Fusion Journal of Engineering and Sciences
At Fusion Journal of Engineering and Sciences, our Editorial Board Members (EBMs), Associate Editors (AEs), and external peer reviewers play a pivotal role in maintaining the highest standards of academic excellence. Their expertise and dedication ensure a rigorous and fair peer-review process, helping us deliver impactful research to the global scientific community. Online ISSN : 3105-2193 Print ISSN : 3105-2185
Optimization of Vegetated Open Channel for Enhancing Flood Resilience: Numerical Analysis
Vegetated open channel significantly altered the dynamics of floodwater. For this purpose, the current research aims to optimize a vegetated open channel by altering bed roughness for enhancing flood resilience. This paper utilized an ANSYS-Fluent-based computational fluid dynamics (CFD) under diverse flow conditions. The roughness conditions of the channel bed were altered from 0 to 14.32mm under different flow conditions. The result of the numerical model was validated against an experimental study conducted in a controlled laboratory setting. The result demonstrated a close alignment between experimental and numerical models with coefficient of determination (R2) and root mean square error (RMSE) values of 0.96 and 0.0176, respectively. After validating the result of numerical models, further cases were simulated in ANSYS Fluent to observe flow dynamics under diverse flow conditions. Parameters like velocity distribution and turbulent kinetic energy (TKE) were determined. The results indicate that stream-wise depth average velocity and TKE increased 47.3% and 66.4% by varying Froude number (Fr) from 0.40 to 0.65. However, upon introducing bed roughness in a vegetated open channel, it was observed that the stream-wise depth average velocity and TKE reduced by 3.4% and 4.17% under roughness conditions of 7.22mm. By increasing the roughness conditions of the channel bed, the velocity distribution and TKE were further reduced. Therefore, the result of this study concluded that increasing vegetated open channel bed roughness enhanced flow energy dissipation, reduced erosive forces, stabilized channels, and provided an effective nature-based framework for enhancing flood resilience.
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Mapping and Assessing Glacial Lakes in Pakistan: Risk Insights from NDWI
Water is essential for life, but it also brings serious risks, especially through water-related disasters. Over the past two decades, about 74% of natural disasters have been water-related, affecting millions worldwide. Glaciers are vital sources of freshwater for drinking, farming, industry, and hydropower. However, melting glaciers accelerated by climate change are forming unstable glacial lakes that can burst and cause Glacier Lake Outburst Floods (GLOFs), posing serious threats to downstream communities. This study focuses on five major river catchments in northern Pakistan: Hunza, Gilgit, Shyok, Astore, and Chitral. These areas have extensive glaciation and important hydropower infrastructure, making them especially vulnerable to GLOFs. Using satellite images from 2015 to 2020, we applied the Normalized Difference Water Index (NDWI) a technique that highlights water bodies using satellite data to map and monitor glacial lakes. An automated method helped identify around 1,500 glacial lakes, covering a total area of 62.25 km². Out of these, 36 lakes were classified as potentially hazardous. Most lakes (82%) are small, under 0.05 km², and lie at elevations between 3,400 and 5,100 meters, with the highest concentration (43%) between 4,000 and 4,500 meters. The study did not include en-glacial or sub-glacial lakes, as they require ground-based tools/equipment to detect. This research offers valuable baseline data for understanding glacial lake hazards in Pakistan. It emphasizes the need to include GLOF risk in national and regional planning and supports future efforts in hazard modeling and disaster risk reduction strategies.
Integrating Artificial Neural Networks for Enhanced Decision Making in Water Management
The structures, such as sluice gates, play a critical role in hydraulic structures and determine the flows internal and external management in them. This research focuses on the analysis of the behavior of the sluice gate in terms of the gate opening, the flow rate, and the coefficient of discharge (Cd). The experimental data show that the highest values of Cd are achieved at the least gate opening and the maximum value of Cd is 0.765 at the gate opening of 0.03 m. With the view of improving decision making capacity, the study uses Artificial Neural Networks (ANN) to forecast Cd using experimental data. In addition, a prediction accuracy of 0.999 is established by the ANN model, as shown by the R² value. This high level of precision underscores the capability of ANN in modelling of hydraulic behaviors and justifies its use in the improvement of sluice gate operations. With the incorporation of ANN based predictions, management of water resources may be enhanced and hence improve the designing, operation and decision-making processes in hydraulic infrastructural projects especially in cases of dynamic flow.
