Machine Learning-Based Modeling for the Duration of Load Effect in Wood Structural Members Under Long-Term Sustained Load

• Published in: IEEE access Vol. 8; pp. 17903 - 17915
• Main Authors: Qi, Peng, Li, Mengwei, Zheng, Xiuzhi, Li, Zheng, Liu, Chuang, Zeng, Xin, Tao, Duo, Qi, Xiaoya, Ma, Zhong, He, Minjuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Complexity; Construction materials; Damage accumulation; damage accumulation model; duration of load; Goodness of fit; Load modeling; Loading; long-term experiment; Machine learning; Markov processes; Mathematical model; Mathematical models; Neural networks; Parameter estimation; Predictive models; Residual strength; Stochastic processes; Structural members; Timber structures
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2966883

The load resisting capacity of structural members will decrease when they are subjected to long-term sustained load. Such phenomenon is widely known as the duration of load effect, which is mainly caused by the damage accumulation in the material. The deterioration mechanism of the material is a typical stochastic process which is influenced by a large variety of parameters involving complex physical and chemical process. Although classical models have been proposed to evaluate the duration of load effect, it is nearly impossible to quantify the influence of various parameters and to achieve an accurate estimation. To optimize the combination of complexity and goodness-of-fit, a neural network model is proposed in this paper to evaluate the duration of load effect in wood structural members. Taking individual uncertainties into consideration, the proposed model treats the damage in wood as a Markov process and can estimate the residual strength distribution of the investigated wood structural members under long-term sustained load. The coefficient of determination reaches above 95% under sustained loading scenario, and it shows good adaptability across different wood properties. Moreover, the model can be adapted to continuously varied loading scenarios with a 98% coefficient of determination. This research aims to provide a useful and straightforward tool for accurately predicting the duration of load effect in wood structural members, and the proposed algorithm can be easily modified to deal with similar engineering problems for other construction materials.