Bayesian Analysis of Accumulated Damage Models in Lumber Reliability. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Yang, Chun-Hao; Zidek, James V.; Wong, Samuel W. K.
• Is Part Of: Technometrics. Volume 61:Issue 2(2019)
• Page Start: 233
• Page End: 245
• Publication Date: 2019-04-03
• Subjects: Approximate Bayesian computation -- Duration of load -- Failure time distribution; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2018.1512900
• Languages: English
• ISSNs: 0040-1706

Abstract

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. Code is available at https://github.com/wongswk/abc-adm .