Quantifying Uncertainty in Lumber Grading and Strength Prediction: A Bayesian Approach

This article presents a joint distribution for the strength of a randomly selected piece of structural lumber and its observable characteristics. In the process of lumber strength testing, these characteristics are ascertained under strict grading protocols, as they have the potential to be strength reducing. However, for practical reasons, only a few such selected characteristics among the many present, are recorded. We present a data-generating mechanism that reflects the uncertainties resulting from the grading protocol. A Bayesian approach is then adopted for model fitting and construction of a predictive distribution for strength that accounts for the unrecorded characteristics. The method is validated on simulated examples, and then applied on a sample of specimens tested for bending and tensile strength. Use of the predictive distribution is demonstrated, and insights gained into the grading process are described. Details of the lumber testing experiments can be found in the online supplementary materials.