English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

It is well known that wood structural members can stand a relatively heavy load in the short term but will gradually get weaker if the load is applied for a longer period. This phenomenon is caused by the damage accumulation effect in wood and should be appropriately considered during the design of timber structures. Although various formulation methods (also known as classical models) have been proposed to evaluate the damage accumulation effect in wood, the calibration of model parameters is very time-consuming. Our work proposes a novel method to deal with the damage accumulation effect in wood that involves the application of machine learning algorithms. The proposed algorithm considers a multi-objective optimization process with a combination of goodness-of-fit and complexity. Long-term experimental data of typical wood species are used for developing the machine learning based damage accumulation model. Compared with existing pre-formulated models, our model managed to reduce the complexity of the model structure and give sufficiently accurate and unbiased predictions. This study aims to provide a novel tool for evaluating the damage accumulation in wood structural members, and the proposed model can further support the life-cycle performance assessment of timber structures under long-term service scenarios.

Prediction of Damage Accumulation Effect of Wood Structural Members under Long-Term Service: A Machine Learning Approach