more vulnerable to erosion. Estimating the magnitude of bank erosion is essential for planning any bank protection work. However, conducting comprehensive in-situ surveys to measure bank erosion rates for a large area is time-consuming, this study integrates machine learning (ML) techniques to predict spatial riverbank erosion across the entire study area. In-situ JET measurements were used to determine erosion rates at specific sites, and key influencing factors such as developed shear stress, bankfull depth, bankfull days, reach slope, discharge, vegetation cover, soil texture, bank material size, bank slope, and curvature were identified. The dataset was randomly split for training and testing, and four ML algorithms random forest (RF), decision trees (DT), multiple linear regression (MLR), and gradient boosting regressor (GBR) were employed to establish the correlation between bank erosion and the influencing factors, and the predictive performance of each ML model was analyzed using statistical indices such as R-squared, NSE, RMSE, and NRMSE. Among the four ML models tested, the Random Forest (RF) model demonstrated better performance (R-squared: 0.96, RMSE: 2.34, NRMSE: 0.14, NSE: 0.96) and was chosen as the optimal methodology to calculate the spatial distribution of river bank erosion throughout the study area. The developed shear stress and bankfull depth are the most influential variables affecting river bank erosion. Moreover, discharge, percentage of clay, mean bank material size (D50), reach slope, and bank angle are other essential variables as well. Findings also show that the areas with high-developed shear stress and steep slopes have relatively high bank erosion rates. The optimal (RF) model was applied to estimate the riverbank erosion rate over the entire study area, and the resultant values were then spatially mapped in GIS to visualize annual bank erosion distribution. The developed bank erosion map was verified through additional submerged JET data This study demonstrates that integrating the submerged JET test and ML techniques has proven highly effective for the spatial modeling of riverbank erosion, and the proposed methodology can be implemented elsewhere. However, a primary challenge in implementing this methodology is identifying and selecting the ideal input variables and obtaining sufficient observations to adequately explain bank erosion variability that may not be readily available in all locations. Another advantage lies in the ability to integrate ML inputs into GIS to map the spatial estimated riverbank erosion rates and serve as a tool for zoning bank erosion hazards. Therefore, the methodology adopted in this present study can be used in similar environments to map bank erosion and prioritize bank stabilization treatments or other preventive measures. By combining field-based measurements with advanced ML techniques, this research contributes to a deeper understanding of riverbank erosion dynamics and provides a robust framework for future erosion assessment and management strategies.Item Integrating Data-Centric Insights With Bayesian Approach For Robust Geotechnical Predictions(Department of Civil, NITS, 2026) Kumar, DhawalUncertainty is an unavoidable aspect of geotechnical engineering, driven by the inherent variability of soils, the incompleteness of site investigations, and the practical limitations of time, cost, and accessibility. Deterministic methods remain the dominant practice, but they reduce uncertainty to fixed parameters and rely on conservative safety factors. While simple, such approaches often either overestimate risk, leading to uneconomical designs, or underestimate it. Probabilistic approaches provide a more rigorous way to manage uncertainty, and among these, Bayesian methods are particularly compelling. They allow engineers to incorporate prior knowledge, update predictions as new data becomes available, and represent both parameters and predictions as probability distributions. Despite these advantages, the adoption of Bayesian methods in practice has been limited by technical complexity, lack of software support, and unfamiliarity among practitioners. This thesis addresses this gap by demonstrating how Bayesian frameworks can be applied to key geotechnical problems, improving prediction, interpretability, and decision-making under uncertainty. The motivation for this research lies in the need to bridge the divide between probabilistic theory and engineering practice. Four geotechnical problems were chosen deliberately because they represent core aspects of design and analysis where uncertainty is unavoidable, existing deterministic or empirical models are inadequate, and data are available but often underutilised. The estimation of shear wave velocity (Vs) from SPT blow count (N) is fundamental for seismic site characterisation, yet regional variability leads to inconsistent predictions. Bearing capacity estimation from Plate Load Tests (PLTs) is central to foundation design but is hindered by limited data and model generalisability. Groutability prediction in permeation grouting is a safety-critical process where complex soil–grout interactions defy simple modelling. Soil liquefaction prediction is one of the most important challenges in earthquake engineering, where the consequences of misclassification are severe. Together, these problems span different domains of geotechnical engineering while sharing the unifying theme of decision-making under uncertainty, making them ideal for testing and demonstrating Bayesian approaches. Shear wave velocity (Vs) is a fundamental parameter for assessing soil stiffness and dynamic response, playing a central role in earthquake engineering, site classification, and liquefaction evaluation. Its empirical correlation with Standard Penetration Test (SPT) blow count (N) provides a practical means for estimating Vs from commonly available field data. However, this correlation is known to vary across regions due to differences in geological history, depositional