Supplementary files for "Using Digitized Building and Weather Records to Improve the Accuracy of Ground to Roof Snow Load Ratio Estimations"

Creators

Gideon Parry, Utah State UniversityFollow
Brennan Bean, Utah State UniversityFollow

Description

Reliability targeted snow loads (RTLs) measure the weight in accumulated snow (i.e. snow load) that a roof is required to support to ensure the probability of failure is suf- ficiently low. This calculation has historically relied upon a probability distribution that characterizes the ratio between the annual maximum ground snow load to the annual max- imum roof snow load, a quantity referred to as Gr. The best available data for estimating Gr comes from Canadian case studies from the 1950s and 1960s. However, much of the data was never digitized, with only approximations of data being made available in scanned versions of printed graphs. As a result, existing models for Gr are based upon limited information that often fails to account for the interaction between a structure’s geometry and the surrounding environment as it relates to roof snow retention. This thesis digitizes data from these Canadian case studies and develops new models of Gr that better account for the effects of building geometry and wind speeds on roof snow retention. Using the dig- itized Canadian data, these new models improve the prediction accuracy in Gr compared to previous modeling efforts. To apply models from Canadian data to use in the United States, gridded estimations of weather variables are used to model Gr in place of digitized data from the Canadian reports. These gridded estimations of weather data do not improve prediction accuracy like the models using the digitized data, suggesting that site-specific variations in wind and exposure effects not captured in gridded weather maps are necessary for accurately predicting Gr.

Author ORCID Identifier

Brennan Bean; https://orcid.org/0000-0002-2853-0455

Contributors

This folder contains a data folder, scripts to clean and the data, scripts to model the data, and scripts to validate and apply the models. The collected data come from the following sources. - Allen, C. & Peter, B. (1963). Snow loads on roofs 1962-63: Seventh progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20386563 - Allen, D. E. (1958). Snow loads on roofs 1956-57: A progress report. Technical report National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20338139 - CCCS (2023). Era5 hourly data on pressure levels from 1940 to present. https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5 -pressure-levels?tab=form - Faucher, Y. (1967). Snow loads on roofs 1964-65: Ninth progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https:// doi.org/10.4224/20386791 - Hebert, P. & Peter, B. (1963). Snow loads on roofs 1961-62: Sixth progress report with an appendex on roof to ground load ratios. Technical report, National Research Coun- cil, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/ 20386760 - Ho, M. & Lutes, D. A. (1968). Snow loads on roofs 1965-66: Tenth progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20386688 - Kennedy, I. & Lutes, D. (1968). Snow loads on roofs 1966-67: Eleventh progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20386579 - Scott, J. & Peter, B. (1961). Snow loads on roofs 1960-61: Fifth progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20338224 - Thorburn, H. & Peter, B. (1959). Snow loads on roofs, 1958-59. third progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20386753 - Watt, W. & Thorburn, H. J. (1960). Snow loads on roofs 1959-60: Fourth progress report. Technical report, National Research Council, Division of Building Research, Ottawa, Canada. https://doi.org/10.4224/20338150 Data processing and models were created using R Statistical Software with the help of the tidyverse package.

Document Type

Dataset

DCMI Type

Dataset

File Format

.csv, .Rproj, .md

Viewing Instructions

To reproduce results and figures found in the thesis, all that needs to be run is 1_data.R, 2_methods.R, 3_validation_methods.R and 4_rtl.R in that order. 0_data_variables.R and 5_reliability_targeted_loads.R are included to demonstrate how these actions were performed, and 0_data_variables.R is reproducible.

Publication Date

6-20-2024

Publisher

Utah State University

Referenced by

Parry, Gideon, "Using Digitized Building and Weather Records to Improve the Accuracy of Ground to Roof Snow Load Ratio Estimations" (2024). All Graduate Theses and Dissertations, Fall 2023 to Present. 306. https://doi.org/10.26076/1ced-6c74

Language

eng

Comments

1. Scripts This folder contains reproducible scrips to produce the figures and results from the thesis the files in order of usage include: Files: 0_data_variables.R: Start with this one. This files obtains the data created from digitizing the Canadian reports and aggregating them to one data file 1_data.R: This file takes the aggregated data along with metadata to create the final data used in modeling and show rrelevant figures. 2_methods.R: Creates the models used to predict GR. This file shows the process for selecting the final model, including figues and coefficient relevant for doing so. 3_Validation_methods.R Shows cross validation being used for models that were attempted in the Thesis. This uses a function to run 25 different partitions of buildings and observations 4_rtl.R This file uses RTL data and shows effects of different assumptions and different eco regions on RTLs.It shows sheltered vs non sheltered effects, and effects of different assumptions for wind and temperature. 5_reliability_targeted_loads.R: Not reproducible. Creates simulations of RTLs. This is not reproducible due to the lack of some r scripts and data files it calls.

2. data-raw This folder contains data and non-reproducible scripts used to create it. Some notable data files are 1. complete_data.csv: This is the data file that data_variables.R creates from the files in complete_data 2. gr_meta_ca_all.csv This file is the metadata this complete_data is joined with 3. updated_data.csv This is the final data used in modeling after running data.R The sub folders here are 1. Unused_previous_attempts: Previous attempts at R scripts that are no longer in use 2. data_fixing: Scripts used to correct errors in digitizing 3. data_validation: Scripts used to validated that data was entered correctly 4. era_maps: scrips used to aggreagate many ERA5 grids into one used in modeling

complete_data This folder contains all data obtained from digitized reports. It is recommended to avoid interacting with these data directly, but rather interact with the data summaries available in the R scripts contained in this repository.

Disciplines

Mathematics | Statistics and Probability

License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Bean, B., & Parry, G. (2024). Supplementary files for "Using Digitized Building and Weather Records to Improve the Accuracy of Ground to Roof Snow Load Ratio Estimations" [Data set]. Utah State University. https://doi.org/10.26078/F5QA-E607