Stochastic Modeling of Uncertainties in the Wind Load Pressure on Residential Buildings Using Different Stochastic Techniques
Received Date: August 21, 2020; Published Date: September 10, 2020
There are different methods to quantify the amount of uncertainties in the analysis of structures and their design procedures. The efficiency and the suitability of each method depend on the type of analysis and the load scenario that will be used for this analysis. In this paper, different uncertainty propagation methods are used to quantify the amount of uncertainties in the wind load analysis procedures to quantify the load impact on the gable roofs of residential buildings. Therefore, Direct Monte Carlo Simulation, Importance Sampling, First Order Reliability Method, and Taylor Approximation are used to further investigate the impact of each method on the uncertainty quantification. The selected example structure to perform the analysis is one of the different archetypes that has been used for the tornado analysis in the literature. The different wind load demand parameters and components statistics are used to conduct the different stochastic analyses. The analysis results showed that MCS is the most efficient method with the least coefficient of variation in the simulated wind pressure and the calculated failure probabilities associated with each stochastic model.
Keywords: Wind load analysis; Direct monte carlo simulation (MCS); Markov chain monte carlo (MCMC); First order reliability method (FORM); Subset analysis; Importance sampling