Title: NG Civil Engineering Frequency: Quarterly ISSN (online): 3107-569X Chief Editor: Dr. Bibhuti Bhusan Sahoo Starting Year: 2025 Subject: Civil Engineering Language: English Publication Format: Online Publishing Model: Open Access Publisher: Next Gen Academic Services
GIS and AHP-Based Landslide Susceptibility Mapping of the Teesta River Basin, Eastern Darjeeling-Kalimpong Himalaya, India
Landslides are among the most recurrent hydro-geomorphic hazards in the Eastern Himalaya, where steep terrain, weak lithology, intense monsoonal rainfall, high drainage dissection and increasing anthropogenic pressure collectively reduce slope stability. This study prepared a landslide susceptibility map for a part of the Teesta River Basin covering Darjeeling and Kalimpong districts, West Bengal, India, using Geographic Information System (GIS), Remote Sensing (RS) and the Analytic Hierarchy Process (AHP). Seventeen landslide-conditioning factors were incorporated: altitude, slope, aspect, curvature, relative relief, lineament density, distance from lineament, land use/land cover, soil, Normalized Difference Vegetation Index, lithology, geomorphology, drainage density, rainfall, Topographic Wetness Index, distance from drainage and Stream Power Index. Thematic layers were derived from DEM, satellite data, geological and soil information, rainfall data and ancillary spatial datasets, standardized in a GIS environment and integrated through weighted overlay analysis. The resulting susceptibility map classified the basin into very low, low, moderate, high and very high susceptibility zones. Moderate susceptibility occupied the largest area (69.85 km²), followed by high susceptibility (68.74 km²), low susceptibility (48.94 km²), very high susceptibility (37.87 km²) and very low susceptibility (19.48 km²). The northern and central mountainous sectors, especially around Kalimpong and Kalimpong-I, were more susceptible than the southern foothill areas. The map provides a spatially explicit decision-support layer for land-use planning, infrastructure alignment, disaster risk reduction and sustainable watershed management in the Teesta Himalayan landscape.
Copyright (c) 2026 NG Civil Engineering https://creativecommons.org/licenses/by/4.0
Remote Sensing and GIS-Based Flood Hazard and Susceptibility Mapping: A Review of Methods, Datasets and Applications
Flood hazard and susceptibility mapping has become a core component of disaster risk reduction because it converts hydrological, geomorphological, meteorological, land-cover, and exposure information into spatial products that can guide preparedness, land-use planning, infrastructure siting, agricultural risk management, and emergency response. Remote sensing and geographic information systems (GIS) have transformed this field by providing synoptic Earth observation, repeat monitoring, terrain derivatives, rainfall products, land-use dynamics, flood inventories, and computational environments for multi-criteria, statistical, machine-learning, and cloud-based modeling. This review synthesizes recent and foundational literature on remote sensing and GIS applications in flood hazard and susceptibility mapping, with emphasis on data sources, conditioning factors, model families, validation practices, operational limitations, and future research needs. The review distinguishes flood hazard, susceptibility, vulnerability, exposure, and risk; evaluates optical, synthetic aperture radar (SAR), digital elevation model, rainfall, soil, land-cover, and ancillary datasets; and compares weighted overlay, analytical hierarchy process, frequency ratio, weights of evidence, logistic regression, support vector machine, random forest, XGBoost, convolutional neural network, deep-learning, and explainable-AI approaches. The evidence shows that GIS-based multi-criteria analysis remains useful in data-poor contexts, while SAR-derived flood inventories and machine-learning models are increasingly important for empirical, repeatable, and event-based susceptibility mapping. However, model reliability is constrained by inventory quality, spatial sampling design, class imbalance, scale dependence, DEM uncertainty, transferability limits, and insufficient validation against independent flood events. The review concludes that the strongest future direction is not simply adopting more complex algorithms, but building transparent, validated, multi-source, reproducible, and decision-oriented flood-mapping workflows that combine physical knowledge, Earth observation, local evidence, and explainable geospatial artificial intelligence.
