Addis Ababa Institute of Technology

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Browsing Addis Ababa Institute of Technology by Subject "2D"

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    am Breach Modelling and Flood mapping, a Case Study of Ribb Dam
    (Addis Ababa University, 2021-01) Fasika, Worku; Daneal, Fikreselassie (PhD0
    The spontaneous dam breach phenomenon and the resultant flooding that happened in the world history leads to the requirement of establishing a dam safety plans and hazard management strategies. In this regard, the dam breach pre-event analysis will be the prerequisite work. This thesis addressed a pre-event analysis of a dam breach scenario for Ribb dam located in Amhara regional state, Ethiopia. Deterministic and probabilistic approach for the modelling of the dam breach is used. Overtopping and piping failure modes are assessed and the resulting flood inundation is mapped. A 1.5 PMF inflow hydrograph and a base inflow hydrograph are used as upstream boundary condition, while Lake Tana makes the downstream boundary condition. Fourteen 2D simulations are carried out and of which ten are for different breach parameters, two are for uncertainty analysis on breach parameters, and two are sensitivity analysis on Manning’s roughness coefficient ‘n’. HEC-RAS Ver 5.0.7 hydraulic model is employed, and McBreach is used for probabilistic dam breach modelling. In this study, five deterministic non-physical empirical methods and probabilistic breach modelling are assessed and compared. The five deterministic non-physical empirical methods have resulted in peak flow values between 67,570m 3 /s and 113,153m 3 /s for overtopping and between 22,269m 3 /s and 40,926m 3 /s for piping modes of failure respectively. For both modes of failure, MacDonald and langridge-Monopolis and Frohelich (1995a) produced the lowest and highest peak discharge respectively. The 1% and 90% exceedance probability peak discharge for overtopping failure mode is 104,379m 3 /s and 77,521m 3 /s respectively. The Manning roughness coefficient ‘n’ sensitivity analysis showed a 0.11 to 39.9 percentage increase in flood depth and 2.20 to 20.67 percentage decrease in velocity for an increase of the Manning roughness coefficient by 30%. In addition, the Manning roughness coefficient ‘n’ sensitivity analysis showed a 0.00 to 15.32 percentage decrease in flood depth and 10.81 to 28.09 percentage increase in velocity for a decrease of the Manning roughness coefficient by 30%. The study highlighted the dam breach and its corresponding flooding could be potentially catastrophic and high priority should be given to monitoring and surveillance of the dam.
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    Resource Allocation Model for IP-Backhaul Maintenance using Risk Analysis and Convolutional Neural Network
    (Addis Ababa University, 2021-11) Tamrat, Demena; Rosa, Tsegaye (PhD)
    resources is vital. Over- or under-used maintenance resources such as automotive, tool, technician, and others are the main causes of extended maintenance times and dissatisfied customers. Ethiotelecom uses IP backhaul transport network technology mainly using IP-MPLS enabled routers and power systems to deliver Fourth Generation (4G) cellular and Fixed Mobile Convergence (FMC) services. Due to a lack of a failure risk analysis as a metric in the maintenance of the IP backhaul and unavailability of the services, Ethiotelecom loses 2.073 billion ETB every month. In this study, to assist the decision-making framework in daily resource sharing between IP and Power maintenance teams optimally at Ethiotelecom, we developed a knowledge-based optimal failure mode classifier model by fusing IP backhaul network alarm and Key Performance Indicators (KPIs) data obtained from Network Management Systems (NMS) at the network (topology) and feature level by using a design matrix and sporadic data aggregation process (DAP). To create a labeled design matrix based on a rational decision, we use weak-supervision data programming based on user-defined criteria functions obtained from reliability analysis using Failure Mode and Effect Analysis (FMEA) and the Decision-Making and Trial Evaluation Laboratory (DEMATEL) on the network failure history data with the help of experts from the two sections. In addition, we adopted a Windows slicing method in the time domain to obtain daily sliced grayscale images of the two failure modes on Python. We trained the two-dimensional convolutional neural networks (2D-CNN) to classify the images using grid search and k-fold cross-validation and tested the model using F1-Score and ROC-AUC. We have a 97 percent accurate baseline model. Besides, we test the model separately by selecting the alarm and KPI frames to capture the confidence of the model and to avoid uncertainty in the decision-making framework in IP-backhaul maintenance resource allocation. We then have a 98 percent and a 46 percent fair model, respectively. The study then concludes with some validation challenges and pitfalls