Combination of expert decision and learned based Bayesian Networks for multi-scale mechanical analysis of timber elements

The use of Bayesian Networks allows to organize and correlate information gathered from different sources and its optimization may incorporate restrictions adjusting the network based on expert knowledge and network operativeness, in such a way that it may satisfactorily represent a given domain. The main goal of this paper is to study if an optimized learned Bayesian Network may be used as a prior structure for an expert based network of an engineering structural material analysis. The methodology is applied to a database of results from an experimental campaign that focused on the mechanical characterization of timber elements recovered from an early 20th century building. To that study case it is evidenced that through a suitable combination of model averaging and supervision steps it is possible to achieve robust and reliable models to underpin the causal structure of a typical multi-scale timber analysis.