Analysis and Design of a G+4 Reinforced Concrete Residential Building Using ETABS
The structural design of low- to mid-rise residential buildings in urban areas requires reliable assessment of gravity and lateral load effects, appropriate member sizing, and code-compliant detailing of reinforced concrete elements. This paper presents the analysis and design of a G+4 reinforced concrete residential building modelled in ETABS. The building was idealized as a reinforced concrete framed structure with five storeys, 3.0 m storey height, M30 concrete, Fe500 reinforcement, 150 mm slab thickness, 400 mm x 500 mm beams, and square and rectangular columns of 450 mm x 450 mm and 350 mm x 450 mm, respectively. Loads were assigned in accordance with Indian code provisions for dead, imposed and wind loads, and the frame was analysed for displacement, shear force, bending moment and axial force response. The results indicate that the adopted member sizes are structurally adequate for the considered G+4 residential configuration under the applied load cases, subject to final validation using project-specific soil investigation data, seismic parameters and detailed construction drawings. The study demonstrates the usefulness of ETABS as an integrated modelling, analysis and design environment for reinforced concrete building systems, while emphasizing that software results must be supported by engineering judgement, code checks and quality control during execution.
Use of Foundry Sand in Geopolymer Concrete: A Comprehensive Review
The reuse of waste foundry sand (WFS) in geopolymer concrete is an important circular-economy strategy to reduce ordinary Portland cement consumption and conserve natural river sand. WFS is generally silica-rich, thermally stable and abundantly available from casting industries, but its clay content, binders, carbonaceous additives, residual metals and variable fineness can affect concrete performance and environmental safety. This review synthesizes recent studies on the use of WFS as fine aggregate, filler and potential silica contributor in geopolymer concrete. It discusses material characteristics, pre-treatment requirements, mix design parameters, fresh and hardened properties, durability, microstructure, leaching behaviour, and sustainability implications. Existing evidence suggests that properly processed WFS can improve particle packing and strength at moderate replacement levels, typically 10–30%, depending on precursor type, activator concentration, and curing regime. However, excessive WFS may reduce workability, increase activator demand and weaken the interfacial zone due to fines and organic residues. The review concludes that WFS-based geopolymer concrete is technically promising, but wider adoption requires standardised characterisation, long-term durability studies, leachability assessment, field validation and performance-based specifications.
Performance Improvement of Asphalt Mixes with Liquid Polymers and Warm Mix Technology: A Case Study
The performance and durability of bituminous pavements are strongly influenced by the binder characteristics and the asphalt mix composition. This study evaluates improvements in asphalt mix performance through the incorporation of a liquid polymer, Vinyl Acrylic Copolymer (VAC), and warm-mix technology in bituminous concrete. VG-30 bitumen was modified with 0.5% liquid polymer to develop a liquid-polymer-modified binder (LPMB), and its behaviour was compared with that of conventional mixes prepared with VG-30. Bituminous concrete mixes were prepared with lime and fly ash as fillers, and their optimum binder contents were determined using the Marshall mix design method. Results showed that incorporating a liquid polymer improved key performance parameters of the mixes, including Marshall stability, retained stability, indirect tensile strength, and tensile strength ratio. Mixes prepared with fly ash performed better than those with lime, indicating enhanced resistance to moisture damage and improved overall stability. The tensile strength ratio increased from 86.51% in the conventional VG-30 mix with lime to 91.12% in the polymer-modified mix, while retained stability also improved from 88.56% to 90.32%. Although the elastic recovery of the modified binder did not fully satisfy IRC SP-53 requirements, the modified mixes demonstrated better engineering performance than conventional mixes. The study concludes that liquid polymer modification, combined with appropriate filler selection and warm-mix technology, can enhance the functional properties of asphalt mixtures, resulting in more durable, sustainable pavement construction.
A Review on Nonstationary Flood Frequency Analysis under Climate and Catchment Change
Flood frequency analysis has traditionally relied on the assumption that hydrological extremes fluctuate within a stationary probabilistic regime, allowing past observations to serve as a basis for estimating future flood risk. That assumption has become increasingly questionable in many river basins affected by climate change, land-use transformation, urbanization, river regulation, and evolving hydroclimatic variability. In response, nonstationary flood frequency analysis has emerged as a major research area in modern hydrology. This review critically examines the conceptual basis, statistical frameworks, covariate strategies, uncertainty sources, and practical implications of nonstationary flood frequency analysis. It discusses the hydrological mechanisms that generate nonstationarity, evaluates methodological developments including time-varying extreme value models, generalized additive models for location, scale and shape, Bayesian approaches, and regional methods, and assesses the opportunities and limitations of machine learning and hybrid frameworks. The review argues that nonstationary analysis should not be treated as a universal replacement for stationary frequency methods, but as a context-dependent approach that must be justified by hydrological evidence, physically meaningful covariates, and decision relevance. Particular attention is given to uncertainty, because flexible nonstationary models often improve in-sample fit while weakening extrapolative reliability. The review concludes that future progress depends on stronger process-statistics integration, more disciplined covariate selection, clearer reporting standards, and a shift from fixed return periods toward design-life risk and reliability-based planning. These directions are essential if nonstationary flood frequency analysis is to become a robust tool for hydrologic design and flood risk management.
