Date of Award:

12-2024

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Mathematics and Statistics

Committee Chair(s)

Brennan L. Bean

Committee

Brennan L. Bean

Committee

Yan Sun

Committee

John R. Stevens

Committee

Jürgen Symanzik

Committee

Yoshimitsu Chikamoto

Committee

Marc Maguire

Abstract

In the United States, accommodating the weight of accumulated snow on buildings is a crucial consideration in building design. Engineers are tasked with determining the design snow load, which is defined as the weight of accumulated snow that a structure should withstand to limit the risk of building collapse to an acceptably low level. Typically, this process involves analyzing historical data of the annual maximum snow accumulations for each snow season. However, accurately assessing these design snow loads entails navigating through a series of statistical challenges. This dissertation, composed of three papers, is dedicated to addressing these statistical hurdles in the estimation of design snow loads. The first paper compares various methods for calculating design snow loads, focusing particularly on short-term snow accumulation events. This is highly relevant for structures like solar panels, which melt snow faster than the roofs of buildings. The second paper addresses the issue of underestimated design snow loads for buildings. This underestimation is a result of employing imputed snow data from statistical models in place of missing snow data. In the third paper, we take a forward-looking approach by accounting for the effect of climate change on snow load design. Taken together, these three papers outline improved methods for characterizing extreme snow accumulations and handling the statistical challenges associated with several steps in the design snow load estimation process.

Checksum

38ca4384de440b034be3a28b42dd886e

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