Kathford Journal of Engineering and Management

Evaluating the Capacity of Existing Stormwater Drainage System in Kapan, Kathmandu via SWMM Mode

2024-02-20T04:28:31+00:00

Overflowing drainage systems in the Nepalese cities are the most significant issue due to unplanned urban expansion and inadequate drainage networks. As the city's drainage networks are not designed to handle the extra runoff from catastrophic flooding incidents, flash floods typically occur during short-duration, and high-intensity rainstorm storms frequently. In the vicinity of Kapan, Kathmandu, and many other Nepalese cities, particularly during the rainy season, roads are seen changing into the streams. This study employs a hydrological analysis model to examine the storm water drainage system existing currently in the Kapan area. Being the EPA Storm Water Management Model (SWMM) physically based, deterministic type that simulates the inflows, outflows, and storages of water within a sub-catchment efficiently, it is chosen here mainly to model the catchments specifically. Though it has not been employed frequently in the context of Nepal, despite its popularity in the industrialized countries such as United States of America for the proper management of the storm water, the SWMM is particularly employed here to model the catchments due to its publicly available, easy to obtain, simple data input parameters, and simple processing steps in compared to other relatively more expensive yet sophisticated models. The catchment's overall area of 360.57 hectares was at first separated into the 86 sub catchments using the SWMM based on its surface elevation and existing drainage network, and the attributes of each sub-catchment were then assigned accordingly. After the proper identification of the sub-catchments, the runoff from the relevant sub-catchments was routed to the respective nodes, and finally to the outlet via conduits. In the current study, the drain system (Combined Sewer line) is given along the center of the road network. The storm network are represented by junctions, conduits, and outfalls. The longitudinal and velocity profiles of the drain were obtained. The critical runoff and capacity of existing drains were determined, as well as their validation with discharge derived using the rational technique. With the runoff generated during peak rainfall 45.08 mm/hr., the Kapan area's existing drainage system was found to be insufficient. This study suggests that the SWMM would be the most effective model to be employed for predicting the sudden surface runoff and for prior managing of the storm water, particularly in Kathmandu and other vulnerable parts of main cities in Nepal, where overflow has caused serious problems during rainfall.

Factors Affecting Job Satisfaction of Civil Engineers Working in Private Organizations in Kathmandu Valley

2024-02-20T04:44:20+00:00

The satisfaction of employees in their jobs holds a substantial influence on the overall success and effectiveness of an organization. This study is focused on evaluating the factors that directly affect the job satisfaction of civil engineers employed in private organizations in Kathmandu Valley. To gather primary data, structured questionnaires were distributed utilizing the summation job factors method. 120 questionnaires were collected and subsequently employed for detailed data analysis using SPSS 25. To ensure the reliability of the collected data, the internal consistency of the construct variables was assessed using Cronbach's alpha. All computed alpha values for the constructs surpassed the threshold of 0.8, indicating a high level of consistency. Data were analyzed using mean, standard deviation, frequency, percentage, and multiple linear regression. The findings revealed a high level of job satisfaction with a mean score of 3.4. Specifically, factors such as the role of supervisors, payment/salary, working conditions, empowerment and leadership, and relationships with co-workers played significant roles in influencing job satisfaction. However, the job itself and promotion opportunities were not identified as significant factors. The coefficient of determination (R²) was calculated to be 0.872, signifying that these independent variables collectively explain a substantial 87.2% of the variation in job satisfaction among civil engineers working in private organizations in Kathmandu Valley. In light of these findings, private organizations and relevant authorities must prioritize enhancing the core responsibilities tied directly to the job, as well as providing ample opportunities for career advancement.

A Comparative Study of Machine Learning Algorithms for Early Cost Estimation of Building Projects in Nepal

