NG Civil Engineering
type: channel
description: 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
publisher: Next Gen Academic Services
language: en
publication name: NG Civil Engineering
issn: 3107-569X
items: Predicting Concrete Compressive Strength Using an Ensemble Machine Learning Model
items: Modelling Floodplain Boundary Shear Distribution in Two-Stage Meandering Channels Using Machine Learning Techniques
items: Daily River Flow Forecasting in the Baitarani River, Odisha
items: Water Quality Assessment of Angul District, Odisha: A Comprehensive Study of Surface and Ground Water Contamination
items: Hydrologic Modelling of Mahanadi River Using Rainfall-Runoff Method
items: Experimental Investigation of Reinforced Concrete Incorporating Copper Slag and Steel Fibers
items: Metakaolin –A Partial Replacement of Cement in Concrete as Admixture
items: Assessment of Load Response in Interlocking Concrete Block Pavements By ANSYS
items: Surface Water Quality Appraisal and Suitability Assessment for Designated Uses along the Brahmani River Basin, Odisha, India
items: Energy and Momentum Correction Coefficients in Meandering Channels
items: Spatial Zonation of Flood Hazards Using GIS Coupled with AHP
items: Lift Irrigation Planning for Drought-Prone Koraput, India
items: Prediction of Runoff Using Hybrid SVM-SSA in Baitarani River Basin, Odisha, India
items: Machine Learning Approaches for Estimating Roughness Coefficients in Two-Stage Sinuous Channels
items: Reinforcing Soil Stability Through Human Hair Application
items: Assessment of Soil Moisture Variability for Urban Flood Risk Mitigation
items: A Study on the Strength and Consolidation Characteristics of High-Concentrated Ash Slurry
items: Assessment of Physico-Chemical Water Quality Parameters of the Brahmani River
items: Assessing the Reliability of Monthly Inflow Predictions with Monte Carlo Simulation
items: Enhancing Soil Properties through Ground Granulated Blast Furnace Slag: An Experimental Study

Predicting Concrete Compressive Strength Using an Ensemble Machine Learning Model
type: item
description: 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.
subject: Research Article
  topic: section
subject: Concrete compressive strength
  topic: keywords
subject: Ensemble learning
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Prediction modeling
  topic: keywords
subject: Stacking model
  topic: keywords
subject: Sustainable construction
  topic: keywords
creator: Sovana Acharya
creator: Aparupa Pani
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-12-30
publication date: 2025-12-30
starting page: 31
ending page: 36
doi: 10.5281/zenodo.18110904

Modelling Floodplain Boundary Shear Distribution in Two-Stage Meandering Channels Using Machine Learning Techniques
type: item
description: 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.
subject: Research Article
  topic: section
subject: Boundary shear distribution
  topic: keywords
subject: Two-stage meandering channel
  topic: keywords
subject: Gaussian Process Regression
  topic: keywords
subject: Extreme Learning Machine
  topic: keywords
subject: Relevance Vector Machine
  topic: keywords
creator: Abinash Mohanta
creator: Chitaranjan Dalai
creator: Monalisa Mallick
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-12-30
publication date: 2025-12-30
starting page: 22
ending page: 30
doi: 10.5281/zenodo.18109570

Daily River Flow Forecasting in the Baitarani River, Odisha
type: item
description: 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.
subject: Research Article
  topic: section
subject: ARIMA
  topic: keywords
subject: Baitarani River
  topic: keywords
subject: River flow
  topic: keywords
subject: Hydrology
  topic: keywords
creator: Arpan Pradhan
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-12-30
publication date: 2025-12-30
starting page: 15
ending page: 21
doi: 10.5281/zenodo.18106743

Water Quality Assessment of Angul District, Odisha: A Comprehensive Study of Surface and Ground Water Contamination
type: item
description: 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.
subject: Research Article
  topic: section
subject: BOD
  topic: keywords
subject: COD
  topic: keywords
subject: TDS
  topic: keywords
subject: Water Quality Assessment
  topic: keywords
creator: Monalisa Mallick
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-12-30
publication date: 2025-12-30
starting page: 8
ending page: 14
doi: 10.5281/zenodo.18106635

