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Materials 2019, 12(8), 1243; https://doi.org/10.3390/ma12081243

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

1
Department of Structural Engineering, Tongji University, Shanghai 200092, China
2
Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education, Tongji University, Shanghai 200092, China
3
Beijing Deep Singularity Technology Co., Ltd., Beijing 100086, China
4
Department of Control Science & Engineering, Tongji University, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Received: 26 January 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 16 April 2019
(This article belongs to the Section Construction and Building Materials)
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Abstract

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. View Full-Text
Keywords: timber structures; damage accumulation model; machine learning; multi-objective optimization; long-term experiment timber structures; damage accumulation model; machine learning; multi-objective optimization; long-term experiment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, Z.; Tao, D.; Li, M.; Shu, Z.; Jing, S.; He, M.; Qi, P. Prediction of Damage Accumulation Effect of Wood Structural Members under Long-Term Service: A Machine Learning Approach. Materials 2019, 12, 1243.

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