Harnessing AI for Hydraulic Efficiency: A Comparative Study of Numerical Modeling and Machine Learning in Energy Dissipation Over a Vertical Drop Structure
In hydraulic engineering, accurately predicting energy dissipation at vertical drop structures is crucial for ensuring structural integrity and optimizing design efficiency. This study presents a comparative analysis of energy dissipation calculations using two methodologies: numerical modeling through Computational Fluid Dynamics (CFD) and advanced Machine Learning (ML) techniques. The study is performed at vertical drop structure at Lower Gogera Branch Canal (LGBC) in Pakistan, experimental data has been utilized alongside CFD simulations to establish a reliable benchmark for energy dissipation at a vertical drop of 3.69 meters, with a discharge rate of 63.73 m³/s. The CFD method employs the Volume of Fluid (VOF) technique to simulate flow dynamics, providing precise insights into energy dissipation. In parallel, we implement various ML algorithms, including decision trees, random forests, and neural networks, to model energy dissipation based on key input parameters such as flow rate, height of fall, and downstream conditions. The performance of the ML models is evaluated against the numerical results, allowing for a comprehensive comparison of their predictive capabilities. Initial findings indicate that while the CFD model shows a high level of accuracy with discrepancies within 5% of empirical measurements, the ML techniques also demonstrate significant potential in capturing complex, nonlinear relationships inherent in energy dissipation phenomena. The use of ML not only provides a faster alternative for predictions but also offers insights into optimizing design parameters in hydraulic structures. This research highlights the transformative impact of AI methodologies on hydraulic engineering, illustrating how integrating ML can enhance traditional modeling techniques. The results contribute to the ongoing discourse on the application of artificial intelligence in water resources management, paving the way for more efficient and sustainable engineering practices.
Artificial Intelligence and Machine Learning for Prediction of Extreme Floods in Pakistan
This paper discusses the application of artificial intelligence (AI) and machine learning (ML) techniques in predicting extreme floods in Pakistan, a country frequently affected by monsoon-related flooding. The study aims to develop predictive models that utilize historical hydrological and meteorological data to improve flood forecasting and mitigation. By integrating AI/ML models, such as regression, classification, and anomaly detection, with datasets on rainfall, river inflows, topography, and climate factors, this research seeks to enhance early warning systems and flood management practices. Various algorithms, including Random Forest, Gradient Boosting, and Neural Networks, are evaluated based on prediction accuracy and the models’ ability to detect anomalies that precede extreme flood events. These methods, coupled with real-time data from weather stations and satellite imagery, offer a robust approach to forecasting and mitigating the impact of floods in Pakistan. The paper also highlights the practical implications of these AI/ML techniques for disaster management and water resource planning, with the aim of reducing the socioeconomic impact of floods.
Efficient Water Resource Management in Dam Basins using Machine Learning
The purpose of this paper is to highlight the need for proper water management in dam basins in order to enhance effective water resources management and flood control in a changing climatic situation. This study uniquely combines meteorological data (temperature, precipitation) with hydrological indicators (reservoir levels, inflow, outflow) using a multi-model machine learning framework to improve integrated water resource management. Inputs such as meteorological data, including temperature and precipitation, combined with dam hydrology data like dam levels, inflows, and outflows, can assist in forecasting models. With machine learning, it is possible to detect trends in rainfall and inflows inflating water availability and adjust dam management in real-time, where necessary. This will improve flood management by providing a predictive tool for water-raising during rainfall, storage, and release in time to avert any spilling over. Also, hydropower and agricultural irrigation demand a systemic cross-platform approach to manage water resources. Anomaly detection models can inform operators of abnormal inflow trends and assist with quick water flow modulation. The proposed models demonstrated strong performance, with the KNN regressor achieving an R² score of 0.9443 and classification models like Logistic Regression attaining 99.5% accuracy. This study will help in enhancing decision-making in dam basin management by adding various datasets of water sources in the AI models to provide more acceptable ways of using water while minimizing risks associated with extreme weather and water production instability. The efficiency of the machine learning-based model has been measured in terms of R2 score, root means squared error (RMSE).
A Deep Learning Ensemble Model for Flood Image Classification
Flood is a type of natural disaster that leads to a widespread devastation. The increasing amount of rain specifically in the Urban regions of Sindh province causes several issues, whereas the drainage system is not very efficient to handle the large amount of water in a short period of time. Identification of floods is essential for disaster response, as it helps locate areas which need immediate help. Recently, the deep learning-based models have shown the best performance for image classification tasks. In this paper, a deep learning-based ensemble model has been developed where four state-of-the-art deep learning models are combined to classify flood from the images either captured with the mobile camera or other image capturing devices. The deep learning ensemble model has been trained and tested on the two publicly available datasets labelled with flood and non-flood images. To enhance the efficacy of the deep learning-based ensemble model, the hyper-parameters of the four models are fine-tuned. The results obtained show that the deep learning-based ensemble model outperforms than the individual models.
Harnessing AI for Water Resource Management: Innovations, Applications, and Challenges
Water resource management in Pakistan faces growing pressure due to increasing water scarcity, pollution, and inefficient distribution challenges intensified by climate change. This review investigates how Artificial Intelligence (AI) can contribute to more effective and sustainable water management in this context. It examines the application of AI techniques such as machine learning, deep learning, and predictive analytics for monitoring water quality, optimizing reservoir operations, forecasting flood risks, and improving agricultural water use. (Ahmad et al., 2025)). Assessing water demand and supply in the Upper Indus Basin using integrated hydrological modeling under varied socioeconomic scenarios. Applied Water Science. The paper presents both global and national case studies that demonstrate successful implementation of AI tools in water-related sectors. It also explores existing barriers, including limited data access, technological constraints, and inadequate policy support. Ultimately, the study underscores the transformative potential of AI in addressing Pakistan’s water challenges and enhancing the resilience of its water systems.
