Snow tends to slide from unobstructed slippery sloped roofs of sufficient steepness. This behavior can reduce the required design load and deposit additional loading on lower adjacent structures. Computer-based data acquisition systems have made it possible to obtain the necessary data to understand sliding snow. Microcomputers can collect data, transmit the information while unattended, and control laboratory equipment. Data from field sites, combined with data generated using artificial snow, comprised the database; thus, we expressed the conditional probability of sliding using temperature as the random variable. The probability of sliding on any given day is expressed using the equation of joint probability; with these probabilities and actual meteorological data, we simulated roof loads for each winter day over a period of 25 years. A log Pearson type III extreme value distribution yielded 50-year mean recurrence interval roof loads that correlated with measured values.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £78 +P&P)