Comparison Between Artificial Neural Networks and Response Surface Methodology to Predict the Bending Moment Capacity of Heat-treated Wood Dowel Joints

Sergiu Racasan, Bogdan Bedelean, Sergiu Georgescu, Anca Maria Varodi


The bending moment capacity of heat-treated wood dowel joints loaded in compression or tension was predicted via two artificial neural network (ANN) models. Additionally, a comparative study between similar models that were developed through response surface methodology (RSM) was performed. The joints were made of heat-treated ash (Fraxinus excelsior). The values of the ultimate failure load and the moment arms were recorded for each run via a universal testing machine. To develop the ANN models, the experimental data were randomly divided into three subsets, which were needed for the training, testing, and validation phases. The RSM models were obtained from the literature. The performances of the models were analyzed in terms of the correlation coefficient, coefficient of determination, root mean square error, mean square error, and mean absolute prediction error. A sensitivity analysis was also performed to observe potential changes in the results due to the uncertainty in the input variables. The ANN model better predicted the bending moment capacity of heat-treated wood dowel joints loaded in compression than the RSM model. In contrast, the RSM model predicted the bending moment capacity of joints loaded in tension more accurately than the ANN model.


Artificial neural network; Response surface methodology; Modeling; Wood joints; Mechanical properties; Heat-treated wood

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