Recent Patterns and Trends in Extreme River Flow Event in the Upper Indus Basin, Pakistan
The Upper Indus Basin (UIB), covering the Hindukush, Karakoram, and Himalayan (HKH) ranges, is a key water source for ten major rivers, including Jhelum, Kabul, Hunza, Shyok, and Shigar. This study analyzes trends in high and low streamflows from 1981 to 2016 using the Mann-Kendall test and Sen’s slope estimator to assess their magnitude and timing. The results reveal a decline in high flows across key sub-basins like Jhelum, Indus, and Kabul, while glacier-fed basins such as Shigar and Shyok showed increasing trends. In contrast, nival and rainfall-fed rivers, including Jhelum, Neelum, and Kunhar, exhibited significant negative trends. For low flows, decreasing trends were observed annually and during extreme low-flow periods (one-day, seven-day, fifteen-day), especially in summer. Some stations, however, recorded increased low flows during autumn, winter, and spring. The pronounced decline in summer low flows highlights extended dry periods, raising concerns for water availability during peak demand seasons, which could adversely affect agriculture and hydropower generation in the region.
Simulating Decadal Sedimentation in the Upper Indus Basin and Tarbela Reservoir Using SWAT Model
Tarbela Reservoir, one of Pakistan's key irrigation and hydropower sources, has experienced diminishing live storage due to high sediment generation from the Upper Indus Basin (UIB), over the past three decades. This study monitors sedimentation trends from 1994 to 2023 using Satellite data from USGS STRM, ERA5-Land Climate Reanalysis, FAO's Digital Soil Map of the World, and Global Land Cover dataset. Ground data came from the Surface Water Hydrology Project and Tarbela Dam Project Library. Using ArcSWAT, a 30-year model was developed for a 102,028 km² area, with 64 HRUs and 26 subbasins to assess spatial heterogeneity. Sensitivity analysis, calibration, and validation were performed using SWAT-CUP and SUFI-2 algorithm showing excellent model performance with 22 parameters. The model achieved R² of 0.89, NSE of 0.84 during calibration, and R² of 0.81, NSE of 0.68 during validation. Model simulations for the Upper Indus Basin at Besham Qila show 78% of precipitation contributes to stream flow, with 61% as surface runoff. July presents peak rainfall, sediment yield, and water flow. Annual surface runoff averages 505.35 mm, with upland sediment yields from 5,772.90 to 2,086.09 Mg/ha. Significant in stream sediment deposition (-2,082.52 Mg/ha) heightens flood risks. Decadal sediment yield decreased from 5.9–385.3 tons/ha to 2.7–370.6 tons/ha. Reforestation, terracing, check dams, and sustainable land use practices are recommended to control sediment generation and deposition in Tarbela reservoir. Continuous monitoring and stakeholder engagement are essential to maintain reservoir storage capacity and hydropower efficiency.
Flood Area Segmentation using Semantic Segmentation-based Deep Learning Models
Floods are natural disasters that frequently occur in different areas of Pakistan and cause several damages including human lives, infrastructure, and material losses. The unavailability of bench mark dataset of the flood data is the primary constraint in improving the flood monitoring systems. With state-of-the-art computer vision and deep learning techniques, flood detection, segmentation, and recognition systems have gained much attention. In this paper, a deep learning based DeepLabV3 architecture has been applied to segment the flood areas from the images. A ResNet-50 model has been used as a backbone network for extracting the features. The model has been trained and evaluated on the flood area segmentation dataset. The performance of the flood area recognition model has been assessed using the mean F1-score and Jaccard index. This research study also compares the performance of DeepLabV3 architecture with other backbone models including VGG-16 and DenseNet. Furthermore, the performance of UNet architecture has been evaluated and compared with DeepLabV3. Based on the experimental results, the DeepLabV3 architecture with ResNet-50 as a backbone model achieved the best segmentation results than the other models.
Systematic Review of Machine Learning Applications in Water Resources Management in Pakistan
Almost all data-driven fields have been revolutionized by machine learning. Water resources management is a rich domain of data, and Machine learning can help improve water resource management by leveraging such a large amount of data. A plethora of studies worldwide have established significant potential in the application of machine learning in water resources management and its subfields. However, a relatively limited number of studies have been conducted in Pakistan, and the opportunity of machine learning from a Pakistani perspective has not been explored. This study aims to present a systematic review of machine learning application studies conducted in Pakistan in the area of water resources management. Over the past five years, nearly two dozen studies have been reviewed that have been done in Pakistan. Studies reviewed focus on groundwater quality and recharge monitoring and management, stream flow forecasting, climate change impacts on stream flows, surface water quality modeling, and drought prediction and management. Different machine learning algorithms were applied to get results for the above-mentioned tasks. For example, Random Forest, Decision Trees, Support Vector Machine (SVM), ANNs, and CNNs. The use of RNN-LSTM and IoT are getting popular day by day. Different researchers explored different machine learning algorithms to perform the same task, and compared the performances of these algorithms. The current study also seeks to suggest a new direction for machine learning-based water resource management in Pakistan.
