Browsing by Author "Morris Oleng"

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    Fault-Oriented Spatially Distributed Seismicity Model and Coseismic Landslide Hazard Assessment Framework for Uganda
    (SECED, 2023-09-15) Morris Oleng; Zuhal Ozdemir; Kypros Pilakoutas
    Uganda’s location between the western and eastern branches of the East African Rift System exposes several parts of the Sub-Saharan African country, which already suffers from landslides, to earthquakes of varying degrees. Over the past decades, many destructive seismic events e.g., the 1929 Masaka, 1966 Toro, 1994 Kisomoro and 2016 Bukoba earthquakes have caused enormous human and economic losses. Moreover, the catastrophic impacts of these events have been exacerbated by the triggered landslides around earthquake epicentres. The proliferation of a substandard building stock caused by lack of building control and obsolete seismic design guidelines coupled with rapid population growth and urbanisation exposes Uganda to a massively increasing risk from such disasters. Hence, there is an urgent need to quantify this risk so that mitigation measures can be applied. In retrospect, as the first step towards the development of a seismic risk and resilience assessment framework for Uganda, this paper holistically presents a stochastic probabilistic seismic multi-hazard model developed based on fault-oriented and spatially distributed seismicity data for Uganda. Suitable attenuation relationships are implemented using a logic tree approach to predict ground motion in both stable continental and active shallow crust geological formations. Herein, the multi-hazard assessment tool incorporates coseismic landslides into the framework for Uganda. Mean seismic hazard maps in terms of PGA are computed for 475 and 2475-year return periods, in addition to a probabilistic assessment of coseismic landslides based on various conditioning factors. The findings, which are generally consistent with previous regional studies, indicate that western Uganda is prone to higher seismicity and coseismic landslides compared with the other parts of the country. Correspondingly, the framework presented herein can be used to kick-start the update and continuous improvement of Uganda Seismic Design Code, as well as help develop a National Seismic Mitigation Strategy.
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    Probabilistic Seismic Hazard Assessment Framework for Uganda: A Stochastic Event-Based Modelling Approach
    (Springer, 2024-01-23) Morris Oleng; Zuhal Ozdemir; Kypros Pilakoutas
    Uganda lies between the eastern and western arms of the East African Rift System, the largest seismically active rift above sea level. With increasing population, urbanisation and rapid construction, seismic risk in the country is escalating fast and is compounded by the high vulnerability of the building stock and inadequate disaster prevention and mitiga- tion strategies. Hence, there is an urgent need to assess Uganda’s resilience against seis- mic risks. This paper presents a Monte-Carlo based probabilistic seismic hazard model for Uganda, as the first step towards the development of a seismic risk and resilience assess- ment framework for the country. In addition to fault segment data, earthquake catalogues are compiled for the period between 1900 and 2022 to estimate recurrence parameters for source zones in the area of interest. Area source zones incorporating focal mechanisms are used to stochastically model a national hazard framework for Uganda. A logic tree approach is applied to implement four ground motion prediction equations for both stable continental and active shallow crust geologies. Mean hazard curves, uniform hazard spec- tra, earthquake disaggregation and spectral pseudo-accelerations for major Ugandan cities are derived in addition to hazard maps for the country. The findings are largely consistent with previous regional studies and confirm that western Uganda is exposed to the highest level of seismicity. The model presented herein can be used to kick-start the update and continuous improvement of Uganda Seismic Design Code and the National Policy for Dis- aster Preparedness and Management.