Civil Engineering
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14540/105
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Item Experimental testing and characterization of bambusa balcooa and bambusa nutans for analysis and design of bamboo structures(I.O.E, 2023-12) Poudel, Sarowar; Parajuli, Hari RamItem Numerical Study of Bearing Capacity under Strip footing having underground void : A Case of Lamachaur Pokhara(I.O.E, 2023-12) Nepal, Bibek; Yadav, Santosh KumarItem Prediction of Optimum Bitumen Content in Asphalt Mix Design Using Artificial Neural Network(I.O.E, 2024-07) Giri, Moti Ram; Tamrakar, Gautam Bir SinghItem Post Encroachment Time-Based Behavioral Analysis of Vehicle-Pedestrian Interactions at Unsignalized Midblock Crosswalks: A Case Study of Shantinagar and Dhobighat Crosswalks in Kathmandu Valley(I.O.E, 2025-04) Lamsal, Sandesh; Pradhananga, RojeeItem Evaluating Public Transport Accessibility for Work, Education and Health Trips: A Case Study of Kathmandu Valley(I.O.E, 2025-04) Pandey, Sajan; Pradhananga, RojeeItem Bicyclist’s Perception based Level of Service in Heterogeneous Traffic Condition: A Case Study of Bharatpur, Nepal.(I.O.E, 2025-04) Dhital, Rabin; Marsani, AnilItem Value of Risk Reduction of Fatal Road Crashes: A Case Study of Long- and Medium-Route Public Vehicle Passengers Traveling To and From Kathmandu(I.O.E, 2025-04) Sukubhattu, Puspa; pradhananga, RojeeItem Optimizing Charging Station Locations for Public Transport Route Coverage in Kathmandu Valley(I.O.E, 2025-04) Khatiwada, Purushartha; pradhananga, RojeeItem Calibration of VISSIM Social Force Model Parameters: Case Studies on Signalised Pedestrian Crossings at Min Bhawan and Pulchowk(I.O.E, 2025-04) Shrestha, Pragyan; Shrestha, Pradeep KumarItem An Assessment of Utilization, Compliance and User Awareness of Pelican Crossings: A Case Study of Midblock Crossings at the Gwarko-Lamatar Road(I.O.E, 2025-04) Shrestha, Merina; Marsani, AnilItem 3D Stress-Strain Analyses of Geogrid-Reinforced Pavements under Vehicle Load Configurations and Dynamics(I.O.E, 2025-04) Tiwari, Aanchal; Shahi, Padma BahadurItem Spatio-Temporal Analysis of Road Traffic Crash Hotspots in Kathmandu Valley, Nepal(I.O.E, 2024-07) K.C., Anuradha; Pradhananga, RojeeItem Assessing Service Quality of Ride Hailing Bike Service within Kathmandu Valley(I.O.E, 2024-07) Chaudhary, Ambika; Shrestha, Pradeep KumarItem Performance Evaluation of Ekantakuna Intersection(I.O.E, 2025-04) Kafle, Shesh Raj; Shrestha, Pradeep KumarItem Post-Disaster Analysis of Road User Cost – A Case Study of BP Highway(NH13)(I.O.E, 2025-04) Khanal, Shankar; Marsani, AnilItem Analysis of Road Traffic Crash Cost Using the Human Capital Approach in Kailali District(I.O.E, 2025-04) Dhami, Maheshwari; Shrestha, Pradeep KumarItem Analyzing the Vehicles’ Platooning and Evaluation of Operational Performance Measures of Two-Lane Intercity Highway in Nepal: A Case Study of the Muglin- Narayanghat Section (NH44-004)(I.O.E, 2025-04) Dhakal, Kshitiz; Marsani, AnilItem Modelling Pedestrian-Vehicle Conflict and Severity at Uncontrolled Midblock Crossings Inside Kathmandu Valley(I.O.E, 2025-04) Banstola, Ashish; Shrestha, Pradeep KumarItem Correlation Between CBR Value and Plasticity Index of Base Course Material in Flexible Pavement(I.O.E, 2024-07) Shah, Saroj Prasad; Tamrakar, Gautam Bir SinghThe California Bearing Ratio (CBR) serves as a crucial indicator of base course strength, informing the thickness design of the pavement. It signifies the material's capacity to withstand loads and resist deformation, with higher values suggesting stronger base course materials capable of bearing heavier loads with minimal deformation. Plasticity Index (PI) is another pivotal parameter guiding engineers in assessing the suitability of the base course for pavement design. A larger PI implies a higher clay content, potentially compromising the base course's strength and stability. This prompts researchers to explore the potential relationship between CBR and PI for base course. Twenty-one samples of base course were gathered from Lalitpur, Makwanpur, and Dhading, ensuring compliance with the Standard Specifications for Road and Bridge Works, 2016 (SSRBW). In assessing CBR, Optimum Moisture Content (OMC) and Maximum Dry Density (MDD) are also considered. Thus, OMC, MDD, and PI are treated as independent variables to formulate an expression for estimating CBR. Utilizing Multiple Linear Regression in Excel, a predictive model for CBR was established, demonstrating a strong correlation between predicted and observed CBR, with an R2-value of 0.81. This model streamlines testing procedures by facilitating the determination of CBR values for base courses without extensive testing. Laboratory tests indicate that even with a PI exceeding 6 (e.g., PI = 8), the CBR value remains above 80%.