2024-02-20T05:15:29+00:00

Construction cost estimation is crucial to a project’s success, but because of the many variables that impact it, it is challenging to make an accurate prediction. Traditional methods are being used for preliminary cost estimation in the construction industry of Nepal. There still exists the problem of cost overrun, and time delay due to incorrect cost budgeting. This study aims to analyze a modern method of preliminary cost estimation in Nepal to prove its efficiency over the traditional method. In this work models such as Linear Regressor, Decision Tree Method, Random Forest method, Artificial Neural Networks, Support Vector Machine, Boost method, Extra tree method, Voting Regression, and Stacking method are used. Regarding the datasets, the buildings that were used are Educational Building, Commercial Building, Hospital Building, Residential Building, Public Building, Official Building, and Hotel Building having 0 to 2 basements ranging above 1 crore. The input features were taken from the literature review, and validated by expert opinion, and after successfully conducting pilot testing, the survey questionnaire was distributed among contractors and consultants. Data preprocessing was done and training and testing data sets were developed. The model was developed for nine algorithms. Mean absolute error (MAE), Mean square error (MSE), Root mean square error (RMSE), and R square value are used as evaluation metrics. In the evaluation of various regression models, three stand out as the most promising for predicting the target variable. The Decision Tree model exhibited remarkable performance with an MSE of 0.088575, an MAE of 0.104625, an RMSE of 0.297615, and an R² of 0.876170. Similarly, the Extra Tree model closely followed with an MSE of 0.088601, an MAE of 0.102909, an RMSE of 0.297659, and an R² of 0.876134. The Voting Model with an MSE of 0.105035, an MAE of 0.222807, an RMSE of 0.324091, and an R² of 0.853159. This study also opens the path for the exploration of other models and motivate to follow the trends of machine learning in the present era.

Factors Affecting the Use and Understanding of Nutrition Labeling on Food Labels among Nepalese Youths

2024-02-20T05:28:09+00:00

Nutrition labeling, crucial for informing consumers about food content, faces significant gaps in Nepal's regulatory framework. These gaps impact how the nutritional information is understood and used by consumers, contributing to challenges like obesity and chronic diseases in the country. This study aims to uncover the factors affecting the use and understanding of nutrition labeling on food labels among Nepalese youths. Conducted through a cross-sectional approach, this research involved administering a structured questionnaire to 225 participants in June 2023, targeting regular consumers of packaged foods. Data analysis, employing both descriptive and multivariate principal component analysis using SPSS 25, revealed that 24% of respondents always read nutrition labeling on food labels in packed food items. The Principal Component Analysis identified four key factors affecting the use and understanding of nutrition labeling: "perception of the nutritional information", "motivations in dietary decisions", "awareness" and "health priority interest in nutritional attributes". These factors collectively explained over 54% of the variance in the data, as validated by the scree plot and Kaiser Criterion. The study underscores the necessity of initiatives aimed at enhancing the accessibility, clarity, and appeal of nutritional labeling on food labels in packaged food products. Such initiatives are pivotal for policymakers, educators, and marketers striving to encourage healthier dietary practices among Nepalese youths.

A Review on the Application of Machine Learning Algorithms on Smart Grid Optimization

2024-02-20T05:34:03+00:00

With the increasing challenge of distributed and renewable energy sources, maintaining the stability of the power grid is becoming increasingly difficult. By incorporating information and communication technologies, along with machine intelligence, the conventional power grid has the potential to evolve into a smart grid. The integration of machine learning equips the smart grid to make its decisions and efficiently handle generation, power outages, transmission line failures, unforeseen shifts in customer demands, overall fluctuations in renewable energy production, or any unexpected catastrophic events. These diverse machine-learning algorithms play a crucial role in enhancing the functionality of smart grids, contributing to their optimization. This article examines various machine learning algorithms and approaches designed to optimize the responsiveness of each facet of smart grid optimization.

Forecasting Remittance Inflow in Nepal Using the Box-Jenkins ARIMA Model

2024-02-20T05:42:04+00:00

The remittance, defined as a portion of household income sent by individuals from their earnings in foreign economies, constitutes a substantial aspect of Nepal's current financial landscape. This research endeavors to identify an appropriate ARIMA model to forecast the remittance inflow in Nepal from 1990/91 to 2021/22. The Box-Jenkins methodology serves as the framework for modeling and forecasting the annual remittance inflow, with EViews 12 software employed for comprehensive data analysis. Various ARIMA models were evaluated to capture nuances in annual remittance trends. The investigation identified the ARIMA (1,1,1) model as the most suitable for forecasting Nepal's remittance inflow. This finding provides essential insights for policymakers, economists, and stakeholders, facilitating informed decisionmaking and future economic planning in the country.

ANSYS based Modal Analysis of a Fixed Beam of Uniform Cross Section with a Concentrated Mass at Mid Span

2024-02-20T05:49:37+00:00

The report outlines ANSYS based modal and structural analysis of a fixed beam of dimension 20mm*20mm*1000mm that was constructed in SOLIDWORKS. Structural steel was selected as the material for analysis. The analysis was done in 3 phases, the first analysis was done without any crack and the other two analysis involved cracked beams with cracks created at different lengths. Suitable boundary and loading conditions were used to find the modes of frequencies. The resulting variation in static deflection, position of mode shapes representing transverse deflection and variation in position of equivalent stress corresponding to varying beam conditions with and without cracks are studied.