Impact of Climate Variables on Reservoir Outflow: An AI Approach; A Case Study of Khanpur Reservoir, Pakistan
This research examines the effect of climate factors on the outflow of the Khanpur Reservoir in Pakistan by employing ANN for modeling. To forecast the reservoir outflow, the hydrological and climate variables such as seepage, maximum temperature, rainfall, live storage, evaporation, humidity and inflow are utilized in the analysis. ANN models with 5x5 (5 neurons in each two layers), 10x10, 15x15, 20x20, 25x25 neurons combination were created and evaluated, and it was found that the model with 20 neurons in each hidden layer had the best performance having R² value of the training and validation set was 0.95 and 0.85 respectively. The sensitivity analysis through interaction profiles revealed that live storage is the most significant predictor of reservoir outflow due to its high main effect (84410) and comparatively low interaction with other variables (total effect: 9790). This underscores the importance of accurately measuring and incorporating live storage in reservoir management strategies. Moreover, inflow had moderate direct impact (0.097) and the highest overall impact (0.423) revealing it as another most significant and sensitive predictor of outflow. Rainfall exhibited minimal direct influence with main effect: 0.029) but significant interaction effects (total effect: 0.411) showing its moderate effects on outflow. Total seepage and maximum temperature had small main effects: 0.021 and 0.038, respectively, but their moderate total effects were 0.367 and 0.341, respectively, pointed to their indirect significance. In contrast, evaporation and relative humidity exhibited very small individual and combined impacts on outflow (main effect: 0 and 0.014; total effect: 29.03 and 0.129, respectively). These results reveal the strong impact of climate variables on reservoir outflow prediction and capability of ANN in modelling these aspects. This research adds to the existing literature on employing AI and machine learning for optimizing reservoir management and advancing water management techniques under the new climate reality.
ANN-Based Predictive Modeling of Aquifer Dynamics in Quetta Valley
Monitoring and understanding groundwater level (GWL) dynamics in the Quetta Valley have gained significant attention due to the increasing reliance on groundwater resources. Accurate forecasting of GWL is critical for effective resource management, mitigating risks such as overexploitation, water quality degradation, and land subsidence. This study employs an Artificial Neural Network (ANN) model to predict GWL fluctuations using data collected from 14 monitoring stations over a 40-year period (1980–2020), encompassing a wide range of GWL changes. The dataset was divided into two subsets: 77.23% for training and 22.76% for testing the model. Model performance was assessed quantitatively using root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Additionally, qualitative evaluations were conducted through time-series line plots and scatter plots, offering insights into the model's predictive accuracy. The findings provide a deeper understanding of GWL dynamics in the Quetta Valley and demonstrate the potential of ANN models as effective tools for sustainable groundwater management.
Leveraging Remote Sensing and AI Algorithms for Predicting Inflow and Water Levels in Transboundary Reservoirs on Eastern Reivers of Pakistan
This study integrates remote sensing technologies and artificial intelligence (AI) algorithms to predict inflow and water levels in transboundary reservoirs, a crucial endeavor for managing shared water resources in regions characterized by complex hydrological dynamics. Effective management of these reservoirs is essential for ensuring water and food security, mitigating natural disasters, and supporting economic stability, particularly in countries like Pakistan, which often faces uncertainties regarding reservoir levels and inflows during flood seasons from adjacent transboundary sources. The primary objective of this research is to capture variations in transboundary catchments using remote sensing and AI-based modelling approaches. The focus is on the Bhakra Dam, situated on the Sutlej River in Himachal Pradesh, India, where heavy rainfall, snow melt, and dam operations significantly influence the dam's capacity, leading to potential flooding in Pakistan. This study conducts an inflow, outflow, and storage analysis using various AI algorithms, including Linear Models (LM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Partial Least Squares (PLS), Random Forest (RF), Ridge Regression (RG), and Gaussian Processes (GP). Historical data from 2014 to 2020 regarding inflow, outflow, water levels, and storage from Indian dams was sourced from the Central Water Commission of India. Additionally, daily rainfall data from the Global Precipitation Measurement (GPM) v6 and land surface temperature (LST) and emissivity data from MODIS were utilized. A single C-band synthetic aperture radar (C-SAR) from Sentinel-1 was employed to assess surface area and extent of the dam lake. The performance of the machine learning algorithms was evaluated using the R package Hydrostat, employing a comprehensive set of statistical metrics, including Nash-Sutcliffe Efficiency (NSE), R-squared (R ²), Knowledge Graph Embedding (KGE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Residual Standard Deviation (RSR). To avoid redundancy, these metrics are reported succinctly. The Model Efficiency Index (MEI), with values approaching 1 indicating strong agreement between observed and predicted data, was used as a key indicator of model performance. Among the tested models, Extreme Gradient Boosting (XGB) demonstrated the highest accuracy for predicting inflow, Random Forest (RF) excelled in outflow prediction, and Ridge Regression (RG) was most effective for storage estimation. The results demonstrate a high MEI, indicating the accuracy of AI algorithms for predicting water levels, storage, inflow, and outflow. Furthermore, a correlation model efficiency matrix was utilized to analyze relationships between variables such as level differences, area differences, temperature, and average rainfall, revealing a near-perfect positive correlation (0.99) between storage and level differences. This study underscores the potential of integrating remote sensing and AI technologies to enhance water resource management in transboundary contexts. Potential areas of research with a specific emphasis on comparing their performance against machine learning algorithms involves comparing the predictive capabilities of machine learning algorithms with those of physically-based models for reservoir inflow forecasting. Additionally, research efforts could be directed towards leveraging remote sensing data in regions with limited data availability, particularly in developing countries.
