Bayesian analysis of accumulated damage models in lumber reliability

TL;DR

This paper introduces a Bayesian framework using ABC techniques to analyze accumulated damage models for lumber reliability, improving parameter estimation and uncertainty quantification in long-term stress scenarios.

Contribution

It presents a novel Bayesian approach with ABC for damage models, enhancing analysis of lumber reliability under long-term stress.

Findings

Effective parameter estimation with ABC

Successful application to real and simulated data

Insights into long-term lumber reliability

Abstract

Wood products that are subjected to sustained stress over a period of long duration may weaken, and this effect must be considered in models for the long-term reliability of lumber. The damage accumulation approach has been widely used for this purpose to set engineering standards. In this article, we revisit an accumulated damage model and propose a Bayesian framework for analysis. For parameter estimation and uncertainty quantification, we adopt approximation Bayesian computation (ABC) techniques to handle the complexities of the model. We demonstrate the effectiveness of our approach using both simulated and real data, and apply our fitted model to analyze long-term lumber reliability under a stochastic live loading scenario.