Notice bibliographique
Résumé
In February 2014, owing to heavy snowfall in the Kanto-Koshin region, several buildings were severely damaged. A major cause for the damage was the load on building surfaces caused by heavy snow followed by a surcharge load due to the ensuing rainfall. Countries such as the United States and Canada have established standards on snow loads, which also take into account the surcharge load due to rainfall. In contrast, Japan has not adopted such standards, and hence it is imperative to establish a method for calculating loads added by rainfall to snow loads. Therefore, in this study, experiments were conducted using artificial rainfall on roofs covered with snow to evaluate and propose a calculation method for loads added by rainfall. First, outdoor experiments as well as indoor experiments in a low-temperature experimental facility were conducted. Model roofs with different span lengths and gradients were built and loaded with natural and artificial snow. These model roofs were then treated with rainfall at constant intensity by using an artificial rainfall simulator. The increase in load was measured and several observations were made from the experiments. A smaller roof gradient and larger roof span resulted in a greater peak value of the load added by rainfall. For roofs of the same shape, the peak value of the load added by rainfall increased as the initial snow depth increased. Next, the relationship between surcharge loads due to rainfall and the span length and gradient of the roofs was quantified. The load added by the rainfall was treated as the approximate square root of the snow depth, and a coefficient a, which depends on the span length and gradient of the roof, was defined. However, valuable data were obtained from the experiments for only five types of roof geometries. To estimate the values of the coefficients a for other roof geometries, regression analysis between the coefficient a, roof gradient, and span length was conducted. Comparing the surcharge loads, which were calculated using the estimated coefficients a, with the loads calculated using the O'Rourke equation, a relatively good correspondence between both the results was confirmed. Snow loads that take rainfall into account were obtained for the given regions with corresponding values of the design snow depth. This was achieved by including the load due to rainfall, which is correlated to the snow depth, computed from the proposed formula and the estimated coefficients. In addition, methods that include coefficients reflecting the regional climatic conditions were proposed. One such method is based on the fact that winter rainfall is not always observed during the period when the deepest snow cover occurs. Extreme values of the snow depth were obtained from the meteorological data to derive the ratio of snow loads on rainy days against the maximum winter snow loads; the ratio was treated as the coefficient k1. Another method considers the regional meteorological cases wherein the surcharge load due to rainfall does not reach the peak value. The ratio of the surcharge load value due to such rainfalls against the surcharge load calculated by the proposed formula was treated as the coefficient k2. However, because the influence of coefficient k2 is relatively small, it can be considered as 1 in practice. Moreover, because the impact of loads added by rainfall is relatively small in heavy snowfall regions, the regions where it is necessary to consider the loads due to rainfall may be limited to general regions where the design snow depth is 1 m or less.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».