Civil Engineering
Permanent URI for this collection
Browse
Browsing Civil Engineering by Academic Level "Masters"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Assessing Service Quality of Ride Hailing Bike Service within Kathmandu Valley(I.O.E, 2024-07) Chaudhary, Ambika; Shrestha, Pradeep K.Adopting a sustainable transportation approach necessitates a shift towards eco-friendly travel modes, such as ride-hailing bike services like Pathao and Indrive. These services which have introduced a potentially transformative change to Nepal's transportation landscape are prominent in Kathmandu valley, as evidenced by their daily ridership and public recognition. Being a relatively new concept, assessing its service quality is crucial for its continued viability. Evaluating perceived service quality involves a complex decision making process that considers various observed and unobserved factors. This study evaluates the service quality of Pathao and Indrive bike services using structure equation modeling to identify unobserved influencing factors. Six latent factors were identified through factor analysis. An empirical model was developed to understand the interactions among key variables affecting service quality. SPSS 22 and SPSS Amos 21 were used for model development. The study found that user safety is the most significant latent variable influencing service quality followed by service features and application efficiency. The heterogeneity among users regarding different service quality attributes were also analyzed. This study will provide valuable insights to improve these services, enhancing their effectiveness and usability and provide clarity to inform suitable policy decisions.Item Prediction of Optimum Bitumen Content in Asphalt Mix Design Using Artificial Neural Network(I.O.E, 2024-07) Giri, Moti Ram; Tamrakar, Gautam Bir SinghThe Marshall design process, commonly employed for estimating Optimum Bitumen Con-tent (OBC), is known for its designation as the asphalt mix design and quality control of asphalt concrete is often constrained by the conventional Marshall Mix Design methodol-ogy. Which is characterized by its time-intensive nature, labor requirements, and suscepti-bility to result variations. This study explores different predictive modeling techniques to enhance the efficiency and accuracy of determining Optimum Binder Content (OBC), Me-chanical and Volumetric properties of hot mix asphalt. The study examines Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) models to predict mechanical (MST, MFV) and volumetric prop-erties (AV, BSG, VFA, VMA) using variables such as aggregate gradation, specific grav-ity, and proportions of fine and coarse aggregates in the mix. A comprehensive dataset of 148 Marshall mix design forms was collected, and 141 valid sets were used after outlier analysis. Descriptive statistics revealed skewness, and compliance checks against SSRBW 2016 standards highlighted areas of concern in Marshall quotient and Filler-Binder Ratio. Centrality analysis showed significant deviations between mid-point gradation and sample mean for specific gradations.Item Spatio-Temporal Analysis of Road Traffic Crash Hotspots in Kathmandu Valley, Nepal(I.O.E, 2024-07) K.C., Anuradha; Pradhananga, RojeeKathmandu Valley is one of the rapidly urbanizing cities in Nepal, which registers the highest incidence of road traffic crashes compared to other regions in the country. Identifying the dangerous road sections where the crashes happen frequently (or simply hotspots) is a critical initial move towards devising effective strategies for reducing the future severe incidences in the Valley. On this account, this thesis aimed to pinpoint the hotspots in the Valley by examining the spatial and temporal patterns of reported traffic crashes. The study also took an additional step by investigating relationship of hotspot occurrences with two spatial factors; population density and land use. The study utilized three years of crash data (2019-2021) collected from Traffic Police Office, and road network polylines obtained from Survey Department. Of the total data, 23,278 (79.55%) crashes and 912.54 km of road network were analyzed. The temporal distribution reveals a notable increasing trend of crashes and their severity. The incidence of fatalities and severe injuries reaches its highest level during the month from October to April. The highest frequency of crashes is recorded during the weekday morning rush hours, from 8:00 AM to 12:00 PM, in the afternoon time, from 12.00 PM to 4:00 PM and in the evening around sunset, from 4:00 PM to 8:00 PM, but severer incidences are reported during night hours and during weekends