Strength Performance of Concrete Incorporating Copper Slag and Hooked-End Steel Fibers
River sand depletion and the growing burden of industrial waste have increased interest in alternative concrete materials. This study evaluated copper slag as a partial replacement for natural fine aggregate in conventional concrete and steel fiber reinforced concrete (SFRC) of grades M30 and M40, designed as per IS 10262. Workability was assessed by slump, while compressive, flexural, and split tensile strengths were measured at specified curing ages. Copper slag improved workability because of its lower water absorption than sand, but strength improved only up to an optimum replacement level. The best performance was observed at 50% replacement for M30 conventional concrete and 40% for M40, while SFRC achieved the highest 28-day compressive strength at 40% replacement. Compared with conventional mixes, SFRC showed higher compressive, flexural, and split tensile strength, along with improved crack control and ductility. The study supports optimized copper slag use for more sustainable and better-performing concrete.
Groundwater Contamination Sources, Pathways, Health Risks, Monitoring and Management
Groundwater is a vital source of drinking water, irrigation, and industrial supply, but its quality is increasingly threatened by both natural and human-induced contamination. Because aquifers are hidden and often slow to flush, pollutants can remain undetected and persist for long periods. This review examines the major sources, transport mechanisms, impacts, monitoring approaches, remediation options, and governance challenges associated with groundwater contamination. It covers geogenic contaminants such as arsenic, fluoride, salinity, and uranium; anthropogenic pollutants from agriculture, sanitation, mining, and industry; and emerging contaminants including per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, and microplastics. The review shows that contamination is strongly shaped by hydrogeology, recharge, redox conditions, and land use, making it a complex multi-contaminant problem. Effective management requires prevention-first governance, risk-based monitoring, source control, targeted treatment, and long-term aquifer protection.
Assessing Groundwater Availability and Agricultural Productivity in Guntur District, Andhra Pradesh
The availability of groundwater in semi-arid areas of India is decisive to agricultural production, although it can be easily evaluated independently of crop structure, irrigation sources, and hydrological stress at the district level. The paper will provide a synthesized secondary-based data analysis of the groundwater availability and agricultural productivity in the undivided Guntur district within the state of Andhra Pradesh. The district has an average rainfall of approximately 853 mm annually and a cropping intensity of 134.6 therefore indicating a very productive yet a water sensitive agrarian system. Although the estimates of the groundwater resources in the district scale surface suggest an extensive net dynamic availability, results in the pre-monsoon groundwater levels and the localized overexploited areas indicate the emergence of stress in the individual mandals. The concentration of crop in paddy, cotton, pulses and maize, coupled with the presence of canal water and ground water irrigation, demonstrates that agricultural output does not only hinge on the overall amount of water but also on the spatial dependability, accessibility during a specific season and irrigation control. The paper suggests that the future of agricultural sustainability in Guntur will be based on conjunctive water utilization, focused recharge, micro-irrigation, groundwater surveillance, as well as a progressive transition in stressful regions to agricultural crop options with high water consumption.
Sustainable Concrete Building Using Clever Materials and Technologies: A Review
This paper highlights the growing need for environmentally responsible practices that can reduce the construction sector’s ecological footprint, using an overview of recent developments and future directions in sustainable concrete. It reviews key materials and approaches such as green concrete, supplementary cementitious binders, porous concrete, and the use of locally available resources to minimize impacts. The discussion also covers emerging innovations, including self-healing concrete, 3D-printed construction materials, photocatalytic concrete, electrified construction equipment, and carbon capture, utilization, and storage (CCUS) strategies. In addition, it outlines the major technical, economic, and social barriers that must be addressed, while emphasizing the value of coordinated efforts among government, industry, and academia. Overall, the move toward sustainable concrete is being accelerated by data-driven decision-making, digital technologies, and circular-economy thinking.
Predicting Concrete Compressive Strength Using an Ensemble Machine Learning Model
Accurate prediction of concrete compressive strength (CCS) is essential for ensuring structural safety, optimizing mix design, and promoting sustainable construction practices. Traditional empirical models often fail to capture the complex nonlinear relationships among concrete constituents, leading to limited predictive reliability for modern concrete mixtures. This study proposes an advanced ensemble machine learning framework for robust and accurate prediction of CCS. Multiple individual learning models—Artificial Neural Network, Support Vector Regression, Random Forest, and Extreme Gradient Boosting—were developed and integrated using bagging, boosting, and stacking ensemble strategies. The performance of each model was evaluated using standard statistical metrics, including the coefficient of determination (R²), root mean square error, mean absolute error, and mean absolute percentage error. Results demonstrate that ensemble models significantly outperform individual learners, with the stacking-based ensemble achieving the highest predictive accuracy and lowest error values. Feature importance analysis further revealed that curing age, cement content, and water–cement ratio are the most influential parameters governing strength development. The proposed framework provides a reliable, cost-effective, and interpretable solution for concrete mix optimization and quality control, offering substantial potential for practical implementation in sustainable construction engineering.