Impact of Window-to-Wall Ratio on Lighting Load in Municipality Building-A Case Study of Bharatpur Metropolitan Administration Bloc

2024-02-20T06:08:06+00:00

Visual comfort is a critical aspect of indoor environments, contributing to occupant satisfaction, well-being, and overall productivity. In the face of rapid urbanization, particularly in regions like the Chitwan District, understanding and enhancing visual comfort in built environments take on heightened significance. The primary focus of this study is to explore how varying Window-Wall Ratios (WWR) affect the energy performance of buildings. The research begins with an in-depth review of existing literature on building energy efficiency, focusing on the significance of WWR in determining natural lighting penetration. Field measurements are collected to establish a baseline understanding of the building's lighting performance. Utilizing advanced simulation tools, the study explores various WWR scenarios to simulate the potential impact on lighting load. The analysis considers both energy efficiency and occupant comfort, aiming to identify an optimal WWR that balances natural lighting benefits with energy conservation goals. The study finds that a 30% WWR represents the ideal balance for effective electricity consumption for lighting. Monthly analysis underscores its significance by revealing diminishing returns beyond this point. Consequently, recommended optimal WWR values stand at 25% for the ground floor and 30% for both the first and top floors. The findings hold significant importance for designers and policymakers in making informed design and policy decisions.

Impact of Employees’ Turnover on the Performance of Five Star Hotels of Kathmandu

2024-02-20T06:14:19+00:00

This study examined the Impact of Employee Turnover on the Performance of Five five-star hotels. Data was collected from the 120 hotel employees from six hotels in Kathmandu using the questionnaire. The employees were randomly sampled. Jamovi was used to run the analysis. While collecting the data among the respondents, 91 (75.8%) were Male, and 29 (24.2%) were female. Likewise, 64 (53.3%) were staff, 36 (30.0%) were supervisors, and 20 (16.7%) were heads of departments. Out of 120 respondents, 51 respondents (42.5%) had worked in the respective hotel for <5 years, followed by those who had worked for 6–10 years were 22 respondents (18.3%), while 12 respondents (10.0%) had worked for 11–15 years and 35 respondents (29.2%) had worked for more than 16 years. Employee turnover has highly affected the Organizational performance of the hotel. So, for that reason, the hotel has used various strategies related to employee retention. Likely examining employee turnover and its impact on organizational performance, the researcher uses correlation and regression, which shows the negative impact on organizational performance.

IoT Application in Agriculture: A Spotlight on Indoor Plant Monitoring System-IPMS

2024-02-20T06:27:43+00:00

The incorporation of Internet of Things-IoT technology in agriculture has ushered in a transformative era, shifting from qualitative, experience-based practices to quantitative, datadriven methodologies in recent years. This paper delves into the world of IoT in agriculture, with a particular emphasis on Indoor Plant Monitoring Systems. This study investigated the practical implications of an IoT framework designed for indoor plant monitoring, to bridge the gap by focusing on improving data collection and visualization capabilities. A prototype-based approach was used, which included DHT11 sensors for temperature and humidity monitoring, a soil
moisture sensor, and a watering actuation subsystem. Succulent plants were chosen as resilient samples to test the IoT system's ability to capture and respond to critical parameters because of their capacity to endure changes in environmental conditions and flourish in arid environments. The DHT11 sensor results demonstrated the interconnected dynamics of temperature and humidity, providing the important insights into climate control strategies for optimal plant growth. The data from the soil moisture sensors, combined with manual interventions, demonstrated the IPMS's adaptability in maintaining favorable soil conditions. A point-biserial correlation analysis, in particular, revealed a strong negative correlation (−0.89) between moisture levels and water pump status, confirming the system's efficacy in automated watering. The IPMS demonstrated its effectiveness in leveraging real-time data for informed decison-making, paving the way for future enhancements and comprehensive plant health assessments.

