Predicting Severe Snow Loads Using Spatial Extremes
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2019
Författare
Szolnoky, Vincent
Hansson, Peter
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Severe snow load, unlike severe snow fall, happens over a longer period of time for
which snow accumulates causing it to produce increasing amounts of down force on
the structure below it. When a structure is forced to bear a load it was not designed
for, structural wear and damage can take place and in the worst case scenario, total
failure in which the structure collapses.
To avoid such occurrences, extreme snow load should be modelled and the potential
risks identified so as to improve laws and regulation pertaining to the maximum
load a building must handle. This thesis makes use of spatial statistics and extreme
value theory to analyse weather data and create models that can aid in predicting
extreme snow depth which is directly linked to extreme snow load. Specifically
interpolation by Kriging and non-stationary GEV methods are used to obtain predictions
between stations. The results are compared to maps already published by
the Swedish Building and Housing authority, Boverket, and the discrepancies between
them show that certain regions in Sweden are currently being under-estimated.
This under-estimation can lead to buildings being constructed to withstand loads
less then what is predicted and therefore are at risk of structural fatigue. However
in areas of heavy snowfall where the danger is greater, the map by Boverket actually
overestimates predicted extreme values. Hence buildings constructed in areas where
the risk of very high snow loads is prevalent, should be well future-proofed, and be
able to withstand even more load that what is expected.
Beskrivning
Ämne/nyckelord
geostatistics, extreme value theory, monte carlo simulation, kriging, weather v