Copyright (c) 2025 NG Civil Engineering https://creativecommons.org/licenses/by/4.0
Modelling Floodplain Boundary Shear Distribution in Two-Stage Meandering Channels Using Machine Learning Techniques
The distribution of boundary shear stress plays a pivotal role in addressing various river engineering challenges, including flood control, sediment transport, and riverbank stabilisation. This study employs three machine learning (ML) techniques, i.e. Gaussian Process Regression (GPR), Extreme Learning Machine (ELM), and Relevance Vector Machine (RVM), to predict boundary shear distribution along the floodplains of a two-stage sinuous channel. Key geometric and hydraulic parameters such as the relative width , depth ratio , sinuosity , slope of the channel bed , and sinuous relative belt width of the two-stage meandering channels are used to create predictive models. A comprehensive comparative analysis was conducted using standard statistical performance metrics. Among the models, the GPR approach demonstrated superior predictive accuracy, achieving a coefficient of determination (R²) of 0.984 and the lowest root mean squared error (RMSE) of 2.0. These findings highlight the potential of GPR as a robust tool for modelling shear stress distribution in complex channel geometries.
Daily River Flow Forecasting in the Baitarani River, Odisha
Accurate streamflow forecasting is essential for sustainable water resource management, particularly in climatically sensitive river basins. This study applies the Autoregressive Integrated Moving Average (ARIMA) modeling framework to forecast monthly streamflow of the Baitarani River basin in eastern India using observed discharge data from 2000 to 2020. Preliminary analysis revealed pronounced seasonal variability and non-stationary behavior in the raw time series, necessitating data transformation through differencing to achieve stationarity. Model identification was performed using autocorrelation and partial autocorrelation functions, and the optimal ARIMA structure was selected based on information criteria and diagnostic testing. The selected model demonstrated strong predictive performance, achieving a Nash–Sutcliffe Efficiency of 0.82 and coefficient of determination (R²) of 0.87, while residual diagnostics confirmed model adequacy. The forecasting results effectively reproduced observed hydrological patterns, including monsoon-driven peak flows and low-flow conditions. The findings indicate that ARIMA-based forecasting provides a robust and computationally efficient decision-support tool for reservoir operation, flood management, irrigation planning, and long-term water resource management in data-scarce regions. Future work should integrate climatic and land-use variables to further improve predictive reliability under changing hydro-climatic conditions.
Water Quality Assessment of Angul District, Odisha: A Comprehensive Study of Surface and Ground Water Contamination
This study aims to evaluate the physico chemical quality of surface and ground water in Angul district, Odisha, an industrial hub dominated by coal based and aluminium industries. Twenty sampling sites across rural, agricultural, industrial, and residential zones were monitored post monsoon. Samples were analysed for pH, total dissolved solids (TDS), total hardness, chloride, nitrate, sulphate, electrical conductivity (EC), biological oxygen demand (BOD), and chemical oxygen demand (COD). The results show that pH ranged from 6.4 to 8.3; TDS from 200 to 1100 mg L 1; hardness from 120 to 450 mg L 1; chloride remained below 250 mg L 1 in most sites; nitrate reached up to 48 mg L 1; BOD varied from 1.5 to 6.8 mg L 1; COD values reached 90 mg L 1. Industrial zones exhibited higher TDS, hardness, and COD, while agricultural areas showed elevated nitrates. Comparative evaluation against BIS IS 10500:2012 and WHO standards indicates that several sites exceed permissible limits, signaling significant pollution. Spatial analysis highlights downstream contamination gradients. The study underscores the need for stringent effluent management, adoption of organic farming, community-based sanitation infrastructure, and real time monitoring. It contributes baseline data for policymakers and researchers in designing sustainable water resource management strategies.
