Evolving Studies of Ground Snow Loads for Several Western U.S. States

Sack, Ronald L.; Nielsen, Richard J.; Godfrey, Bruce R.

doi:10.1061/(asce)st.1943-541x.0001679

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…A(u α ) . They "appear to mask out the effects of the environment on the snow-making mechanism" and "reduce the entire area to a common base elevation" (Sack et al 2016). NGSL have a long history of use in western state snow load studies, including the current snow load reports of Idaho, Montana and Washington (Sack et al 2016).…”

Section: Methodsmentioning

confidence: 99%

“…However, little is written regarding the accuracy of the methods used to predict design ground snow loads. While Sack (2015) and Sack et al (2016) discuss differences between state methodologies and acknowledge discrepancies in predictions along state boundaries, no formal comparison of design ground snow load prediction methods is found in the literature. A lack of accuracy comparisons makes it difficult to determine whether differences in design ground snow load requirements along state boundaries are caused by differences in methodology, data, or both.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Design Ground Snow Load Prediction in Utah and Idaho

2019

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Snow loads in the western United States are largely undefined due to complex geography and climates, leaving the individual states to publish detailed studies for their region, usually through the local Structural Engineers Association (SEAs). These associations are typically made up of engineers not formally trained to develop or evaluate spatial statistical methods for their regions and there is little guidance from ASCE 7. Furthermore, little has been written to compare the independently developed design ground snow load prediction methods used by various western states. This paper addresses this topic by comparing the accuracy of a variety of spatial methods for predicting 50 year (i.e. design) ground snow loads in Utah and Idaho. These methods include, among others, the current Utah snow load equations, Idaho's normalized ground snow loads based on inverse distance weighting, two forms of Kriging, and the authors' adaptation of PRISM. The accuracy of each method is evaluated by measuring the mean absolute error using ten fold cross validation on datasets obtained from Idaho's 2015 snow load report, Utah's 1992 snow load report, and a new Utah ground snow load dataset. These results show that regression-based Kriging and PRISM methods have the lowest cross validated errors across all three datasets. These results also show that normalized ground snow loads, which are a common way of accounting for elevation in traditional interpolation methods, do not fully account for the effect of elevation on ground snow

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing Design Ground Snow Load Prediction in Utah and Idaho

2019

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“…CRREL studies have encountered situations in Idaho where ground snow load guidance (Al Hatailah et al 2015) We agree with Sack (2015) that a coordinated research effort is needed to provide consistent, updated ground snow load design criteria for the United States with attention given to Rocky Mountain States. Sack et al (2017) offer guidelines for achieving this goal for Western States.…”

Section: Contextmentioning

confidence: 99%

“…In addition to those done by CRREL, ASCE references ground snow load studies and mapping done by structural engineering associations and others in those states. Sack (2015) and Sack et al (2017) discuss those studies.…”

Section: Mapped Valuesmentioning

confidence: 99%

Site-specific case studies for determining ground snow loads in the United States : case study spreadsheet

Buska¹,

Greatorex²,

Tobiasson³

2020

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The U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) has mapped ground snow loads for much of the United States. In some areas where extreme local variations preclude mapping on a national scale, instead of loads, "CS" is used to indicate that Case Studies are needed. This report and the accompanying spreadsheet, which contains the 15,104-station CRREL ground snow load database, provide the information needed to conduct Case Studies. When the latitude, longitude, and elevation of a site of interest are provided, the spreadsheet tabulates data available in the vicinity and generates plots that relate ground snow loads nearby to elevation. With this information, the ground snow load at the site of interest can be determined. This report uses 10 examples to illustrate the methodology and provides our answer and the comments we generate for each of these Case Studies and for 16 additional sites of interest, 8 of which have their answers "disguised" for practice purposes. CRREL has conducted over 1000 Case Studies upon request. Practicing structural engineers were involved in over 250 of them to verify that this methodology is ready to transfer to the design profession. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. The location data included herein has been obtained from information developed, produced, and maintained by others. The CRREL Case Study database and methodology used herein to determine ground snow loads meet the requirements of the current national design load standard, Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE 2017). Statutory requirements of an Authority Having Jurisdiction may necessitate use of different design loads.

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