Bayesian networks for assessment of disruption to school systems under combined hazards - BRGM - Bureau de recherches géologiques et minières Access content directly
Journal Articles International Journal of Disaster Risk Reduction Year : 2022

Bayesian networks for assessment of disruption to school systems under combined hazards


Exposure of school buildings to floods and earthquakes poses significant risk to the vulnerable population of students and their education process. In regions of high exposure, these hazards may often act concurrently, whereby yearly flood events weaken masonry school buildings, rendering them more vulnerable to frequent earthquake shaking. This recurring damage, combined with other functional losses, ultimately result in disruption to education delivery. The socioeconomic condition of the users-community also plays a role in the extent of such disruption. A complex problem of this nature demands consideration of a large number of dimensions, to estimate the impact to the school system infrastructure in a locality. To handle the qualitative and quantitative nature of these variables, a Bayesian network (BN) model is proposed representing multiple schools in a locality as a system. Three dimensions are considered to contribute to the system disruption, namely, schools' physical functionality loss, accessibility and use loss, and social vulnerability. The impact is quantified through the probability of the system being in various states of disruption. The BN also explores mitigating measures, such as the mobility of students between schools in the system. The general methodology is illustrated by a case-study of school buildings in Guwahati, India, whereby the majority of buildings is constructed in confined masonry with varying level of seismic performance. The physical effects of combined flood and seismic action on confined masonry buildings is assessed by nonlinear numerical modelling, and their probabilistic occurrence is expressed in terms of fragility functions corresponding to varying flood depth and peak ground acceleration.


Earth Sciences
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hal-03669417 , version 1 (16-05-2022)



Ahsana Parammal Vatteri, Dina d'Ayala, Pierre Gehl. Bayesian networks for assessment of disruption to school systems under combined hazards. International Journal of Disaster Risk Reduction, 2022, 74, pp.102924. ⟨10.1016/j.ijdrr.2022.102924⟩. ⟨hal-03669417⟩


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