Hydrologic Modelling of Mahanadi River Using Rainfall-Runoff Method
type: item
description: 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.
subject: Research Article
  topic: section
subject: Rainfall runoff
  topic: keywords
subject: Hydrological modeling
  topic: keywords
subject: SCS–Mockus
  topic: keywords
subject: Rainfall trends
  topic: keywords
creator: Monalisa Mallick
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-12-30
publication date: 2025-12-30
starting page: 1
ending page: 7
doi: 10.5281/zenodo.18106555

Experimental Investigation of Reinforced Concrete Incorporating Copper Slag and Steel Fibers
type: item
description: 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.
subject: Research Article
  topic: section
subject: Concrete
  topic: keywords
subject: Flexural Strength
  topic: keywords
subject: Copper Slag
  topic: keywords
subject: Steel fiber strengthened concrete
  topic: keywords
subject: M30
  topic: keywords
creator: J. Arun Kumar
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-09-27
publication date: 2025-09-27
starting page: 28
ending page: 33
doi: 10.5281/zenodo.17965653

Metakaolin –A Partial Replacement of Cement in Concrete as Admixture
type: item
description: 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.
subject: Research Article
  topic: section
subject: Concrete
  topic: keywords
subject: Flexural Strength
  topic: keywords
subject: Metakaolin
  topic: keywords
subject: Pozzolanic Reaction
  topic: keywords
subject: Workability
  topic: keywords
creator: G Ganesh
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-09-27
publication date: 2025-09-27
starting page: 20
ending page: 27
doi: 10.5281/zenodo.17848050

Assessment of Load Response in Interlocking Concrete Block Pavements By ANSYS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Interlocking Concrete Block Paving
  topic: keywords
subject: Equivalent Elastic Modulus
  topic: keywords
subject: Finite Element Method
  topic: keywords
subject: Herringbone pattern
  topic: keywords
creator: Rajesh Kumar Mantry
creator: Santosh Kumar Jena
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-09-15
publication date: 2025-09-15
starting page: 15
ending page: 19
doi: 10.5281/zenodo.17213732

Surface Water Quality Appraisal and Suitability Assessment for Designated Uses along the Brahmani River Basin, Odisha, India
type: item
description: 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.
subject: Research Article
  topic: section
subject: Brahmani River
  topic: keywords
subject: Human health risk
  topic: keywords
subject: Water quality
  topic: keywords
subject: Water Resources Management
  topic: keywords
subject: Open-channel flow
  topic: keywords
creator: Chitaranjan Dalai
creator: Prativa Priyadarsini Bishoi
creator: Deba Prakash Satapathy
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-09-15
publication date: 2025-09-15
starting page: 9
ending page: 14
doi: 10.5281/zenodo.17190576

Energy and Momentum Correction Coefficients in Meandering Channels
type: item
description: 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.
subject: Research Article
  topic: section
subject: Hydraulic modelling
  topic: keywords
subject: Mean velocity
  topic: keywords
subject: Meandering flows
  topic: keywords
subject: Open-channel flow
  topic: keywords
creator: Arpan Pradhan
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-09-22
publication date: 2025-09-22
starting page: 1
ending page: 8
doi: 10.5281/zenodo.17178364

Spatial Zonation of Flood Hazards Using GIS Coupled with AHP
type: item
description: 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.
subject: Research Article
  topic: section
subject: Lift Irrigation System
  topic: keywords
subject: Millet Cultivation
  topic: keywords
subject: Climate-Resilient Agriculture
  topic: keywords
subject: Water Resource Management
  topic: keywords
subject: Drought-Prone Areas
  topic: keywords
creator: Mani Bhushan
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-06-11
publication date: 2025-06-11
starting page: 34
ending page: 40
doi: 10.5281/zenodo.15645595

Lift Irrigation Planning for Drought-Prone Koraput, India
type: item
description: 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.
subject: Research Article
  topic: section
subject: Lift Irrigation System
  topic: keywords
subject: Millet Cultivation
  topic: keywords
subject: Climate-Resilient Agriculture
  topic: keywords
subject: Water Resource Management
  topic: keywords
subject: Drought-Prone Areas
  topic: keywords
creator: Kirtisuta Bhoi
creator: Kishore Kumar Singh
creator: Sambit Kumar Majhi
creator: Gloria Pitkamal Mohanta
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-05-20
publication date: 2025-05-20
starting page: 27
ending page: 33
doi: 10.5281/zenodo.15607923