Hydrologic Modelling of Mahanadi River Using Rainfall-Runoff Method
Hydrological modeling is a real-world system which implies the simplification that leads for better understanding, managing and prediction of water resource system. These hydrological models are used to estimate the rainfall–runoff behaviors. This study shows the results of rainfall forecasts obtained with mathematical analysis techniques, using past rainfall data for thirty years. This study describes the hydrological model of the largest river of Odisha i.e. the Mahanadi. Hydrological models are formulated using thirty years of rainfall data. Analytical models are developed to analyzing and forecasting the behavior of the river during rain periods throughout every season of the year. For rainfall prediction linear regression analysis is done which gives the forecasting data of next seven years. The discharge or the estimated runoff is calculated using Mockus model which is the empirical formula of SCS method by taking some river stations of the Mahanadi. In this thesis, flood forecasting models based on linear regression have been evaluated along with other performances using analytical model using MS Excel has been analyzed for a channel reach of the Mahanadi River in Odisha.
Experimental Investigation of Reinforced Concrete Incorporating Copper Slag and Steel Fibers
The increasing scarcity of natural river sand and the environmental hazards associated with its extraction have necessitated the exploration of sustainable alternative materials for concrete production. Copper slag, a by-product of the copper smelting industry, possesses physical and mechanical properties comparable to natural sand and presents a viable replacement option. Simultaneously, steel fiber reinforcement has gained prominence for improving the tensile strength, ductility, and crack resistance of concrete. This study investigates the combined influence of copper slag as a partial replacement of fine aggregate and hooked-end steel fibers on the mechanical performance of concrete. Experimental investigations were conducted on M30 and M40 grade concretes with copper slag replacement levels ranging from 0% to 60%, both in conventional concrete and steel fiber reinforced concrete (SFRC) containing 1% steel fibers by volume. Fresh properties, compressive strength, split tensile strength, and flexural strength were evaluated at different curing ages. The results indicate that copper slag significantly enhances strength properties up to an optimum replacement level of 50% for M30 concrete and 40% for M40 concrete. The incorporation of steel fibers further improved post-cracking behavior, tensile resistance, and flexural performance. Beyond the optimum replacement levels, a reduction in strength was observed due to increased free water content and void formation. The study concludes that the combined use of copper slag and steel fibers offers an environmentally sustainable and structurally efficient solution for high-performance concrete applications.
Metakaolin –A Partial Replacement of Cement in Concrete as Admixture
The use of supplementary cementitious materials has become an important approach for improving the performance and sustainability of concrete. Among these materials, metakaolin—produced by controlled calcination of kaolin clay—offers high pozzolanic reactivity and the potential to enhance strength and durability. This study evaluates the influence of metakaolin as a partial replacement for ordinary Portland cement in M25 concrete. Replacement levels of 5%, 10%, 15%, and 20% were tested to determine their effects on workability and mechanical properties. Concrete specimens were prepared and subjected to slump and compaction factor tests in the fresh state, followed by compressive, split tensile, and flexural strength tests at 3, 7, and 28 days. Workability was found to decrease slightly with increasing metakaolin content, though all mixes remained workable. The mechanical test results showed a clear improvement in strength for mixes containing metakaolin, with the 10% replacement level providing the highest enhancement across all strength parameters. This improvement is attributed to the pozzolanic reaction, which reduces calcium hydroxide and forms additional C–S–H gel, resulting in a denser and stronger microstructure. Based on the findings, metakaolin can be considered an effective mineral admixture for producing stronger and more durable concrete. The study identifies 10% replacement as the optimum level for achieving maximum performance benefits while maintaining acceptable workability.
Assessment of Load Response in Interlocking Concrete Block Pavements By ANSYS
Concrete Block Pavement (CBP) consists of discrete masonry units arranged in engineered patterns, unlike monolithic concrete or asphalt surfaces. Because load paths are transferred through interlocking blocks, bedding, and joints, overall structural stiffness depends on block shape, thickness, and the laying pattern, making a single elastic modulus difficult to define. This study develops a practical deflection‑prediction model for CBP using dynamic plate load tests combined with Finite Element Method (FEM) analysis. We propose a procedure to estimate the Equivalent Elastic Modulus (EEM) of an Equivalent Block Layer (EBL)—a composite representation of blocks and bedding sand—calibrated to measured surface deflections. Results indicate that properly compacted jointing sand can reduce peak pavement deflection by as much as 30%. Additional reductions are achieved by increasing block thickness, using larger loading plate diameters, and providing thicker, well‑graded sub‑bases that limit shear deformation. Among common laying patterns, the herringbone bond offers the most efficient load dispersion due to multidirectional interlock; basket‑weave performs moderately; stretcher bond shows the least effectiveness under dynamic loading. FEM simulations in Ansys reproduced the measured deflections with good fidelity, matching manual calculations of approximately 8 mm and thereby validating the modelling approach. The framework offers practitioners a rational way to select block geometry, pattern, and layer thicknesses to meet serviceability targets while optimizing material use. The methodology can support maintenance planning and lifecycle cost evaluation.
