Statistical correlation investigation of a single-doweled timber-to-timber joint

• Autor(a) principal: Aquino, Caroline D.
• Data de Publicação: 2022
• Outros Autores: Rodrigues, Leonardo Filipe Guilherme, Branco, Jorge M., Gomes, Wellison J. S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/88640
• topic: Timber joints; Uncertainty quantification; Joint behavior; Copula theory; Reliability analysis; Engenharia e Tecnologia::Engenharia Civil; Science & Technology

Dowel-type joints are widely used in timber structures given their ease of construction, strength, and capacity to deform before failure. The embedment strength of timber and the bending moment capacity of dowels are considered key properties in the design. On the other hand, these properties have an inherent variability that increases the uncertainties related to the connection's strength and associated failure modes. This study proposes to quantify the uncertainty related to the statistical correlation behavior between the timber embedment strength and dowel bending moment capacity while comparing analytical solutions to the results of double shear single doweled timber joints. Traditional distribution fitting procedures, as well as copula functions, are implemented to capture their marginal and dependence behavior. Since their source of mutual correlation is known, the effectiveness of the different approaches in describing the statistical dependence structure can be assessed. This is done by investigating how equivalent are the descriptions of dependence by copula functions and directly from the correlation origin. Results obtained here indicate that, for single dowel-type connections in double shear, the impact of the copulas on the results is small, which means that improving their joint characterization represents a minor improvement in the reliability results. Besides the minor differences, the results show that copula functions are a viable tool capable of capturing the nuances of the joint behavior between random variables.