Prediction of Runoff Using Hybrid SVM-SSA in Baitarani River Basin, Odisha, India
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Neural Network
  topic: keywords
subject: Salp Swarm Algorithm
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Willmott’s Index
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Rainfall and Runoff modelling
  topic: keywords
creator: Chitaranjan Dalai
creator: Debiprasad Panda
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-05-20
publication date: 2025-05-20
starting page: 19
ending page: 26
doi: 10.5281/zenodo.15487798

Machine Learning Approaches for Estimating Roughness Coefficients in Two-Stage Sinuous Channels
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Neural Network
  topic: keywords
subject: Extreme Learning Machines
  topic: keywords
subject: Gene Expression Programming
  topic: keywords
subject: Manning’s roughness coefficient
  topic: keywords
subject: Sinuous Channels
  topic: keywords
creator: Abinash Mohanta
creator: Jahnvi Choudhary
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-05-20
publication date: 2025-05-20
starting page: 7
ending page: 18
doi: 10.5281/zenodo.15479938

Reinforcing Soil Stability Through Human Hair Application
type: item
description: 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.
subject: Research Article
  topic: section
subject: CBR
  topic: keywords
subject: HHF
  topic: keywords
subject: Liquid limit test
  topic: keywords
subject: Soil stabilization
  topic: keywords
subject: Rainfall and Runoff modeling
  topic: keywords
creator: Chinmaya Chidananda Sahu
creator: Shradhanjalee Pradhan
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-05-20
publication date: 2025-05-20
starting page: 1
ending page: 6
doi: 10.5281/zenodo.15449296

Assessment of Soil Moisture Variability for Urban Flood Risk Mitigation
type: item
description: 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.
subject: Research Article
  topic: section
subject: Field Capacity
  topic: keywords
subject: Infiltration
  topic: keywords
subject: Soil Moisture Content
  topic: keywords
subject: Specific Gravity
  topic: keywords
creator: Kumar Ayush Abhishek
creator: Shashi Shankar Ojha
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-03-31
publication date: 2025-03-31
starting page: 32
ending page: 42
doi: 10.5281/zenodo.15390324

A Study on the Strength and Consolidation Characteristics of High-Concentrated Ash Slurry
type: item
description: 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.
subject: Research Article
  topic: section
subject: Ash Slurry
  topic: keywords
subject: Geotechnical Properties
  topic: keywords
subject: Strength
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Abinash Mohanta
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-03-31
publication date: 2025-03-31
starting page: 24
ending page: 31
doi: 10.5281/zenodo.15115821

Assessment of Physico-Chemical Water Quality Parameters of the Brahmani River
type: item
description: 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.
subject: Research Article
  topic: section
subject: Brahmani River
  topic: keywords
subject: Physico-Chemical Parameters
  topic: keywords
subject: pH
  topic: keywords
subject: Dissolved Oxygen (DO)
  topic: keywords
subject: Biochemical Oxygen Demand (BOD)
  topic: keywords
creator: Monalisa Mallick
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-03-31
publication date: 2025-03-31
starting page: 15
ending page: 23
doi: 10.5281/zenodo.15115537

Assessing the Reliability of Monthly Inflow Predictions with Monte Carlo Simulation
type: item
description: 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.
subject: Research Article
  topic: section
subject: ARIMA
  topic: keywords
subject: Flood forecasting
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Rainfall and Runoff modeling
  topic: keywords
creator: Shradhanjalee Pradhan
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-03-31
publication date: 2025-03-31
starting page: 8
ending page: 14
doi: 10.5281/zenodo.15113444

Enhancing Soil Properties through Ground Granulated Blast Furnace Slag: An Experimental Study
type: item
description: 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.
subject: Research Article
  topic: section
subject: California Bearing Ratio (CBR)
  topic: keywords
subject: Soil Stabilization
  topic: keywords
subject: Unconfined Compressive Strength
  topic: keywords
subject: Specific Gravity
  topic: keywords
creator: Akshay Kumar
creator: Shashi Shankar Ojha
rights: Copyright (c) 2025 NG Civil Engineering
date: 2025-03-31
publication date: 2025-03-31
starting page: 1
ending page: 7
doi: 10.5281/zenodo.15113284