Surface Water Quality Appraisal and Suitability Assessment for Designated Uses along the Brahmani River Basin, Odisha, India
The Brahmani River in Odisha supports regional agriculture, industry and domestic supply but human activities are mounting pressures on the water quality of the river. The paper measures the spatio-seasonal variability in the surface-water quality between 2017 and 2024 by using the samples obtained in 12 monitoring stations during pre- and post-monsoon periods. The main parameters that were examined to describe hydrochemistry and their suitability for use were core physico-chemical parameters (pH, dissolved oxygen, biochemical and chemical oxygen demand, total dissolved solids, major cations, and anions). The overall status was calculated in terms of the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), and agricultural usability was assessed using common irrigation indices, which are sodium adsorption ratio (SAR), soluble sodium percentage (SSP), permeability index (PI), Kelly ratio (KR), magnesium adsorption ratio (MAR) and residual sodium carbonate (RSC). It was observed that some quality deterioration occurred locally and was in line with the discharge of urban and industrial wastewater into the basin. The type was predominant and the hydrochemical facies was characterized by carbonate weathering with minor evaporite effects.
Energy and Momentum Correction Coefficients in Meandering Channels
Understanding flow behaviour in meandering channels is essential due to their complex velocity distribution influenced by centrifugal forces. Unlike straight channels, meandering flows exhibit lateral variations in velocity and associated energy losses, necessitating correction factors such as the kinetic energy correction coefficient (α) and the momentum correction coefficient (β). This study investigates these coefficients in a highly sinuous meandering channel (sinuosity 4.1) under varying discharge conditions and surface roughness (smooth and rough). Velocity measurements were obtained using Pitot tubes across multiple cross-sections along the meander path. The results reveal significant lateral variation in depth-averaged velocities and the correction coefficients, highlighting the influence of channel roughness and discharge on the distribution of α and β. Comparative analysis between smooth and rough channels shows higher energy and momentum correction coefficients in smooth conditions, attributed to reduced turbulence and frictional effects. These findings contribute to a better understanding of flow mechanics in natural and engineered meandering systems, aiding in more accurate hydraulic modelling and design.
Spatial Zonation of Flood Hazards Using GIS Coupled with AHP
Floods are among the most frequent and devastating natural hazards, occurring globally except in polar regions. They pose significant threats to human life, infrastructure, and ecosystems. The increasing frequency and intensity of floods are largely attributed to climate change and anthropogenic land-use changes, which alter river morphology and exacerbate channel stress. Effective flood risk management and policy interventions are essential to minimize these impacts. This study focuses on assessing flood hazards and mapping inundation-prone areas in the Balasore district and adjacent coastal regions of Odisha, which are highly susceptible to flooding. Key flood-contributing factors considered in the analysis include slope, elevation, drainage density, annual precipitation, Normalised Difference Vegetation Index (NDVI), land use/land cover (LULC), and the Topographic Wetness Index (TWI). These parameters were integrated using a Geographic Information System (ArcGIS) and analyzed through the Analytical Hierarchy Process (AHP) to generate a comprehensive flood hazard zonation map. The results indicate that approximately 36.50 ha, 5,073.23 ha, 27,362.54 ha, 301,622.30 ha, and 32,937.77 ha fall under very low, low, moderate, high, and very high flood hazard zones, respectively. The AHP-based GIS model demonstrated high reliability and can be replicated in other flood-prone regions for effective planning and disaster risk reduction. This approach supports proactive flood prediction, early warning systems, and sustainable flood management practices that local and national authorities can institutionalize.
Lift Irrigation Planning for Drought-Prone Koraput, India
The Koraput district, prone to frequent droughts, requires sustainable irrigation strategies to support agriculture, particularly millet cultivation, which is both climate-resilient and water-efficient. This study focuses on the planning and design of a lift irrigation system suited to the region’s agro-ecological conditions. The system aims to optimize the use of local water resources, ensuring reliable irrigation and improving crop productivity. Key elements of the project include the identification of suitable sites for lift stations, the design of pump systems capable of transporting water to higher elevations, and the construction of storage tanks to manage seasonal water shortages. A 10 HP pump has been proposed, supported by renewable energy to ensure energy efficiency and reduce operational costs. Smart irrigation techniques are integrated to minimize water wastage and enhance system performance. Technical assessments using CROPWAT show that maize requires 62.1 mm (net) and 43.5 mm (gross) irrigation, while millet needs 106.0 mm (net) and 74.2 mm (gross). The crop water demand was estimated at 451.6 mm/dec for maize and 375.5 mm/dec for millet. Structural design for the underground tank specifies a 400 mm wall thickness, 2296.8 mm² minimum steel reinforcement, and 1.5 N/mm² tensile stress. A triangular channel, 4.42 m deep and 11.05 m wide with a 1:1 side slope, was designed for water conveyance.
