Search

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.

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.

The United States has a rich history of snow load studies at the state and national level. The current ASCE 7 snow loads are based on studies performed at the Cold Regions Research and Engineering Laboratory (CRREL) ca. 1980 and updated ca. 1993. The map includes large regions where a site-specific case study is required to establish the load. Many state reports attempt to address the "case-study regions" designated in the current ASCE 7 design snow load requirements. The independently developed state-specific requirements vary in approach, which can lead to discrepancies in requirements at state boundaries. In addition, there has been great interest to develop site-specific reliability-targeted loads that replace the current load and importance factors applied to 50-year snow load events as defined in ASCE 7-16. This interest stems from the fact that the relative variability in extreme snow load events is not constant across the country, leading to a non-constant probability of failure for a given design scenario. This report describes the creation of a modern, universal, and reproducible approach for estimating reliability-targeted design ground snow loads for the conterminous United States. This new approach significantly reduces the size of case-study regions as currently designated in ASCE 7-16 and resolves discrepancies in design snow load requirements that currently exist along western state boundaries.

The changes to the ASCE 7 ground snow load maps proposed for the 2022 edition target a uniform reliability rather than a uniform hazard – an important distinction – and are the first of their kind in ASCE 7. Previously, the ASCE 7 snow loads used a uniform-hazard 50-year mean recurrence interval (MRI) with a 1.6 load factor. The newly proposed loads directly target the safety levels stipulated in Chapter 1 of ASCE 7, resulting in a strength design level load that is to be used with a load factor of 1.0. This paper describes changes in design provisions that result from this transition to reliability-targeted loads and provides reasons for some of these differences.

One critical consideration in the design of buildings constructed in the western United States is the weight of settled snow on the roof of the structure. Engineers are tasked with selecting a design snow load that ensures that the building is safe and reliable, without making the construction overly expensive. Western states use historical snow records at weather stations scattered throughout the region to estimate appropriate design snow loads. Various mapping techniques are then used to predict design snow loads between the weather stations. Each state uses different mapping techniques to create their snow load requirements, yet these different techniques have never been compared. In addition, none of the current mapping techniques can account for the uncertainty in the design snow load estimates. We address both issues by formally comparing the existing mapping techniques, as well as creating a new mapping technique that allows the estimated design snow loads to be represented as an interval of values, rather than a single value. In the process, we have improved upon existing methods for creating design snow load requirements and have produced a new tool capable of handling uncertain climate data.

Proper consideration of snow loads in building design can be a delicate balancing act: Underestimates lead to structure failure Overestimates lead to increased construction costs

Recent reports of snow related building collapses across the western United States in early 2017 highlight the importance of snow load considerations in building design. Specifying such design snow loads requires a proper balance between safety and economy. As articulated by Nowak and Collins (2012): "Conceptually, we can design [a] structure to reduce the probability of failure, but increasing the safety...beyond a certain optimum level is not always economical." It is the search for this optimum level of safety that has motivated the development of design ground snow loads at state and local levels.