environment, and testing practices. In this objective, a two-level Bayesian multilevel regression model is used to capture these regional differences systematically. By transforming both variables into their natural logarithms (ln Vs and ln N), the model assumes a common slope across regions and allows for region-specific intercepts. This hierarchical structure captures both within-region variability and between-region differences, enabling better predictions in data-scarce regions through partial pooling. The Bayesian framework also quantifies uncertainty in both parameters and predictions, facilitating the development of regional Vs maps for seismic hazard assessments. Compared to traditional single-level regressions, the multilevel model significantly improves predictive accuracy and interpretability by accounting for the hierarchical nature of the data. The Plate Load Test (PLT) is widely used to evaluate the bearing capacity of shallow foundations under both Ultimate Limit State (ULS) and Serviceability Limit State (SLS) conditions. However, the test is expensive, time-consuming, and often site-specific, limiting the availability of large-scale datasets. Existing machine learning (ML) models trained on PLT data often suffer from overfitting and limited generalizability. To address these challenges, the objective is to develop an ensemble learning framework using Adaptive Boosting (AdaBoost) and Gradient Boosting (GBoost) models optimised through Bayesian Optimisation (BO). A dataset of 576 PLT records from 58 sites was compiled, incorporating input features such as settlement (S), undrained cohesion (Cu), footing width (B), embedment depth (Df), groundwater depth (Dw), and unit weight (γt). The Bayesian optimised GBoost model achieved superior performance compared to both AdaBoost and conventional Artificial Neural Networks. Bayesian Optimisation enabled the efficient tuning of hyperparameters to minimise generalisation error and avoid overfitting. The proposed method provides a robust and interpretable approach for bearing capacity prediction and can be integrated into preliminary design stages, improving efficiency and reliability. Permeation grouting using cement-based suspensions is an essential ground improvement technique, especially for controlling groundwater inflow during tunnelling and underground construction. The effectiveness of grouting depends on several interacting factors, including soil gradation, grout composition, water-cement ratio, and injection pressure. Predicting whether a grouting attempt will succeed (groutability) is challenging due to these complex, nonlinear interactions and the inherent variability in soil conditions. Existing approaches, especially Discrete Bayesian Networks (DBNs), suffer from information loss due to the discretisation of continuous variables, limiting their predictive capabilities. This objective introduces a Hybrid Bayesian Network (HBN) that allows for the integration of both discrete and continuous variables, thereby preserving the richness of continuous soil parameters while maintaining the causal interpretability of BN structures. The HBN was constructed using the bnlearn package in R and benchmarked against DBNs and machine learning classifiers such as Support Vector Machines and Naïve Bayes. The HBN achieved high accuracy, recall, and AUC, while remaining robust to class imbalance and missing data. Its computational efficiency and interpretability make it a valuable tool for field practitioners dealing with groutability assessment and project risk management. Soil liquefaction is one of the most destructive phenomena that can occur during earthquakes. This objective explores the impact of variable transformation and model formulation on the performance of logistic regression for predicting earthquake-induced soil liquefaction. Using 399 field case histories, the variables qc1N and CSR7.5 were transformed into √qc1N and ln(CSR7.5) to better satisfy assumptions of linearity and normality. Probability contour plots for sand and sand mixtures reveal distinct patterns: a nonlinear interaction in sands. Bayesian logistic regression models were developed using both original and transformed variables and a benchmarked empirical method. Comparisons on an independent validation set of 101 cases show that the Bayesian model consistently offers higher accuracy and precision, particularly at lower probability thresholds. Unlike fixed-parameter models, the Bayesian framework captures uncertainty in model coefficients and produces probabilistic boundaries that better reflect the transitional nature of liquefaction behaviour. The model’s conservative classification at higher resistance levels makes it advantageous for seismic risk mitigation. Across these four studies, the thesis demonstrates the practical strengths of Bayesian frameworks in geotechnical engineering. They provide explicit quantification of uncertainty, enhance interpretability through distributions, feature importance, and causal structures, and adapt to new data through iterative updating. The consistent use of published datasets ensured reproducibility and transparency, showing that existing information can be leveraged effectively without requiring costly new testing. In conclusion, this thesis argues for a paradigm shift in geotechnical engineering from deterministic simplifications toward probabilistic, transparent, and data-conscious methods. By demonstrating how Bayesian approaches can be successfully applied to problems of central importance, it shows that uncertainty need not be treated as a limitation but as an opportunity to make predictions that are safer, more interpretable, and more adaptable. In doing so, it bridges the gap between statistical rigour and engineering practice, offering tools that are not only theoretically sound but also practical for real-world decision-making. Keywords: Bayesian inference, multilevel modelling, ensemble learning, Bayesian optimisation, hybrid Bayesian networks, liquefaction prediction, geotechnical uncertainty