Improving Reservoir Water Supply and Inflow Volume Predictions with Encoder-Decoder Deep Learning Models
Recently, deep learning (DL) models have shown tremendous potential for hydrological prediction, reservoir management, and operational planning. However, their effectiveness in predicting reservoir inflows over extended time horizons remains limited. Recent advancements in deep learning algorithms have enhanced the accuracy of inflow forecasts, but most studies have focused on short-term applications or real-time operations. This study introduces a novel multi-step forecasting framework aimed at improving long-term predictions of reservoir inflow and water supply. Using the snow water equivalent metric and reservoir inflow data, we trained a deep learning model using a Convolution Neural Network (CNN.) - Long Short-Term Memory (LSTM.) encoder- decoder model to forecast inflows to predict future time series of steps during the critical March-August runoff period. The model’s architecture and hyper-parameters were fine-tuned using multi-fold cross-validation of the timeseries, analyzing different adaptations of Encoder-Decoder architectures based on CNNs and LSTMs. The proposed methodology was applied to 40-year time series of SWE and inflow data from the Jor- danelle Reservoir in Utah. The optimal configuration a 16-node-per- layer LSTM Encoder-Decoder model demonstrated significant improvements in long-term forecast accuracy. We further examined the balance between model complexity and performance by benchmarking against a A process-driven Ensemble Streamflow Forecasting model and traditional statistical methods, including SARIMA, VAR, and TBATS. The deep learning approach outperformed statistical models in long-term water supply forecasts and achieved comparable accuracy to the ESP model’s 50% exceedance probability forecast. These findings indicate the potential of enhanced DL methods to improve the long-term hydrological fore- casting and resource management.
Multi-Hazards Susceptibility Assessment for Landslides and Floods in Hunza-Nagar, Northern Pakistan
The destructive potential of natural hazards has increased due to rapid urbanization and greater exposure, leading to significant loss of life, infrastructure damage, and environmental degradation. Floods and landslides, among the most prevalent geo-hazards, often occur simultaneously in vulnerable regions. This study focuses on the hazard component of disaster risk by developing a multi-hazard susceptibility map (MHSM) for the Hunza-Nagar region, prone to both floods and landslides. For landslide susceptibility, the Frequency Ratio (FR) model is employed, considering geo-environmental factors like elevation, slope, LULC, rainfall, lithology, and proximity to roads, rivers, and faults. A landslide inventory of 139 events is split into training (70%) and validation (30%) datasets, achieving an accuracy of 91.6%. For flood susceptibility, the Random Forest (RF) model is applied using ten explanatory factors. A 100-year return period flood simulation in HEC-RAS provides flood extents from which flood inventory is prepared, achieving an accuracy of 91.2%. The MHSM integrates FR-based landslide and RF-based flood susceptibility maps using a combined sum approach in GIS. The region is classified into four zones: No Hazard, Flood Hazard, Landslide Hazard, and Combined Hazard. The MHSM maintains high prediction accuracy and serves as a reliable tool for identifying multi-hazard zones. This map is essential for disaster management authorities and infrastructure planners, enabling targeted mitigation strategies to reduce hazard impacts and enhance regional resilience.
Addressing Climate Change with Green Technologies: Biomass-Based Remediation of Heavy Metals in Hattar Industrial Estate Wastewater
Heavy metals in industrial wastewater, such as lead, mercury, and cadmium, pose significant environmental and health risks. This study examines the use of biomass from seasonal fruit and vegetable peels (banana, onion, watermelon, and melon) to reduce heavy metal concentrations in wastewater from Hattar Industrial Estate, specifically from FAG Pharma and Fauji Banaspati. Four samples were prepared with varying biomass quantities and treatment durations: Sample 1 (1g biomass, 6-hour treatment with 3 hours of mixing and 3 hours of resting), Sample 2 (2g biomass, 6-hour treatment with 3 hours of mixing and 3 hours of resting), Sample 3 (1g biomass, 24-hour treatment with 12 hours of mixing and 12 hours of resting), and Sample 4 (2g biomass, 24-hour treatment with 12 hours of mixing and 12 hours of resting). Water quality was assessed using BOD, COD, TDS, TSS, EC, pH, and Atomic Adsorption Spectroscopy (AAS) for metal detection. The best results were observed in Sample 4, with 2g of biomass and a 24-hour treatment, showing the greatest reduction in heavy metal concentrations. This approach provides a cost-effective method for treating industrial wastewater.
