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    Creating a Universal Depth-to-Load Conversion Technique for the Conterminous United States Using Random Forests

    Source: Journal of Cold Regions Engineering:;2022:;Volume ( 036 ):;issue: 001::page 04021019
    Author:
    Jesse Wheeler
    ,
    Brennan Bean
    ,
    Marc Maguire
    DOI: 10.1061/(ASCE)CR.1943-5495.0000270
    Publisher: ASCE
    Abstract: As part of an ongoing effort to update the ground snow load maps in the US, 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, compared with 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 is consistently effective across regions and station networks. In this paper, a random forest regression model is proposed for estimating annual maximum snow loads in the conterminous US 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, whereas other methods are competitive only for 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 US. 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 current national standards.
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      Creating a Universal Depth-to-Load Conversion Technique for the Conterminous United States Using Random Forests

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4283135
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    contributor authorJesse Wheeler
    contributor authorBrennan Bean
    contributor authorMarc Maguire
    date accessioned2022-05-07T20:58:13Z
    date available2022-05-07T20:58:13Z
    date issued2022-3-1
    identifier other(ASCE)CR.1943-5495.0000270.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283135
    description abstractAs part of an ongoing effort to update the ground snow load maps in the US, 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, compared with 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 is consistently effective across regions and station networks. In this paper, a random forest regression model is proposed for estimating annual maximum snow loads in the conterminous US 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, whereas other methods are competitive only for 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 US. 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 current national standards.
    publisherASCE
    titleCreating a Universal Depth-to-Load Conversion Technique for the Conterminous United States Using Random Forests
    typeJournal Paper
    journal volume36
    journal issue1
    journal titleJournal of Cold Regions Engineering
    identifier doi10.1061/(ASCE)CR.1943-5495.0000270
    journal fristpage04021019
    journal lastpage04021019-11
    page11
    treeJournal of Cold Regions Engineering:;2022:;Volume ( 036 ):;issue: 001
    contenttypeFulltext
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