Description
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.
Document Type
Dataset
DCMI Type
Dataset
File Format
.Rdata, .gz, .csv, .R
Viewing Instructions
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.
Publication Date
1-12-2023
Funder
American Society of Civil Engineers, Structural Engineering Institute
Publisher
Utah State University
Award Number
American Society of Civil Engineers, Structural Engineering Institute 202827
Methodology
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.
Referenced by
Wagstaff, J., Bean, B., Wheeler, J., Maguire, M., & Sun, Y. (2024). Adaptive Mapping of Design Ground Snow Loads in the Conterminous United States. Journal of Structural Engineering, 150(1), 04023193. https://doi.org/10.1061/JSENDH.STENG-12396
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.
Language
eng
Code Lists
See README
Disciplines
Mathematics | Statistics and Probability
License
This work is licensed under a Creative Commons Attribution 4.0 License.
Identifier
https://doi.org/10.26078/prkk-a860
Recommended Citation
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
Checksum
b6911790c0544f4a66fbdfb0fe0ec1b4
Additional Files
bean_2023_readme.txt (5 kB)MD5: 528610921fad22fbf40fbdf09cb604cf
rtsl_map_data.zip (2414 kB)
MD5: b43797b5111d136c743d82c94f52a5e1
eco3_simp.Rdata (225 kB)
MD5: 5bb41fcbd4714c06eded18c73f7d1baf
remap_0.3.0.tar.gz (583 kB)
MD5: 705d4261a005d8155bacad09aeeba708
rtsl.csv (670 kB)
MD5: e7cb546a391afd87452f16fd87702247
rtsl_cross_validation.R (9 kB)
MD5: fcb8b69f8df5aa4780135c1d97375768
snowload_1.0.0.901.tar.gz (1285 kB)
MD5: 06a1f8be086c3864647737cbdb041585