Fruit Waste to Nano- Wonders: Green Synthesis of Silver Nanoparticles for Microbial Application
Resistance of microbes developed as one of the most persistent worldwide threats to the health, which lead to the crucial need for advanced and sustainable antimicrobial solutions. The silver nanoparticles gathered a substantial consideration as a potential alternate to commercial antibiotics because of their antimicrobial activity against the pathogens that show the resistance against the drugs. However, AgNPs synthesis through the conventional methods majorly contains the use of harmful chemicals and involvement of intense energy procedures that cause risks to the health and environment as well. In this regard, green synthesis proposes an environment-friendly and economical method, where the natural materials such as fruit waste oils are utilized to produce AgNPs. The given review inspects the synthesis of the silver nanoparticles (AgNPs) using fruit oil acting as a reducing and stabilizing agent following the green approach, this procedure addresses not only the issue of food waste but also helps to minimize the ecological footprints caused by the production of nanoparticles. The synthesis process is explained in depth, while concentrating on the reduction and formation of silver ions and nanoparticles having the stability. The characterization of the AgNPs that are synthesized is carried out using a number of techniqies, including X-ray diffraction (XRD), UV-Vis spectroscopy and transmission electron microscopy (TEM), these techiques give the insights into the morphology, size and crystalline structure of the nanoparticles (NPs). Additionally, the AgNps are assessed for their antimicrobial properties, which underlines the working of the AgNps against the bacteria that are pathogenic in nature. Eventually, this work stresses on the formation of silver nanoparticles using the fruit waste oil following the green synthesis approach, aligning with the concept of sustainability and effective utilization of the resources, this provides an innovative way to fight against the microbes that are resistant and involvement in the sustainability of the environment.
A Bibliometric Analysis of Scaffolding Safety: Challenges, Trends, and Solutions in Construction Projects
Scaffolding plays a critical role in construction, offering essential support for workers at various heights for the smooth functioning of various construction activities. However, it poses significant safety risks stemming from poor design, material deficiencies, environmental factors, and inadequate worker training. This study explores scaffolding safety challenges and proposes strategies to mitigate risks and improve efficiency. By employing bibliometric analysis of Scopus data and tools like VOS viewer, it visualizes research trends, identifies critical safety issues, and highlights advancements in protocols. Key findings emphasize the necessity of certified materials, advanced design technologies, supervised assembly processes, and robust training programs. Emerging global research trends highlight interdisciplinary collaboration and the integration of technologies like automation and deep learning to address scaffolding challenges. The study offers actionable recommendations for industry stakeholders, advocating for innovation and improved safety practices to enhance project outcomes. These insights aim to foster safer, more efficient construction environments through the adoption of advanced tools and best practices.
Integrating SWAT and HEC-RAS models for enhanced flood risk mapping in Swat River, KPK, Pakistan
This research is seeking ways to enhance flood prediction and early warning systems accuracy with better hydrologic and hydraulic modeling. This study offers an automated methodology to integrate two tools - SWAT and HEC-RAS – that automatically associate rainfall-runoff events to river's hydraulic processes. The effectiveness of the system was evaluated in the Swat River basin, Khyber Pakhtunkhwa, Pakistan, a flood prone area due to high rainfall. Relying on sensitivity analysis permitted the variation of model parameters and uncertainty of input data with an assurance of model soundness. The examinations show that the simulated and measured values of stream flow over a period of 23 years correlate strongly to each other with R² = 0.95. Computational efficacy is stated with the phrases real-time flood forecasting and flood forecasting in less than 60 minutes. The rest of the research is focused on assessing its broader implications on policies of regional flood risk management in addition to climate change adaptation, showcasing the usefulness of integrated hydrologic-hydraulic modeling within early warning systems
Investigating the Economic and Environmental Feasibility of White Marble Powder as a Partial Cement Replacement in Concrete
There is a growing push in the construction industry to adopt more sustainable materials due to the high carbon footprint of ordinary Portland cement. This study evaluates the use of white marble powder (WMP), a waste byproduct of marble processing, as a partial cement replacement in concrete. Concrete mixes were prepared with 0%, 10%, 20%, 30%, and 40% WMP replacing cement by weight, and tested for workability and mechanical properties (compressive, split tensile, and flexural strength). The 10% WMP mix exhibited the best overall performance, with 28-day compressive, tensile, and flexural strengths increased by approximately 13–14%, 12%, and 9–10% respectively compared to the control mix (0% WMP). Workability (slump) also improved with increasing WMP up to 40%, although mixes with ≥30% WMP showed noticeable strength reduction and potential workability issues (segregation) at the highest replacement. An economic analysis showed that replacing 10% of cement with WMP can reduce cement usage and cost by about 10% per cubic meter of concrete, and an environmental assessment indicated a roughly 10% reduction in CO₂ emissions associated with cement production at this replacement level. Higher replacement levels yield greater cost and CO₂ savings (up to 40% at 40% WMP) but with compromised strength. These findings, supported by recent literature, suggest that a moderate incorporation of marble powder (around 10–15%) is technically, economically, and environmentally feasible, achieving a balance between improved concrete workability, adequate strength, cost savings, and reduced carbon footprint.
