As part of an ongoing effort to update the ground snow load maps in the United States, this paper presents an investigation into snow densities for the purpose of predicting ground snow loads for structural engineering design with ASCE 7. Despite their importance, direct measurements of snow load are sparse when compared to measurements of snow depth. As a result, it is often necessary to estimate snow load using snow depth and other readily accessible climate variables. Existing depth-to-load conversion methods, each of varying complexity, are well suited for snow load estimation for a particular region or station network, but none are consistently effective across regions and station networks. This paper proposes a random forests regression model for estimating annual maximum snow loads in the conterminous United States that makes use of climate reanalysis data and overcomes the limitations of existing methods. The effectiveness of the random forest model is demonstrated through accuracy comparisons of existing depth-to-load conversion techniques using a compilation of national and state-level data sources. The accuracy comparisons show that the random forest model is competitive for all regions and station networks, while other methods are competitive for only certain regions or station networks. These results highlight the feasibility of developing a single depth-to-load conversion method that appropriately characterizes region and climate specific differences in the snow depth/load relationship across the conterminous United States. Such universal models are an essential component for creating a unified set of national snow load requirements that eliminate the case study regions currently defined in ASCE 7.
Author ORCID Identifier
Jesse Wheeler https://orcid.org/0000-0003-3941-3884
Brennan Bean https://orcid.org/0000-0002-2853-0455
.csv, .r, .rds
The Random Forest described in Wheeler, et al. (in press), is provided in the file RF.RDS. Also included is an example R script entitled Rexample.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. Further instructions and comments are given as R comments in the previously mentioned script. Dataset can be downloaded as a complete .zip file, or as the individual files in their original formats.
American Society of Civil Engineers / Structural Engineering Institute
Utah State University
American Society of Civil Engineers / Structural Engineering Institute 202827
See ReadMe file.
Wheeler, J., Bean, B., & Maguire, M. (in press). Creating a Universal Depth-to-Load Conversion Technique for the Conterminous United States Using Random Forests. Journal of Cold Regions Engineering.
See ReadMe file.
Applied Mathematics | Applied Statistics
This work is licensed under a Creative Commons Attribution 4.0 License.
Wheeler, J., Bean, B., & Maguire, M. (2021). Supplementary files for "Creating a Universal Depth-To-Load Conversion Technique for the Conterminous United States using Random Forests" [Data set]. Utah State University. https://doi.org/10.26078/NKDT-T278
Additional Filesdata.csv (6881 kB)
README.txt (7 kB)
Rexample.R (2 kB)
RF.RDS (3139 kB)