Recent amendments to design ground snow load requirements in ASCE 7-22 have reduced the size of case study regions by 91% from what they were in ASCE 7-16, primarily in western states. This reduction is made possible through the development of highly accurate regional generalized additive regression models (RGAMs), stitched together with a novel smoothing scheme implemented in the R software package remap, to produce the continental- scale maps of reliability-targeted design ground snow loads available in ASCE 7-22. This approach allows for better characterizations of the changing relationship between temperature, elevation, and ground snow loads across the Conterminous United States. RGAMs are shown to have 10% or better improvement in mean absolute mapping error in two independently created datasets when compared to traditional mapping techniques. Potential implications and limitations of incorporating mapping accuracy into the reliability-targeted load calculation are demonstrated and discussed.
.Rdata, .gz, .csv, .R
To run the example, download all files into the same folder and set your working directory in R to be the same as the location of these files. Note that you may need to update other R packages in order to install the tar.gz packages described previously.
American Society of Civil Engineers, Structural Engineering Institute
Utah State University
American Society of Civil Engineers, Structural Engineering Institute 202827
This folder contains a dataset, a shapefile, two R packages, and script that reproduces estimates of cross-validated accuracy in the companion manuscript. The associated data represents a consolidation of information from the following sources.
- The National Oceanic and Atmospheric Administration (NOAA) Global Historical Climatological Network - Daily (GHCND) (http://doi.org/10.7289/V5D21VHZ) Version: August 25th, 2020.
- Gridded climate normals from the PRISM Climate Group (https://prism.oregonstate.edu/normals/)
- US EPA's Environmental Ecoregions (https://www.epa.gov/eco-research/ecoregions-north-america)
Data processing and models were created using R Statistical Software with the help of the tidyverse, sf, mgcv, automap, gstat, snowload, and remap packages.
Wagstaff, J., Bean, B., Wheeler, J., Maguire, M., & Sun, Y. (in press). Regionalized Mapping of Reliability-Targeted Design Snow Loads in the United States.
Bean, B., Maguire, M., Sun, Y., Wagstaff, J., Al-Rubaye, S. A., Wheeler, J., Jarman, S., & Rogers, M. (2021). The 2020 National Snow Load Study. Mathematics and Statistics Faculty Publications. Paper 276. https://digitalcommons.usu.edu/mathsci_facpub/276.
Mathematics | Statistics and Probability
This work is licensed under a Creative Commons Attribution 4.0 License.
Wagstaff, J., Wheeler, J., Bean, B., Maguire, M., & Sun, Y. (2023). Supplementary Files for "Adaptive Mapping of Design Ground Snow Loads in the Conterminous United States" [Data set]. Utah State University. https://doi.org/10.26078/PRKK-A860
Additional Filesbean_2023_readme.txt (5 kB)
rtsl_map_data.zip (2414 kB)
eco3_simp.Rdata (225 kB)
remap_0.3.0.tar.gz (583 kB)
rtsl.csv (670 kB)
rtsl_cross_validation.R (9 kB)
snowload_18.104.22.1681.tar.gz (1285 kB)