Review on Wireless Power Transfer in Electrified Pavements: Advancing Sustainable Urban Transportation with Magnetic Materials
Wireless Power Transfer (WPT) technology has gained renewed attention due to its potential applications in various fields, including transportation. The concept of charging electric vehicles (EVs) wirelessly has emerged as a promising solution to address the challenges associated with traditional charging infrastructure. Since electric cars (EVs) are increasingly replacing traditional gasoline-powered vehicles, it is essential to charge EVs effectively while minimizing energy loss. WPT solutions for electrified pavements show promise as a replacement for traditional charging stations. With a focus on WPT's capacity to dynamically charge moving vehicles, this analysis examines three important electric road technologies: conductive rail power transfer, pantograph line power transfer, and WPT. According to research, power transmission efficiency is greatly increased when soft magnetic composites (SMCs) are incorporated into road materials. In order to maximize the performance of WPT systems integrated into road infrastructure, this research looks at the importance of magnetic materials, namely ferrite-based composites. High prices, efficiency improvement, safety issues, electromagnetic interference, and infrastructure durability are some of the obstacles to integrating WPT onto electrified pavements. Moreover, electrified pavements encourage the use of renewable energy sources, which makes the transportation system greener and more sustainable.
Enhancing Seismic Performance and Material Efficiency in Multi-Storey RC Buildings Using Edge-Lined Shear Wall (ELSW) Systems
This research investigates the structural performance of multi-storey reinforced concrete (RC) buildings using the Edge-Lined Shear Wall (ELSW) system, in comparison to traditional solid shear walls and staircase walls. The ELSW system, strategically positioned along the building's edges, aims to optimize lateral stiffness, reduce material consumption, and enhance energy dissipation under seismic wind loads. A detailed finite element (FE) model of a multi-storey RC building was developed and subjected to lateral load analyses. Key performance metrics, particularly inter-story drift, were evaluated for each configuration. Results indicate that the ELSW system significantly reduces inter-story drift compared to staircase walls and performs comparably to solid shear walls, albeit with reduced material usage. Additionally, the ELSW system demonstrates superior energy dissipation during seismic events, underscoring its potential to enhance earthquake resistance. This research highlights the ELSW system as a sustainable, cost-effective alternative to conventional shear walls, offering a promising solution for the design of earthquake-resistant multi-storey RC buildings. The findings contribute to advancing innovative structural design methodologies focused on performance optimization and material efficiency.
Design of Job Mix Formula (JMF) for Recycled Asphalt Pavement (RAP)
In hot climatic regions such as Pakistan, asphalt pavements often experience premature deterioration due to extreme heat, causing issues like cracking and rutting that significantly shorten their functional lifespan. roads don’t last long. Therefore, the need for asphalt rehabilitation prior to its use is almost necessary. This research article presents a laboratory study of asphalt Rehabilitation. Rehabilitated asphalt samples have been produced by mixing a 60/70 penetration grade unmodified (base) asphalt with used material content. The results indicated rehabilitation improved conventional. Although, the usage of rehabilitated asphalt road will decrease the cost of the binder use. Therefore, the usage of rehabilitated asphalt road is more economical, because it gives better results of stability and flow values as well. The Optimum Bitumen Content appeared to be 1.35 % whereas Stability values came to be 1441.843, 1480.292 ,1587.95 ,1238.063 ,1134.25 of various Bitumen Contents.
Statistical Analysis of Slump Flow Using Gene Expression Programming (GEP) for Self-Consolidated Concrete
Statistical analysis of the slump flow prediction by the application of Gene Expression Programming on the data points of 953 related to Self-Consolidated Concrete. SCC is the acronym for Self-Consolidated Concrete, high-flow concrete, supposed to self-consolidate. This type of concrete really demonstrates its superb applicability especially in the dense and complex structures with reinforcement. Several design variables, namely the water-to-cement ratio, aggregate properties, and admixtures affect this characteristic: therefore, making it rather difficult to predict precisely its slump flow behavior. GEP was applied to analyze a dataset obtained from a sequence of experiments and yielded a predictive model for slump flow. Descriptive analysis tools, regression techniques, as well as error metrics, MSE, RMSE, and R², have been used to test the robustness and reliability of the model. The results have also indicated that, based solely on the mixed design parameters, it is possible to predict slump flow by GEP. Significant relationships have also been found between values of slump flow and input factors.
