Multimodel Bayesian Analysis of Load Duration Effects in Lumber Reliability

TL;DR

This paper introduces a Bayesian model-averaging approach to evaluate lumber reliability considering load duration effects, integrating multiple models and load profiles for more comprehensive risk assessment.

Contribution

It proposes a novel Bayesian model-averaging method to combine different load duration models, improving reliability estimates under various load conditions.

Findings

Bayesian model-averaging effectively combines multiple load models.

Reliability estimates include 95% confidence intervals.

Application to Hemlock data demonstrates method's practicality.

Abstract

This paper evaluates the reliability of lumber, accounting for the duration-of-load (DOL) effect under different load profiles based on a multimodel Bayesian approach. Three individual DOL models previously used for reliability assessment are considered: the US model, the Canadian model, and the Gamma process model. Procedures for stochastic generation of residential, snow, and wind loads are also described. We propose Bayesian model-averaging (BMA) as a method for combining the reliability estimates of individual models under a given load profile that coherently accounts for statistical uncertainty in the choice of model and parameter values. The method is applied to the analysis of a Hemlock experimental dataset, where the BMA results are illustrated via estimated reliability indices together with 95% interval bands.