Prediction of Runoff Using Hybrid SVM-SSA in Baitarani River Basin, Odisha, India
Accurate prediction of surface runoff is essential for effective water resource management, particularly in regions susceptible to hydrological variability such as the Baitarani River Basin in Odisha, India. Traditional hydrological models often fail to capture the complex, nonlinear, and non-stationary behavior of rainfall-runoff processes, especially under the influence of climatic and anthropogenic changes. In this study, a hybrid model integrating Support Vector Machine (SVM) with Salp Swarm Algorithm (SSA) is proposed to enhance the accuracy of monthly runoff prediction. The performance of the SVM-SSA model is compared against conventional Artificial Neural Network (ANN) and standalone SVM models using datasets from Anandpur and Champua gauging stations. Inputs include rainfall, temperature, specific humidity, and relative humidity, offering a comprehensive representation of climatic influences. Model performance is evaluated using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Willmott’s Index (WI). The results demonstrate that the hybrid SVM-SSA model significantly outperforms the conventional models, achieving R² values of 0.9847 and 0.9771 at Anandpur and 0.9844 and 0.9756 at Champua for training and testing phases, respectively. These findings suggest that coupling machine learning with metaheuristic optimization provides a promising approach for runoff prediction in complex river basins.
Machine Learning Approaches for Estimating Roughness Coefficients in Two-Stage Sinuous Channels
Estimating the flow rate of streams during floods is rarely successful due to a lack of dependable data from the field in natural areas. Predicting discharge depends greatly on the value of Manning’s roughness coefficient. The study uses Artificial Neural Networks (ANN), Gene Expression Programming (GEP), Extreme Learning Machines (ELM) and Gaussian Process Regression (GPR) as AI techniques to determine Manning’s roughness coefficient in two-stage sinuous channels. Relative width, depth ratio, sinuosity ratio, channel bed slope and sinuous belt width ratio are the input variables used for the model. They were taken into account to determine the roughness adjustment factor. Several experiments were run to test how different parameters affect roughness prediction. Results of the developed AI models were compared to those of established analytical methods using various statistical tricks. During the testing phase, the highest accuracy was achieved by GPR and ANN (R² = 0.82). After that, GEP reached R² = 0.79 and ELM R² = 0.65. Predictions of Manning's n using the GPR method are quite accurate. However, the GEP model's ability to generate a generalized model equation for Manning's n makes it particularly valuable. Based on the above, the GEP model’s expression was applied to estimate Manning’s n for measurements of a flood from the Baitarani River in India. It was confirmed that the proposed models can work in experimentally small-scale environments and real rivers.
Reinforcing Soil Stability Through Human Hair Application
Soil stabilization is an extremely important process in civil engineering that helps improve the properties of soil so that it can be used in Construction or other works. The application of human hair fibers (HHF) as a soil stabilization reinforcement material offers a novel and environmental-friendly method. Not only this method enhances the engineering characteristics of soil like shear strength and bearing capacity but also offers a permanent solution for the dumping of non-biodegradable human hair waste. Some studies conducted recently have revealed that the soil’s load-bearing capacities can be enhanced considerably by integrating HHF into the samples of the soil. The process entails mixing human hair fibres in the soil of differing percentages and conducting the standard laboratory tests to obtain the best mix ratio that produces the best mechanical properties. The findings establish that HHF could be an alternative for commercial fiber materials and this is a cost-effective approach and environmentally important for stabilizing the soil.
Assessment of Soil Moisture Variability for Urban Flood Risk Mitigation
Urban flooding has become a recurring problem in rapidly developing cities like Patna, primarily due to poor drainage, increased impervious surfaces, and a lack of integrated soil-based flood risk assessment. This study investigates the variability in soil moisture and its influence on urban flood susceptibility across selected locations in Patna. Soil samples were collected from ten urban sites and analyzed in the laboratory for texture (sand, silt, clay), water content, specific gravity, and field capacity. The results indicate a predominance of silt and clay-rich soils in many areas, particularly in Gulzarbagh, Gaighat, BIT, and Patrakar Nagar, contributing to low infiltration rates and increased surface runoff. Field capacity ranged from 9% to 21.41%, suggesting limited water retention capacity in several locations. The Soil Water Retention Curve (SWRC) was plotted using Brooks & Corey (1964), Campbell (1974), and Van Genuchten (1980) models with the help of SWRC v3.0 software to derive unsaturated hydraulic properties. Findings highlight that areas with finer-textured soils exhibit higher flood vulnerability due to reduced permeability and delayed infiltration. This soil-based assessment provides a scientific basis for integrating subsurface hydrological characteristics into urban flood mitigation and planning strategies for Patna and similar flood-prone urban centers.