Effects of Banana Fibers on Mechanical Characteristics of Stone Mastic Asphalt
The pavement industry faces challenges due to global warming and population growth, causing frequent rutting and fatigue cracking in the pavements. To combat these pressing issues, Stone Mastic Asphalt (SMA) can be a viable solution for heavy-traffic-prone road networks. However, polymer-modified asphalt binders and natural or synthetic fibers are typically required to prevent draindown because of their high binder content. Meanwhile, a huge amount of Banana fibers is disposed of in the form of waste generated by Banana cultivation, posing environmental challenges. Therefore, this research article investigates the effects of adding banana fibers to Stone Mastic Asphalt for sustainable and environmentally friendly road construction by utilizing agricultural waste. During the Modification of SMA Banana fibers at a fiber content of: 0.2%, 0.3%, and 0.4% with fiber lengths of 20mm, 25mm, and 30mm were examined based on draindown experiments, in which 0.3% was found to be optimum fiber content. The mechanical properties of banana fiber-modified stone mastic asphalt (BFM-SMA) prepared at optimum fiber content were compared to the conventional SMA using the Marshall Stability and Cantabro loss test. It has been discovered that the increased fiber length improves the mechanical characteristics of the BFM-SMA, as the modified SMA with banana fibers of 30mm length shows the best results, which strongly suggests that this approach could effectively address road problems.
Integrating Sustainability into Construction Contracts: Evaluating Standard Forms for Effective Implementation in Pakistan
The construction industry plays a pivotal role for economic growth, social progress, creating job market, providing vital infrastructure, and driving innovation. However, the conventional construction practices harm the environment by utilizing large quantities of natural resources, producing limitless waste, and emitting greenhouse gases by the use of materials like concrete, steel, plastic, glass and wood etc. Additionally, improper waste disposal practices and land disruption cause pollution and loss of biodiversity. These practices are unsustainable and need deviation towards greener and more energy-efficient methods of construction to reduce the environmental impact. These methods come under the umbrella of sustainability which is the ability to meet the needs of present without compromising the ability of future generations to meet their own needs. The public awareness regarding the concept of sustainability is quite good among the literate public but many face challenges in adopting sustainable practices due to lack of accessible resources, cost-effective solutions, clear guidelines, and tools. One of the most effective ways to ensure sustainability especially in construction projects is by incorporating sustainability clauses during contract formation. This would make sustainability not just a voluntary act but a contractual obligation. Globally, various standard forms of contract documents are being followed and a comparative analysis is needed to determine which document is the most effective in promoting sustainability. In Pakistan, we need to assess whether the current standard contract document that we follow is sufficient or we need to adopt a better model that ensures sustainability effectively.
Comparative Analysis of Partial and Full CFRP Retrofitting Approaches for Enhancing Structural Integrity in Reinforced Concrete Frames After Column Removal
Columns form an important part of structural RC frame because they carry the vertical and lateral loads down towards the foundation. When they collapse or are torn down because of the changes in space or accidental destruction, the threat of successive collapse appears much more probable. The conventional retrofitting process of carbon fiber-reinforced polymer (CFRP) normally involves full surface application that ultimately demand clearance of site, exposure of buildings, and discontinuation of business. This paper is fitted with yet another area of retrofitting in which bare column faces are isolated in order to be retrofitted so as to ensure that the impact is reduced to a minimum in an effort to produce a major improvement in the building. Partial and full CFRP retrofitting method was applied in determination of structural behavior of a three-story reinforced concrete frame under column removal loading conditions using state of the art modeling software (such as Tekla and ETABS). It shows that retrofit using bare surfaces can be highly effective, especially in load capacity and in stability, and it provides a low cost and operationally feasible option. The approach minimizes downtimes, does not require demolition and makes it possible to have constant functioning, so it is most appropriate in retrofitting projects of existing structures.
Exploring the Mechanical and Durability Properties of Sustainable Bentonite Concrete
The rising global demand for cement has posed significant environmental challenges, including excessive energy consumption, depletion of natural resources, and contributions to climate change. Supplementary cementitious materials (SCMs) offer a sustainable solution by providing eco-friendly alternatives for cement production. They enable the development of durable and environmentally friendly concrete. This study investigates the individual and combined effects of bentonite and silica fume as partial replacements for cement in polypropylene fiber (PF)-reinforced concrete. The experimental program consisted of 18 unique mixes, including one control mix that used only OPC as the binder. Six primary mixes replaced 10% OPC with bentonite, incorporating micro-PF at varying levels from 0% to 1.25% (in 0.25% increments). Another six mixes replaced 10% OPC with silica fume, maintaining the same PF variations. Additionally, five tertiary mixes combined 15% bentonite and 5% silica fume as SCMs, with PF content ranging from 0.25% to 1.25%. Performance evaluation included tests for slump, compressive strength, splitting tensile strength, and water absorption. Results revealed that increasing PF content beyond 0.75% mostly adversely affected concrete properties.