Brain Tumor Detection Using Convolutional Neural Networks (CNNs)

2024-02-20T06:36:32+00:00

Brain tumors are a serious medical condition that requires early detection for successful treatment. However, accurate diagnosis can be difficult and time-consuming, and current methods such as MRI scans can be expensive and may require highly trained specialists to interpret the results. A model of a brain tumor detection system using Convolutional Neural Networks (CNNs) has been proposed to address these challenges. To use this model, a dataset of medical images of the brain is collected, the dataset is then preprocessed, and the relevant feature is extracted from the images using CNNs. The developed CNN model is designed and trained to accurately detect the presence and location of brain tumors in the images. Optimization of the CNN model's performance is done by experimenting with different architectures, hyperparameters, and optimization techniques, and its performance is evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. The model training was carried out on MRI images containing tumors and without tumors. The developed CNN-based model achieved impressive accuracy in detecting brain tumors, demonstrating high precision and recall rates. This brain tumor detection system has the potential to significantly improve the accuracy and efficiency of brain tumor diagnosis, leading to better treatment outcomes and reducing the burden on healthcare systems.

Blade Structure Analysis of 2 MW V-Shaped Vertical Axis Wind Turbine Using Numerical Simulation

2024-02-20T07:11:55+00:00

The intensive research on large-scale Vertical Axis Wind Turbine (VAWT) couldprovide an alternative to Horizontal Axis Wind Turbine (HAWT) in offshore deployment for the future. Regarding this fact, this article develops a conceptual design of the VAWT blade structure and analyzes its feasibility using numerical simulation. The blade structure is designed for 2 MW Darrieus type V- shaped VAWT. The blade is tapered, 90m long, and inclined at 35° to the vertical axis. Initially, the blade design is tested for ultimate strength test according to IEC 61400-01 standard using aluminum alloy and homogenized composite material. In doing so, maximum aerodynamic loading on the blade is calculated after steady state 3-D RANS CFD simulation of the blade in ANSYS Fluent. The Finite Element Method (FEM) model of the blade is created using shell element in ANSYS Static Structural with structured meshing strategy, and tested with the aerodynamic load using the one-way Fluid-Structure Interaction (FSI) technique, to obtain mesh independent solution. The impact of the using 2, 3, and 4 number of shear webs is then analyzed on the selected mesh model of the blade. Finally, a four-shear web model of the blade is tested under extreme conditions. The thickness of the blade surface and shear web are varied to test maximum deflection at the tip, and maximum allowable strain of the blade. After obtaining the required blade structure, the deformation, maximum equivalent stress, and maximum equivalent strain for the blade are studied at rotation Tip Speed Ratio (TSR) of 5 under aerodynamic and gravitational loading, using both materials. The study showed significant deflection at the tip of the blade for the tower-less V-VAWT blade, suggesting an alternative support mechanism for the blade. Also, the study concluded that better structural robustness is achieved while using a composite material instead of an aluminum alloy. This article provides basis to study structural behavior of the novel V-VAWT blades and contribute to continuing research on obtaining optimized blade structure for such turbine.

Carbon Emissions Due to Construction of Building Using Cement-Stabilized Compressed Earth Bricks and Comparison with Conventional Fired Earth Bricks

2024-02-20T07:35:44+00:00

Carbon emission from human activities including civil engineering constructions has been a major global environmental issue. The emissions due to the use of conventional fired earth bricks (CFEB) in the construction industry are significantly larger, and a large number of researches have been devoted to developing viable alternatives to the uses of CFEB in the construction industry to achieve a low-carbon society. This research investigated carbon emissions due to the use of cement-stabilized compressed earth blocks (CSCEB) in place of CFEB in the construction of a community building in Bidur Municipality, Nuwakot, using the standard tools and methods by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Also, the Bilan Carbone tool was used. Then the emission results of the two cases (CSCEB and CFEB) were compared from different perspectives. Among the considered major emission sectors, the construction materials sector contributed the highest carbon emissions in both bricks. Results indicate that CSCEB requires lower quantities of cement, sand, and aggregates compared to CFEB. Major construction materials contribute significantly to carbon emissions, with CSCEB showing a 1.7 times lower impact than CFEB. The total carbon emissions for CSCEB and CFEB were 160.97 and 206.42 Tons of CO₂ equivalents in this study. That is, the total carbon emission from CFEB construction was about 1.3 times of the CSCEB. Furthermore, the direct emissions in both cases were almost the same, while the 1.4 times larger emission in the case of CFEB was the sole contribution of indirect emissions. The results of this study once again demonstrated that CSCEB can be an alternative to CFEB in the construction industry to achieve the objective of a low-carbon society.