A Study on the Strength and Consolidation Characteristics of High-Concentrated Ash Slurry
High Concentration Slurry Disposal (HCSD) is a modern technique for disposing of fly ash generated in thermal power plants. This study investigates the geotechnical behavior of HCSD pond ash, focusing on its density, permeability, and compaction characteristics. Laboratory tests on samples collected from an ash pond at Vedanta Aluminium Limited, Jharsuguda, revealed that the dry density of pond ash remains stable at approximately 1.4 g/cc over time. The permeability of settled HCSD ash was found to be 1.3 × 10⁻⁴ cm/sec, which is comparable to that of conventional Lean Concentration Slurry Disposal (LCSD) systems, indicating no significant improvement in impermeability. Proctor compaction tests demonstrated that increased compaction effort results in higher maximum dry density (MDD) and lower optimum moisture content (OMC), making HCSD pond ash a suitable material for embankment construction. These findings highlight the potential for sustainable utilization of HCSD ash in infrastructure projects, offering an environmentally viable alternative for ash management.
Assessment of Physico-Chemical Water Quality Parameters of the Brahmani River
This research analyzes the Brahmani River water quality in terms of the important physicochemical parameters. The river is exposed to huge industrial effluents and impacted by extensive agricultural and human practices. To assess the effect of these activities, water samples were gathered from nine points over the river basin between August 2014 and September 2015, with sampling done on the first working day of every month. Parameters like pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and Conductivity were examined. It is found from the results that the water quality of the river remains within acceptable ranges. The physicochemical parameters play a significant role in evaluating the overall water quality of the river. The research highlights the need for constant monitoring to make the river a sustainable resource for both ecological and human purposes.
Assessing the Reliability of Monthly Inflow Predictions with Monte Carlo Simulation
Accurate inflow prediction is essential for effective reservoir planning and management. This study applies the autoregressive integrated moving average (ARIMA) model to forecast inflow into India's Hirakud Reservoir using a 50-year time series of average monthly inflow data. The model development process includes analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF), followed by an augmented Dickey-Fuller (ADF) test to assess stationarity. Forecasts are generated for four lead times: 6, 12, 24, and 36 months. Monte Carlo Simulation (MCS) is conducted for each period to quantify prediction uncertainty. The results show that the 6-month forecast performs best in terms of uncertainty analysis. The 50-year dataset provides a comprehensive understanding of inflow patterns, capturing both short-term fluctuations and long-term trends. ACF and PACF analyses guide the selection of ARIMA model parameters, while the ADF test ensures stationarity. MCS adds robustness to the forecasting process by accounting for uncertainties inherent in hydrological predictions. By simulating multiple scenarios, MCS helps assess forecast reliability and provides a range of possible outcomes. Overall, the study demonstrates the effectiveness of combining the ARIMA model with MCS for inflow forecasting. Shorter lead times, such as the 6-month forecast, offer more precise predictions with lower uncertainty. This information is crucial for optimizing reservoir operations and ensuring sustainable water resource management. Future research could explore integrating additional variables, such as climate indices, to further enhance predictive accuracy.
Enhancing Soil Properties through Ground Granulated Blast Furnace Slag: An Experimental Study
This research examines the influence of Ground Granulated Blast Furnace Slag (GGBS) on soil properties based on a comprehensive experimental investigation. Soil specimens containing different percentages of GGBS (0%, 5%, 10%, 15%, 20%, and 25%) were evaluated for compaction, Atterberg limits, specific gravity, unconfined compressive strength, and California Bearing Ratio (CBR). Results show that GGBS can significantly influence soil properties. Specific gravity first increases and then becomes constant with increasing GGBS. Liquid limit and plasticity index reduce regularly, enhancing soil grading and minimizing plastic behavior. Optimum moisture content reduces first and varies, while the maximum dry density rises first and experiences regular change. Unconfined compressive strength reduces first but enhances with greater GGBS content. CBR of soaked and unsoaked samples experiences intricate regular change. Sieve analysis experiences enhanced soil grading with increased GGBS. The research concludes that GGBS is a good soil stabilizer but its performance depends on the percentage added and exact requirements of the application.