Snow-Induced Building Failures

Geis, J; Strobel, Kristen; Liel, Abbie B.

doi:10.1061/(asce)cf.1943-5509.0000222

Cited by 42 publications

(13 citation statements)

References 2 publications

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“…Extreme snow depths further may have consequences on tourism industry (Koenig & Abegg, 1997) and on mountain ecosystems (Keller et al, 2005;Wipf et al, 2009). Moreover, they also may cause collapse of buildings (Geis et al, 2011;Taylor, 1979) and extreme snow depth values are relevant for avalanche prevention (Schweizer et al, 2009). Hence, modeling extreme snow depths as precisely as possible is important for various environmental issues.…”

Section: Introductionmentioning

confidence: 99%

Assessing Climate Change Impact on the Spatial Dependence of Extreme Snow Depth Maxima in the French Alps

Nicolet

Eckert

Morin

et al. 2018

Water Resources Research

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Modeling extreme snow depths in space is important for water storage, tourism industry, mountain ecosystems, collapse of buildings, and avalanche prevention. However, studies modeling the spatial dependence structure of extremes generally assume temporal stationarity which is clearly questionable in a climate change context. We model climatic trends within the spatial dependence structure of extremes, with application to a data set of snow depth winter maxima. From 82 stations spanning the 1970–2012 period in the French Alps, we infer a strong decrease in the range of spatial extremal dependence. This finding is related to a strong decrease in both the snow precipitation ratio and the winter cumulated snowfall, due to increasing temperatures. Hence, we demonstrate that the spatial dependence of extreme snow depths is impacted by climate change in a similar way as has been observed for extreme snowfalls. Furthermore, snow depths maxima are more spatially dependent than snowfalls. The space‐time approach that we introduce may be very useful for assessing past and future evolutions under ongoing climate change in various hydrological quantities.

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Section: Introductionmentioning

confidence: 99%

Assessing Climate Change Impact on the Spatial Dependence of Extreme Snow Depth Maxima in the French Alps

Nicolet

Eckert

Morin

et al. 2018

Water Resources Research

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“…Snow is an important climatic action, which governs the reliability of many structures, particularly lightweight steel structures [1]. These should operate without major structural maintenance for typically 50 years, and their real working life generally spans over 100 years.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Trends in Annual Ground Snow Maxima for the Carpathian Region

Rózsás

Sýkora

Vigh

2016

AMM

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Abstract. The current structural design provisions are prevalently based on experience and on the assumption of stationary meteorological conditions. However, the observations of past decades and advanced climate models show that this assumption is debatable. Therefore, this paper examines the historical long-term trends in ground snow load maxima, and their effect on structural reliability. For this purpose, the Carpathian region is selected, and data from a joint research effort of nine countries of the region are used. Annual maxima snow water equivalents are taken, and univariate generalized extreme value distribution is adopted as a probabilistic model. Stationary and five nonstationary distributions are fitted to the observations utilizing the maximum likelihood method. Statistical and information theory based approaches are used to compare the models and to identify trends. Additionally, reliability analyses are performed on a simple structure to explore the practical significance of the trends. The calculations show decreasing trends in annual maxima for most of the region. Although statistically significant changes are detected at many locations, the practical significance -with respect to structural reliability -is considerable only for a few, and the effect is favourable. The results indicate that contrary to the widespread practice in extreme event modelling, the exclusive use of statistical techniques on the analysed extremes is insufficient to identify practically significant trends. This should be demonstrated using practically relevant examples, e.g. reliability of structures.

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“…Another well-known case of a disaster caused by the continuing building collapse is the Hartford Civic Center Coliseum [18] (Fig 3), which happened mainly because of errors [12] in calculating the innovative design of a roof covering as a system of trusses arranged in two directions every 2745 mm with spatial pyramidal elements. The collapse of the roof covering in the form of the steel double-layer grate took place because of the faulty design consisting in the underestimation of the weight of the roofing [20] and, above all, in the fact that the main top chord had very understated bearing capacity of the buckling compared to that which was adopted in the project.…”

Section: Source: Own Elaborationmentioning

confidence: 99%